Cardiopulmonary Monitoring: Basic Physiology, Tools, and Bedside Management for the Critically Ill [1st ed. 2021] 3030733866, 9783030733865

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Cardiopulmonary Monitoring Basic Physiology, Tools, and Bedside Management for the Critically Ill Sheldon Magder Atul Malhotra Kathryn A. Hibbert Charles Corey Hardin Editors

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Cardiopulmonary Monitoring

Sheldon Magder Atul Malhotra Kathryn A. Hibbert Charles Corey Hardin Editors

Cardiopulmonary Monitoring Basic Physiology, Tools, and Bedside Management for the Critically Ill

Editors Sheldon Magder Royal Victoria Hospital (McGill University Health Centre), Departments of Critical Care and Physiology McGill University Montreal, QC Canada Kathryn A. Hibbert Division of Pulmonary and Critical Care Medicine Massachusetts General Hospital Boston, MA USA

Atul Malhotra UC San Diego Department of Medicine La Jolla, CA USA Charles Corey Hardin Division of Pulmonary and Critical Care Medicine Massachusetts General Hospital Boston, MA USA

ISBN 978-3-030-73386-5    ISBN 978-3-030-73387-2 (eBook) https://doi.org/10.1007/978-3-030-73387-2 © Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

This book is dedicated to the memory of Dr Brian Kavanagh who sadly died during its preparation. He was our friend, colleague, and another lover of physiology. The book is also dedicated to all the healthcare personnel who worked tirelessly and risked their own health to help others during the COVID 19 scourge.

Acknowledgements

We would like to thank all the authors of this book who spent time preparing their wonderful scholarly contributions. Together we made a whole. SM: I would like to acknowledge Dr Maurice McGregor and Dr Solbert Permutt who shaped my thoughts and lay the basis for this book. I would also like to thank my wife Annette Lefebvre who encouraged me for years to write this book and supported me through the adventure. AM: I would to thank my family, my teachers, my patients and my students for providing inspiration and motivation over the years. CCH: I am grateful to all my teachers including my co-editors and contributors to this volume. KAH: I would like to thank all of the mentors who have so generously given their time to teaching me physiology and challenging me to find a deeper understanding. And to the many patients who have inspired me to become better in all ways.

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Contents

1 Introduction: To the Love of Physiology����������������������������������������   1 Sheldon Magder, Atul Malhotra, C. Corey Hardin, and Kathryn A. Hibbert Part I Physiological Basics: Cardiovascular Basics 2 Volume and Regulation of Cardiac Output����������������������������������   7 Sheldon Magder 3 Function of the Right Heart������������������������������������������������������������  21 Sheldon Magder 4 Function of the Left Heart��������������������������������������������������������������  49 Keith R. Walley 5 Pulmonary Vascular Resistance������������������������������������������������������  61 Wayne Mitzner 6 Fluid Filtration in the Microcirculation����������������������������������������  71 FitzRoy E. Curry 7 Physiology of Heart Rate����������������������������������������������������������������  87 T. Alexander Quinn and Sheldon Magder 8 Physiological Aspects of Arterial Blood Pressure�������������������������� 107 Sheldon Magder 9 Pulsatile Haemodynamics and Arterial Impedance���������������������� 123 David Fitchett and Michael F. O’Rourke 10 Basics of Fluid Physiology �������������������������������������������������������������� 137 Sheldon Magder and Alexandr Magder 11 Cerebral Hemodynamics���������������������������������������������������������������� 153 Christine E. Yeager and Thomas P. Bleck Part II Physiological Basics: Pulmonary Basics 12 Stress, Strain, and the Inflation of the Lung �������������������������������� 167 C. Corey Hardin and James P. Butler 13 Physiology of PEEP and Auto-PEEP���������������������������������������������� 177 John J. Marini ix

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14 Basics of Ventilation/Perfusion Abnormalities in Critically Ill Ventilated Patients������������������������������������������������������ 189 Jeremy E. Orr and Susan R. Hopkins 15 Control of Breathing������������������������������������������������������������������������ 205 Esteban A. Moya, Tatum S. Simonson, Frank L. Powell, Robert L. Owens, and Atul Malhotra 16 Respiratory Muscle Blood Flow and Heart–Lung Interactions���������������������������������������������������������������� 219 Sheldon Magder 17 Surfactant Activity and the Pressure-Volume Curve of the Respiratory System �������������������������������������������������������������� 235 Charles Corey Hardin, Roger G. Spragg, and Atul Malhotra Part III Physiological Basics: Interactions 18 Heart-Lung Interactions ���������������������������������������������������������������� 245 Sheldon Magder and Atul Malhotra Part IV The Tools: Cardiovascular 19 Evaluation of Devices for Measurement of Blood Pressure �������� 273 Agnes S. Meidert and Bernd Saugel 20 Measurement of Cardiac Output��������������������������������������������������� 283 Konstantinos D. Alexopoulos, Sheldon Magder, and Gordan Samoukovic 21 Evaluations of Devices for Measurement of Cardiac Output�������������������������������������������������������������������������������� 309 Pierre Squara 22 Basics of Hemodynamic Measurements���������������������������������������� 319 Sheldon Magder 23 Cerebral Hemodynamic Monitoring Techniques�������������������������� 337 Ivan Da Silva and Thomas P. Bleck 24 Transthoracic Echocardiography for Monitoring Cardiopulmonary Interactions ������������������������������������������������������ 359 Michel Slama 25 Transesophageal Echocardiography for Monitoring Cardiopulmonary Interactions ������������������������������������������������������ 375 Antoine Vieillard-Baron 26 Extra-cardiac Doppler Hemodynamic Assessment Using Point-of-Care Ultrasound ���������������������������������������������������� 385 William Beaubien-Souligny and André Denault

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27 Measurements of Fluid Requirements with Cardiovascular Challenges������������������������������������������������������������� 405 Xavier Monnet and Jean-Louis Teboul 28 CO2-Derived Indices to Guide Resuscitation in Critically Ill Patients������������������������������������������������������������������������ 419 Francesco Gavelli, Jean-Louis Teboul, and Xavier Monnet 29 Microcirculatory Monitoring to Assess Cardiopulmonary Status ���������������������������������������������������������������� 429 Goksel Guven and Can Ince 30 Clinical Assessment and Monitoring of Peripheral Circulation During Shock and Resuscitation������������ 443 Bernardo Lattanzio and Vanina Kanoore Edul 31 Optimizing Oxygen Delivery in Clinical Practice ������������������������ 461 Marat Slessarev and Claudio M. Martin Part V The Tools: Respiratory 32 Measuring Volume, Flow, and Pressure in the Clinical Setting �������������������������������������������������������������������������������� 473 Jason H. T. Bates 33 Measurement of Pleural Pressure�������������������������������������������������� 485 Nadia Corcione, Francesca Dalla Corte, and Tommaso Mauri 34 Ultrasound Assessment of the Lung ���������������������������������������������� 493 Alberto Goffi, Emanuele Pivetta, and Richelle Kruisselbrink 35 Diaphragm Ultrasound: Physiology and Applications ���������������� 521 Ewan C. Goligher 36 Monitoring Respiratory Muscle Function ������������������������������������ 533 Franco Laghi and Martin J. Tobin 37 Basics of Electrical Impedance Tomography and Its Application���������������������������������������������������������������������������������� 585 Christian Putensen, Benjamin Hentze, and Thomas Muders 38 Clinical Monitoring by Volumetric Capnography������������������������ 601 Gerardo Tusman and Stephan H. Bohm 39 MRI in the Assessment of Cardiopulmonary Interaction������������ 619 Ritu R. Gill and Samuel Patz Part VI The Tools: Interaction 40 Respiratory Function of Hemoglobin: From Origin to Human Physiology and Pathophysiology���������������������������������� 635 Connie C. W. Hsia

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41 Acid-Base and Hydrogen Ion���������������������������������������������������������� 653 Sheldon Magder and Raghu R. Chivukula Part VII Applications 42 Use of Maintenance and Resuscitation Fluids������������������������������ 669 Sheldon Magder 43 Identifying and Applying Best PEEP in Ventilated Critically Ill Patients������������������������������������������������������������������������ 685 Takeshi Yoshida, Lu Chen, Remi Coudroy, and Laurent J. Brochard 44 Cardiopulmonary Monitoring in the Prone Patient �������������������� 699 Hernan Aguirre-Bermeo and Jordi Mancebo 45 Cardiopulmonary Interactions in the Management of Acute Obstructive Disease���������������������������������������������������������� 707 Charles Corey Hardin and Julian Solway 46 Evaluation and Management of Ventilator-Patient Dyssynchrony ���������������������������������������������������������������������������������� 715 Enrico Lena, José Aquino-Esperanza, Leonardo Sarlabous, Umberto Lucangelo, and Lluis Blanch 47 Cardiopulmonary Monitoring in the Patient with an Inflamed Lung���������������������������������������������������������������������������� 729 Tommaso Tonetti and V. Marco Ranieri 48 Ventilation During Veno-Venous Extracorporeal Membrane Oxygenation������������������������������������������������������������������ 741 Jacopo Fumagalli, Eleonora Carlesso, and Tommaso Mauri 49 Vasopressor Support for Patients with Cardiopulmonary Failure ���������������������������������������������������������������������������������������������� 751 Daniel De Backer and Pierre Foulon 50 Cardiogenic Shock Part 1: Epidemiology, Classification, Clinical Presentation, Physiological Process, and Nonmechanical Treatments������������������������������������������������������������ 759 Sheldon Magder 51 Cardiogenic Shock Part 2: Mechanical Devices for Cardiogenic Shock �������������������������������������������������������������������������� 793 Sheldon Magder and Gordan Samoukovic 52 Pathophysiology of Sepsis and Heart-Lung Interactions: Part 1, Presentation and Mechanisms�������������������� 821 Sheldon Magder 53 Pathophysiology of Sepsis and Heart-Lung Interactions: Part 2, Treatment������������������������������������������������������ 849 Sheldon Magder and Margaret McLellan

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54 Cardiopulmonary Monitoring of Patients with Pulmonary Hypertension and Right Ventricular Failure�������������������������������� 871 Ryan A. Davey, Ahmed Fathe A. Alohali, Sang Jia, and Sanjay Mehta 55 Monitoring and Management of Acute Pulmonary Embolism���������������������������������������������������������������������� 905 Jenna McNeill and Richard N. Channick 56 Clinical Neurologic Issues in Cerebrovascular Monitoring���������������������������������������������������������������������������������������� 917 Thomas P. Bleck 57 Delirium in the Critically Ill Patient���������������������������������������������� 923 Alex K. Pearce, Jamie Labuzetta, Atul Malhotra, and Biren B. Kamdar 58 Obesity in Critically Ill Patients ���������������������������������������������������� 935 Kathryn A. Hibbert and Atul Malhotra Part VIII Epilogue 59 The Future���������������������������������������������������������������������������������������� 951 Sheldon Magder, Charles C. Hardin, Kathryn A. Hibbert, and Atul Malhotra Index���������������������������������������������������������������������������������������������������������� 959

Contributors

C.  Corey Hardin, MD, PhD Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA Hernan  Aguirre-Bermeo, MD, PhD  Intensive Care Unit, Hospital Santa Inés, Cuenca, Ecuador Konstantinos  D.  Alexopoulos, MD Department of Critical Care, Royal Victoria Hospital – McGill University and McGill University Health Centre, Montreal, QC, Canada Ahmed Fathe A. Alohali, MBBS  Southwest Ontario PH Clinic, Division of Respirology, London Health Sciences Centre, London, ON, Canada Department of Medicine, Schulich School of Medicine, Western University, London, ON, Canada Internal Medicine, Adult Pulmonary Medicine and Pulmonary Hypertension, Adult Critical Care and Cardiovascular Critical Care, King Fahad Medical City Hospital, Critical Care Services Administration, Riyadh, Kingdom of Saudi Arabia Daniel  De Backer, MD, PhD Department of Intensive Care, CHIREC Hospitals, Université Libre de Bruxelles, Brussels, Belgium Jason H. T. Bates, Ph.D., D.sc.  Department of Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA William Beaubien-Souligny, MD  Division of Nephrology, Department of Medicine, Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada Department of Anesthesia, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada Lluis  Blanch, MD, PhD Critical Care Center, Hospital Universitari Parc Taulí, Institut d’Investigació i Innovació Parc Taulí I3PT, Sabadell, Spain Biomedical Research Networking Center in Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, Madrid, Spain

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Thomas P. Bleck, MD, MCCD  Northwestern University Feinberg School of Medicine, Davee Department of Neurology, Chicago, IL, USA Rush Medical College, Chicago, IL, USA The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Departments of Neurological Sciences, Neurosurgery, Internal Medicine, and Anesthesiology, Rush Medical College, Chicago, IL, USA Division of Stroke and Neurocritical Care, Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Stephan Hubertus Bohm, MD  Department of Anesthesiology and Intensive Care Medicine, Rostock University Medical Center, Rostock, Germany Laurent  J.  Brochard, MD Interdepartmental Division of Critical Care Medicine, University of Toronto, St. Michael’s Hospital, Toronto, ON, Canada Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada James  P.  Butler, PhD Harvard TH Chan School of Public Health and Harvard Medical School, Boston, MA, USA Eleonora Carlesso, MSc  Dipartimento di Fisiopatologia Medico-Chirurgica e dei Trapianti, Università degli Studi di Milano, Milan, Italy Richard N. Channick, MD  UCLA Medical Center, UCLA David Geffen School of Medicine, Pulmonary and Critical Care Division, Los Angeles, CA, USA Lu  Chen, MD Interdepartmental Division of Critical Care Medicine, University of Toronto, St. Michael’s Hospital, Toronto, ON, Canada Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada Raghu R. Chivukula, M.D., Ph.D.  Harvard Medical School, Massachusetts General Hospital, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Boston, MA, USA Nadia  Corcione  Department of Anesthesia, Critical Care and Emergency, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy Francesca  Dalla  Corte Department of Anesthesia, Critical Care and Emergency, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy Remi Coudroy, PhD  Interdepartmental Division of Critical Care Medicine, University of Toronto, St. Michael’s Hospital, Toronto, ON, Canada Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada FitzRoy  E.  Curry, BE, PhD Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, USA

Contributors

Contributors

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Ryan  A.  Davey, MD, FRCPC, FACC St. Josephs Hospital PH Clinic, London, ON, Canada Heart Failure Service, Division of Cardiology, London Health Sciences Centre and St. Josephs Healthcare Centre, London, ON, Canada André  Denault, MD, PhD Department of Anesthesia, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada Division of Intensive Care, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada Division of Intensive Care, Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada Vanina  Kanoore  Edul, MD, PhD Facultad de Ciencias Médicas, Universidad Nacional de La Plata, Cátedra de Farmacología Aplicada, La Plata, Argentina Intensive Care Department, Hospital Juan A.  Fernández, Buenos Aires, Argentina José  Aquino-Esperanza, MD Critical Care Center, Hospital Universitari Parc Taulí, Institut d’Investigació i Innovació Parc Taulí I3PT, Sabadell, Spain Universitat de Barcelona, Facultat de Medicina, Barcelona, Spain Biomedical Research Networking Center in Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, Madrid, Spain David  Fitchett, MD, FRCP© Department of Cardiology, St Michael’s Hospital, University of Toronto, Toronto, ON, Canada Pierre Foulon  Department of Intensive Care, CHIREC Hospitals, Université Libre de Bruxelles, Brussels, Belgium Jacopo Fumagalli, MD  Dipartimento di Fisiopatologia Medico-Chirurgica e dei Trapianti, Università degli Studi di Milano, Milan, Italy Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Department of Anesthesia, Critical Care and Emergency, Milano, Italy Francesco Gavelli, MD  Université Paris-Saclay, AP-HP, Service de médecine, intensive-réanimation, Hôpital de Bicêtre, DMU CORREVE, Inserm UMR S_999, FHU SEPSIS, Groupe de recherche clinique CARMAS, Le Kremlin-Bicêtre, France Emergency Medicine Unit, Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Novara, Italy Ritu R. Gill, MD, MPH  Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA Alberto  Goffi, MD  Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada Department of Medicine and Department of Critical Care Medicine, St. Michael’s Hospital, Toronto, ON, Canada Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada

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Ewan C. Goligher, MD, PhD  Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada Toronto General Hospital Research Institute, Toronto, ON, Canada Goksel Guven, MD  Department of Intensive Care, Erasmus MC University Medical Centre, Rotterdam, The Netherlands Charles Corey Hardin, MD, PhD  Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA Benjamin Hentze, Dipl.-Ing  Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany Kathryn  A.  Hibbert, MD Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA Susan  R.  Hopkins, MD, PhD Department of Medicine and Radiology, University of California, San Diego, La Jolla, CA, USA Connie C. W. Hsia, MD  Department of Internal Medicine, Pulmonary and Critical Care Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA Can  Ince, PhD Department of Intensive Care, Erasmus MC University Medical Centre, Rotterdam, South Holland, The Netherlands Sang Jia, MD  Department of Medicine, University of Manitoba, Winnipeg, MB, Canada Biren B. Kamdar, MD, MBA, MHS  Division of Pulmonary, Critical Care, Sleep Medicine and Physiology, University of California San Diego, La Jolla, CA, USA Richelle  Kruisselbrink, BMus MD FRCPC Department of Anesthesia, Grand River Hospital and St. Mary’s General Hospital, Kitchener, ON, Canada Department of Anesthesia, McMaster University, Hamilton, ON, Canada Jamie  Labuzetta, MD, MSc, MPhil Division of Neurocritical Care, Department of Neurosciences, University of California San Diego, La Jolla, CA, USA Franco  Laghi, MD Division of Pulmonary and Critical Care Medicine, Hines Veterans Administration Hospital, Hines, IL, USA Loyola University of Chicago Stritch School of Medicine, Maywood, IL, USA Bernardo  Lattanzio, MD Facultad de Ciencias Médicas, Universidad Nacional de La Plata, Cátedra de Farmacología Aplicada, La Plata, Argentina Critical Care Unit, Clínica Bazterrica y Santa Isabel, Buenos Aires, Argentina Enrico  Lena, MD  Department of Perioperative Medicine, Intensive Care and Emergency, Cattinara Hospital, Trieste University, Trieste, Italy

Contributors

Contributors

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Umberto Lucangelo, MD  Department of Perioperative Medicine, Intensive Care and Emergency, Cattinara Hospital, Trieste University, Trieste, Italy Alexandr Magder, MD, B.Sc  Department of Pediatrics, Bernard and Millie Duker Childrens Hospital, Albany Medical Center, Albany, NY, USA Sheldon  Magder, MD  Royal Victoria Hospital (McGill University Health Centre), Departments of Critical Care and Physiology McGill University, Montreal, QC, Canada Atul Malhotra, MD  UC San Diego, Department of Medicine, La Jolla, CA, USA Jordi Mancebo, MD, PhD  Intensive Care Unit, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain John  J.  Marini, MD, BES University of Minnesota, Minneapolis, MN, USA Claudio M. Martin, MSc, MD, FRCPC  Division of Critical Care Medicine, Department of Medicine, University of Western Ontario, London, ON, Canada Tommaso  Mauri, MD Department of Anesthesia, Critical Care and Emergency, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, University of Milan, Milano, Italy Dipartimento di Fisiopatologia Medico-Chirurgica edei Trapianti, Università degli Studi di Milano, Milano, Italy Dipartimento di Anestesia, Rianimazione ed Emergenza, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milano, Italy Agnes S. Meidert, MD  Department of Anaesthesiology, University Hospital of Munich (LMU), Munich, Germany Margaret McLellan, MD, FRCPC  Department of Anesthesia and Critical Care, McGill University and McGill University Health Centre, Montreal, QC, Canada Jenna  McNeill, MD Massachusetts General Hospital, Department of Pulmonary and Critical Care, Boston, MA, USA Sanjay  Mehta, MD, FRCPC Southwest Ontario PH Clinic, Division of Respirology, London Health Sciences Centre, London, ON, Canada Department of Medicine, Schulich School of Medicine, Western University, London, ON, Canada Pulmonary Hypertension Association (PHA) of Canada, Vancouver, BC, Canada Western University, London Health Sciences Centre, Department of Medicine/Respirology, Victoria Hospital, London, ON, Canada Wayne Mitzner, PhD  Department of Environmental Health & Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

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Xavier  Monnet, MD, PhD Université Paris-Saclay, AP-HP, Service de médecine intensive-réanimation, Hôpital de Bicêtre, DMU CORREVE, Inserm UMR S_999, FHU SEPSIS, Groupe de recherche clinique CARMAS, Le Kremlin-Bicêtre, France Esteban  A.  Moya, PhD Section of Physiology, Division of Pulmonary, Critical Care & Sleep Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, USA Thomas Muders, MD, DESA  Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany Michael  F.  O’Rourke, MD DSc St. Vincent's Hospital, Department of Cardiology, St Vincent’s Clinic, Sydney, NSW, Australia Jeremy  E.  Orr, MD Department of Medicine, Division of Pulmonary Critical Care and Sleep Medicine, University of California, La Jolla, CA, USA Robert  L.  Owens, MD University of California San Diego, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, La Jolla, CA, USA Samuel  Patz, PhD Department of Radiology, Harvard Medical School, Brigham & Women’s Hospital, Boston, MA, USA Alex K. Pearce, MD  Division of Pulmonary, Critical Care, Sleep Medicine and Physiology, University of California San Diego, La Jolla, CA, USA Emanuele  Pivetta, M.D., M.Sc., Ph.D Division of Emergency Medicine and High Dependency Unit, Department of Medical Sciences, University of Turin, Turin, Italy Cancer Epidemiology Unit and CRPT U, Department of Medical Sciences, University of Turin, Turin, Italy Frank  L.  Powell, PhD Section of Physiology, Division of Pulmonary, Critical Care & Sleep Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, USA Christian Putensen, MD  Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany T. Alexander Quinn, PhD  Dalhousie University, Department of Physiology & Biophysics, Halifax, NS, Canada V.  Marco  Ranieri, MD Alma Mater Studiorum – Università di Bologna, Dipartimento di Scienze Mediche e Chirurgiche, Anesthesia and Intensive Care Medicine, IRCCS Policlinico di Sant’Orsola, Bologna, Italy Gordan Samoukovic, MD, MSc, FRCPC, FRCSC, FASE  Department of Critical Care, Royal Victoria Hospital  – McGill University and McGill University Health Centre, Montreal, QC, Canada

Contributors

Contributors

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Leonardo Sarlabous, PhD  Critical Care Center, Hospital Universitari Parc Taulí, Institut d’Investigació i Innovació Parc Taulí I3PT, Sabadell, Spain Biomedical Research Networking Ceter in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salúd Carlos III, Madrid, Spain Bernd Saugel, MD  Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center HamburgEppendorf, Hamburg, Germany Ivan Da Silva, MD, PhD  Rush Medical College, Chicago, IL, USA Rush University Medical Center, Department of Neurological Sciences, Chicago, IL, USA Tatum  S.  Simonson, M.D., Ph.D. Section of Physiology, Division of Pulmonary, Critical Care & Sleep Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, USA Michel  Slama, MD, PhD  Medical Critical Care Unit, Amiens University Hospital, Amiens, France CHU Sud Amiens Hospital, Department of Medical Intensive Care, Place du Professeur Christian Cabrol, Amiens, France Marat  Slessarev, MD, MSc FRCPC  Division of Critical Care Medicine, Department of Medicine, University of Western Ontario, London, ON, Canada Julian Solway, MD  Department of Medicine, Section of Pulmonary/Critical Care, University of Chicago, Chicago, IL, USA Roger  G.  Spragg, MD Divison of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego, La Jolla, CA, USA Pierre Squara, MD  ICU and Cardiologic Department, Clinique Ambroise Paré, Neuilly-sur-Seine, Hauts de Seine, France Jean-Louis Teboul, MD, PhD  Université Paris-Saclay, AP-HP, Service de médecine intensive-réanimation, Hôpital de Bicêtre, DMU CORREVE, Inserm UMR S_999, FHU SEPSIS, Groupe de recherche clinique CARMAS, Le Kremlin-Bicêtre, France Martin J. Tobin, MD  Division of Pulmonary and Critical Care Medicine, Hines Veterans Administration Hospital, Hines, IL, USA Tommaso  Tonetti, MD Alma Mater Studiorum – Università di Bologna, Dipartimento di Scienze Mediche e Chirurgiche, Anesthesia and Intensive Care Medicine, IRCCS Policlinico di Sant’Orsola, Bologna, Italy Gerardo Tusman, MD  Department of Anesthesiology, Hospital Privado de Comunidad, Mar del Plata, Buenos Aires, Argentina Antoine Vieillard-Baron, MD, PhD  Publique-Hôpitaux de Paris, University Hospital Ambroise Paré, Intensive Care Unit, Boulogne-Billancourt, France

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Medical and Surgical Intensive Care Unit, University Hospital Ambroise Paré, APHP, Boulogne-Billancourt, France Keith R. Walley, MD  Division of Critical Care Medicine, Centre for Heart Lung Innovation, University of British Columbia, St. Paul’s Hospital, Vancouver, BC, Canada Christine  E.  Yeager, MD Department of Neurology, University of Minnesota, Minneapolis, MN, USA Takeshi Yoshida, MD, PhD  Osaka University Graduate School of Medicine, Department of Anesthesiology and Intensive Care Medicine, Yamadaoka, Suita, Osaka, Japan Interdepartmental Division of Critical Care Medicine, University of Toronto, St. Michael’s Hospital, Toronto, ON, Canada Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada

Contributors

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Introduction: To the Love of Physiology Sheldon Magder, Atul Malhotra, C. Corey Hardin, and Kathryn A. Hibbert

We begin this introduction to Cardiopulmonary Monitoring: Basic Physiology, Tools, and Bedside Management with a statement of bias. We love physiology. It is why we are intensivists. Physiology is at the core of our approach to management of our critically ill patients, and managing critically ill patients provides continuous feedback for our understanding of physiological processes. It is for this reason that physiological considerations are the central part of this book. We hope that the reader will find the chapters enlightening and will be able to access important information from a single source. In the process of putting together this book, we sought out authors who share our belief in the importance of physiology in the management of the critically ill. These authors have made important contributions to our understanding of the underlying pro-

S. Magder (*) Royal Victoria Hospital (McGill University Health Centre), Departments of Critical Care and Physiology McGill University, Montreal, QC, Canada e-mail: [email protected] A. Malhotra UC San Diego, Department of Medicine, La Jolla, CA, USA e-mail: [email protected] C. C. Hardin · K. A. Hibbert Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA e-mail: [email protected]; [email protected]

cesses and how to better manage them. Our objective was to immortalize these fundamental concepts in a single source to ensure their availability to the critical care community. The first section of the book deals with the underlying fundamental physiological concepts that we believe are necessary for rational management of the critically ill. The chapters are grouped into sections addressing the cardiovascular and respiratory systems with a smaller “overlap” section. A theme throughout the book, though, is that the cardiovascular and respiratory systems are always interacting, and in the final analysis of pathological processes, both must be considered. Some of the concepts in this section are repeated in parts two and three because some review is inevitably necessary to understand the applications. The second section deals with the tools that are available to monitor critically ill patients. It again is divided into separate sections that deal with the cardiovascular and respiratory systems. The third section provides the clinical integration of the physiologic concepts from the first section with the data available from the tools described in the second section. The objective of the third section is to provide a rational physiological approach to the management of the critically ill. An underlying principle in this section is that clinical responses only can be in the realm of the physiologically possible. A few words about what we have not included in this book. We have chosen not to include more cellular and molecular science in these chapters.

© Springer Nature Switzerland AG 2021 S. Magder et al. (eds.), Cardiopulmonary Monitoring, https://doi.org/10.1007/978-3-030-73387-2_1

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We are big believers in the advances molecular sciences have brought and fully appreciate the myriad ways in which knowledge of a particular molecule or receptor is illuminating. However, understanding integrated function is crucial for overall understanding of patient pathophysiologies. In the end, biochemical processes have their effects by altering organ functions, and it is necessary to know how those organ functions affect the overall homeostasis of the body. We note that the majority of improvements in patient care in the ICU have not come from molecular studies divorced from the larger context, but rather from the better understanding of the underlying physiology and unified pathophysiological processes. As such, we argue that physiology is not dead but is alive and well, and we emphasize that integrated function is what should guide bedside decisions and management. We have also not spent much time in this book discussing evidence from clinical trials. Clinical trials are clearly essential for the rigorous practice of medicine, but we also recognize that the majority of clinical trials in the ICU have failed to show positive outcomes. We do not regard these observations as a failure, but rather as a reminder of the complexity of ICU patients. Furthermore, a failed trial still provides information – in this case what is not useful, or at least not useful the way the study was done. Understanding why a trial is negative thus can lead us to generate new hypothesis on how best to treat our patients. Moreover, negative trials have been instrumental in identifying important sources of heterogeneity (e.g., high PEEP in ARDS). Beyond study design failure, a strong understanding of the underlying physiology may give new insights into why the trial results were negative and therefore how to better approach the problem in the future. As physicians working in the intensive care unit, we have the privilege of being able to observing the basic physiology constantly unfold in front of us. Bedside tools we have today allow us to make measurements that previously only could have been possible in an animal laboratory.

S. Magder et al.

A basic part of the approach to a clinical problem is the generation of a hypothesis to explain the observed pathology. As clinicians, we act on that hypothesis by performing tests that give results that are either are consistent with the hypothesis or inconsistent, in which case we consider rejecting the hypothesis or modifying it. We also apply treatments that are thought to be effective for the hypothesized disordered process. The information from our monitoring tools give us constant feedback of the response to the treatments that we have tried. This allows us to constantly test whether or not our hypothesis is correct. Therapy then can be adjusted based on the response. This approach can be called “responsive therapy” rather than goal-directed therapy. As a final step, good clinicians should always consider whether or not the patient’s outcome is consistent with the initial beliefs, and by this process clinicians should constantly modify their understanding of human biology so as to manage the next patient better. When choosing the authors and topics of this book, our goal was to summarize classic concepts that are not always easy to find in traditional textbooks or in the literature. In addition, we sought to bring new concepts and techniques to the reader which were not available when some of the classics were completed. We have learned many of these concepts over the years by talking to colleagues, drawing on the backs of napkins and piecing together studies from the literature. However, the strongest source for our knowledge has come from the day to day management of patients. This approach has provided constant feedback of proposed theories. When the bedside experience contradicts the common belief, it has led us to review the underlying source of these beliefs and to discuss our experiences with colleagues. Knowledge is not static and needs to be constantly modified by experience. An important principle in biological science is that unlike mathematics, physics, and chemistry, nothing is always precisely the same. Exceptions are what allows biological species to evolve. For this reason, we must remember that our patients are rarely the “mean” value but

1  Introduction: To the Love of Physiology

instead they are some standard deviation from that value. The exceptions also allow us to better understand the rules that determine normal function. The key to being a good clinician, and to being a good clinical scientist, is to constantly observe and reflect on what is seen at the bedside.

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We view this book as a consolidation of this knowledge and hope that this book provides a legacy for concepts that we hope will never be lost. If even a portion of the readers of the book help pass the knowledge along to the next generation, we would view the book as a major success. Enjoy the reading.

Part I Physiological Basics: Cardiovascular Basics

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Volume and Regulation of Cardiac Output Sheldon Magder

A fundamental biological need for all animals is that there be sufficient delivery of oxygen and nutrients to tissues and removal of metabolic wastes. In single-cell organisms, this occurs by simple diffusion of substances across the cell wall, as well as membrane channels and active transport mechanisms. In initial multicellular organisms (≈ approximately 800–700 million years ago), this occurred by circulation of sea water, nutrient absorption, and reproduction, all being combined in pathways through the organisms (Moorman and Christoffels 2003; Bishopric 2005; Pascual-Anaya et al. 2013). When a symmetric body plan evolved, a passage formed through the center of the organism. This channel had pulsatility but no directionality to flow. Insects developed an early cardio-aortic valve and pericardial cells which allowed directionality of fluid flow; however, they still did not have a separate gas exchange system (≈ 600 million years ago) (Bishopric 2005). It is at the level of Chordata, ≈ 550 million years ago, that the gut and gas exchange units separated and there was development of early myocardial cells and the beginning of the cardiovascular system (Xavier-­ Neto et  al. 2007). With the evolution of vertebrates around 550 million years ago, the full S. Magder (*) Royal Victoria Hospital (McGill University Health Centre), Departments of Critical Care and Physiology McGill University, Montreal, QC, Canada e-mail: [email protected]

circulatory system began to develop (Simoes-­ Costa et al. 2005). This started with a single ventricle and no separate pulmonary circulation. The final development of the mammalian and avian circulations with a four chamber heart and fully separated pulmonary and systemic circulations occurred between 220 and 170 million years ago and is thus a very late development in the history of evolution (Bishopric 2005). Flow through a closed circuit is governed by three variables: pressure, volume, and time. These are related to each other through two primary relationships: the pressure-volume relationship, which describes the elastic properties of the compartments of the system, and the pressure-­ flow relationship, which describes the resistance to flow between compartments. These two primary relationships also describe movements of air in the respiratory system and thus this analysis of pressure-volume and pressure-flow for the circulation has direct parallels to volume and flow in the respiratory system. A principle underlying the discussion in this chapter is that the volume that stretches the elastic structures of the vasculature is the key independent variable for the flow of blood (Magder 2016). This volume is called stressed volume and over the short run is constant, although it can be increased by intake or loss (Magder and De Varennes 1998; Rothe 1983a). Stretch of the elastic walls of vascular structures by the stressed volume creates pressures in the system; importantly, the pressure

© Springer Nature Switzerland AG 2021 S. Magder et al. (eds.), Cardiopulmonary Monitoring, https://doi.org/10.1007/978-3-030-73387-2_2

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does not determine the volume but rather the volume determines the pressure. Pressure is present with or without flow. However, when the pressure created by the volume in one region is greater than the pressure in a downstream compartment, flow occurs. Some of these concepts have been reviewed previously (Magder 2016; Magder and Scharf 2001).

Pressure-Volume Relationship The force created by stretching walls of vascular structures is based on Hooke’s law, which says that if a substance is homogeneous, stress increases linearly with a change in the length (Fig.  2.1). Below a certain length, elastic structures do not have a tension. Tension only arises when the substance is straightened to a length above which the substance is stretched. This length is called Lo (Fig. 2.1a). The slope of a plot of a change in stress against change in length is called elastance. This can be thought of as a “recoil” force; when the force stretching the structure is released, the substance snaps back to the resting length. In curved vascular structures, stress is described by the term pressure, which is the force over the surface area (Fig.  2.1b). The a

b

Fig. 2.1  Pressure volume relationships. The first panel (a) shows the change in tension for change in length as per Hooke’s law. If the substance is homogeneous, the slope is linear. It starts from an unstretched length (Lo). The slope of the line is elastance (E, elastic modulus), which is the constant for the relationship. In the middle panel (b), the length

inverse of elastance is compliance. Elastance and compliance will be used interchangeably in this chapter, but in general, I will use compliance when considering the uptake of volume and elastance when discussing the expulsion of volume. Elastance and compliance are static measurements, which means that they must be measured under steady state condition. If not, the measured pressure includes resistive and inertial components of the force. In the physiological range, the elastance of veins is linear, but at low pressures, it is curvilinear because some vessels collapse, which decreases the overall surface area (Fig.  2.1b) and because of inhomogeneities of components of the vessel walls. At high pressures, the elastance of arterial vessels increases, i.e., the slope becomes steeper (see Chap. 8 on blood pressure), whereas in veins the slope is curvilinear at low volumes because new channels open, but venous compliance is linear in the physiological range. The compliance (elastance) of the walls of vessels is a function of the properties of the wall, specifically the collagen and elastin, and it does not change acutely. A change requires changes in the matrix of the wall, which takes time. The total compliance of a system that has compartments in a row that have different compliances is simply the sum of the compliances of each compartment c

is replaced by volume (L3) and the tension by pressure (force per cross-sectional area). The slope is still elastance. The right panel (c) shows a decrease in capacitance. The vessels’ circumference is reduced by contraction of vascular smooth muscle, which shifts the elastance curve to the left. The same volume (circles) now has a greater pressure

2  Volume and Regulation of Cardiac Output

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Capacitance

Fig. 2.2  General schema of the circulation. The six regions are roughly drawn in proportion to their size. Regions above the dotted line are in the thorax. C refers to the compliance and R to the resistance. RV = right ventricle, LV  =  left ventricle, vs  =  systemic venous compartment, ap  =  pulmonary arterial compartment, vp = pulmonary venous compartment, as = systemic arterial compartment. When flow is zero, 71% of volume is in the systemic venous compartment (for a total stressed volume, this is 1022 ml) and 10% in the pulmonary venous compartment (137  ml). The total compliance (Ctotal) is the sum of the compliances of the six regions

(Fig. 2.2). Thus, the total compliance of the circulatory system is the sum of the compliance of the arterial, arteriolar, capillary, small veins, large veins, and the pulmonary components. However, the compliance of venules and veins is 40 times that of the arterial compartment. Although the capillaries have a very large cross section area, their compliance, too, is very low, and thus the venous compartments dominate the magnitude of the overall vascular compliance (Permutt and Caldini 1978; Guyton et  al. 1956). As will be seen later, this allows for a “lumped parameter” model for the analysis of cardiac output, which leaves out the compliance of the arterial system. This simplification produces about a 10% error in the quantitative analysis but makes the mathematical analysis much simpler.

Total blood volume in a 70- to 75-kg male is in the range of 5.5 L, but not all of the blood volume stretches vessel walls and creates the pressure in vessels; a portion of the volume just makes vessels round. This portion of blood volume is called unstressed volume and it is the equivalent of the x-intercept (Lo) in Hooke’s assessment of tension versus length (Rothe 1983a, b) (Fig.  2.1). As already noted, the proportion of volume that stretches vessel walls and creates the pressure in vessels is called stressed volume (Rothe 1983a). Under conditions of minimum vascular tone in humans and in animal studies, the stressed portion of blood volume is approximately 30% of the total blood volume (Magder and De Varennes 1998). This means that only about 1.3–1.4 L of blood volume actually is involved in making the blood go around. The advantage of having a reserve of unstressed volume is that this volume can be recruited into stressed volume by contraction of the vascular smooth muscle in the walls of veins and venules (Drees and Rothe 1974). This occurs primarily in the splanchnic circulation. Importantly, a change in capacitance changes the position of the vascular volume-pressure curve, but it does not affect the slope of the relationship, which is 1/compliance (Fig. 2.1c). A strong sympathetic discharge through baroreceptor mechanisms can recruit from 10 to 18  ml/kg of unstressed into stressed volume and this occurs in seconds (Deschamps and Magder 1992). To achieve a similar increase in stressed volume by giving an intravenous crystalloid would require an infusion of almost 2 L because the crystalloid distributes between the vascular and interstitial spaces, whereas the change in capacitance is a pure vascular phenomena. Importantly, the opposite, removal of sympathetic tone that was maintaining stressed volume in someone with a reduced total blood volume, very rapidly decreases stressed volume and produces a marked fall in venous return and cardiac output.

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Uniqueness of Volume-Pressure Relationship of the Cardiac Chambers During diastole, cardiac chambers have a curvilinear pressure-volume relationship which is related to the structural components of the walls of the vascular chambers. However, unlike all other components of the circulation, the elastance of cardiac chambers rhythmically markedly decrease, and thus cardiac chambers have a “dynamic compliance” or what Sagawa called a time-varying elastance. The transient decrease in the elastance of the cardiac chambers markedly increases the pressure in their contained volume. The rise in pressure results in the ejection of the volume into the next region which had a lower pressure. This is the systemic arteries for the left heart and pulmonary arteries for the right heart. Blood flows in one direction because of the cardiac valves. The volume ejected from the left heart transiently increases aortic pressure, which then passes the volume to the arteries, to the capillaries, to the venules and veins, and finally to the vena cavae and back to the right heart. Ultimately, though, the cardiac chambers only can pump out what they get back on each beat (Sylvester et al. 1983). The same process occurs from the right ventricle back to the left atrium. The cyclic changes in ventricular elastic pressures are discussed further in the chapters on the right (Chap. 3) and left ventricles (Chap. 4).

Pressure-Flow Relationship The pressure-flow relationship describes the frictional energy loss of the flow of fluid through tubes as described by Poiseuille’s law: P l 8  (2.1) R r 4 Where Q is the flow, R is the resistance, l is the length, η is the viscosity, r is the radius, and Δ indicates the difference of pressure between upstream and downstream regions. Important assumptions in Poiseuille’s law are that the fluid is “Newtonian” which requires that a steady state Q

flow in the tube be established, and that the flow is “laminar”, in that it has a parabolic profile due to the friction between layers of the fluid and the vessel walls. These assumptions are not true for flows through valves and at major branch points where turbulence can develop. They also are not true in regions in which vessel diameters are very small and the velocity is high. However, for this discussion about flow between major parts of the circulation, the relationship is adequate for the description of total cardiac output.

Importance of Compliance for Blood Flow Although compliance is a static property, it is essential for flow, which is a dynamic property. This is illustrated in Fig. 2.3a, which shows a circuit with rigid tubes (extremely low compliance), a bellows that can be pumped to produce flow, and valves that control the direction of the flow. The resistance through the tubes has a value of 1  L × min−1 × mmHg. The maximum possible flow in this simple system is zero. This is because as soon as the bellows is compressed, the pressure instantly rises everywhere in the system and there is no pressure difference to allow a pulse of volume to travel through the system. For pulsatile flow to occur, there needs to be an area which transiently can take up the volume with a rise in pressure, here simulated by a change in height of the fluid in the open area, for flow to occur (Fig.  2.3b). Height is a measure of pressure because of the force of gravity on the mass of the fluid. If the compliant region has a very high compliance, here simulated by a large surface area relative to the height (Fig. 2.3c), the pulsations are very small.

 low from a Single Compliant F Region Even when there is no blood flow in the circulation, the volume stretching the elastic walls of circulatory structures creates a stored elastic energy that can be released when the system is

2  Volume and Regulation of Cardiac Output

a

b

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c

Fig. 2.3  Significance of compliance in the circulation. In each of the figures, a bellows can create a pressure to move the fluid. Valves control the direction. The tubes are rigid (very low compliance). In the top left (a), no flow (Q = 0) can occur because when the bellows is compressed, pressure is instantaneously transmitted through the system and there is no pressure difference for flow. On the upper right

(b), there is an opening that can transiently take up volume and then let it flow out again; it allows a change in volume for a change in pressure which is compliance. The flow has pulsations. At the bottom (c), the area with opening has a large surface area compared to the volume in the bellows. Pulsations are thus very small. Similarly, there is little change in MSFP during the cardiac cycle

open to the surrounding pressure, even without any pumping by the heart. This is illustrated in Fig.  2.4 which shows a balloon-like structure filled with a volume that stretches the wall above the unstressed volume and a tube draining the balloon. In “A,” a clamp prevents the balloon from emptying; in “B,” when the clamp is released, the balloon expels the volume until it reaches the pressure surrounding the outside of the balloon which in this case is atmospheric pressure. The determinants of flow are given by:

drainage from a bathtub (Magder and De Varennes 1998). Drainage from a bathtub is determined by the height of the water above the hole at the bottom, the resistance draining the tub, and the downstream pressure of the drain. The inflow to the tub only can increase the outflow by increasing the height of the water in the tub. Thus, only the flow from the tap, which is the volume per minute, and not the pressure coming out of the tap, determines the emptying of the tub. Since the surface is so large compared to the volume coming in, shutting off the tap in the short run has little effect on drainage from the tub. At the extreme, when the tub is fully filled, increasing the inflow will not increase outflow from the drain, although it will certainly fill the bathroom floor! This is in a sense what occurs when excess fluid is given to patients; there is a marked increase in vascular leak but no increase in the venous drainage back to the heart. Blood volume in veins and venules provides the equivalent of the bathtub because they have a large volume with a low pressure. This means that the arterial pressure upstream from the veins and venules does determine the outflow from the venous compartment. Only the volume coming from the arteries per minute determines venous outflow. Furthermore, because the bulk of blood volume exists in veins and venules, there is very little other volume that can be recruited from other vascular regions to increase the pressure in the veins, the body’s equivalent of a bathtub. Even the vascular components of the pulmonary

 (2.2) RC Where Q is the flow, γ is the stressed volume, and R is the resistance to drainage. R × C gives the time constant of drainage which is the time it takes to get to a 63% of the new steady state, which in this case is the time it takes to expel 63% of the volume. This simple equation indicates the importance of the total stressed volume as a major determinant of flow around the system. Q

Bathtub Concept As already indicated, the bulk of the volume in the circulation is in small veins and venules (Fig. 2.2). The pressure in this region normally is in the range of 8–10  mmHg indicating that the compliance is very large. The implication of this can be understood by considering the analogy of

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a

b

Fig. 2.4  Generation of flow (Q) in a simple system which is similar to the venous compartment; it has a compliance (Cv), downstream resistance (Rv) and is filled with a stressed volume. When the clamped is released, the elastic recoil of the walls of the compliant region pushes the volume out through the downstream resistance. The volume that remains is the unstressed volume. The equation for this simple system is given in the figure. See text for further discussion

compartments cannot contribute much volume because the total compliance of pulmonary vessels only is about one-seventh of that of the systemic venous compliance (Guyton et al. 1956). When there is no flow in the system, the pressure is the same everywhere in the vasculature. This pressure is called mean circulatory filling pressure (MCFP) and it was first appreciated as a major determinant of blood flow by Weber in the nineteenth century and later considered by Ernest Starling (Patterson et  al. 1914) and almost 50  years later by Arthur Guyton (Guyton et  al. 1954). When there is blood flow around the circulation, volume redistributes based on the compliance and resistance draining each region (Fig.  2.2). However, the pressure in veins and venules changes only by a very small amount because it accounts for such a large proportion of the total vascular compliance. The pressure in this region under flow conditions is called mean systemic filling pressure (MSFP), and it is a major determinant of the return of flow back to the heart. The other factor is the total resistance of the vessels draining the veins and venules (Fig. 2.2) (Guyton et al. 1957). This is called the resistance to venous return. Importantly, vascular compliances (not to be confused with vascular capacitance) do not actively change acutely, and

thus do not act in the regulation of blood flow. However, as will be seen later, distribution of blood flow to regions with different compliances can affect the venous return. In the steady state, cardiac output must equal venous return. Furthermore, stroke volume out from the heart must be matched by stroke return when heart rate is constant. This means that under steady state conditions, only the equivalent of one stroke volume moves around the circulation per cardiac pulsation. The total flow obviously is then determined by the number of pulsations per minute. Since output from the heart cannot have much effect on the stressed volume in the veins and venules, it becomes apparent that the only way the actions of the heart can increase steady state blood flow is by lowering the outflow pressure for venous drainage by lowering right atrial pressure relative to MSFP. Thus, the primary role of the heart in the circulation is a “permissive function.” By lowering right atrial pressure, the heart allows more blood to come back to be pumped out again. The heart also has a second very important role which is a “restorative” function by which I mean that it must put the blood it gets back again. The drainage function of venous return back to the heart (Eq.  2.2) interacts with cardiac function as described by Starlings law, and as discussed in detail in Chap. 3, and together these two functions determine the steady state cardiac output, venous return, and right atrial pressure.

 ifference Between Hydraulic D and Electrical Models of the Circulation It is now possible to come back to the principle laid out in the introduction that volume is the independent variable for blood flow and not the arterial pressure. Note that Eq. 2.1 which describes the outflow from an elastic balloon has no pressure term. The circulation often is described with electrical models based on Ohm’s law:

V  IR

(2.3)

2  Volume and Regulation of Cardiac Output

Where V is the voltage and analogous to pressure differences, I is the current and analogous to blood flow, and R is the resistance as in the hydraulic model. The voltage difference is based on the difference between the source charge and the “ground value.” The voltage is thus a fixed, independent variable. It initially is not obvious where volume fits into this relationship. The “volume” is the number of electrons in the system, and since the current varies with resistance, this means that the volume of electrons changes with changes in current, and volume is not a fixed value in an electrical model as is the case in the hydraulic model of circulation presented in this chapter. This fundamental limitation of electrical models must always be considered. For example, an electrical model does not easily deal with changes in vascular capacitance. Of note, in an electrical model, “capacitance” is the equivalent of “compliance” in a hydraulic model and “unstressed” volume has no meaning because it has no force.

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the heart (Guyton et al. 1957). In an elegant set of experiments, he showed that venous return is determined by stressed volume, venous compliance, and venous resistance (as in Eq.  2.1), as well as the downstream pressure for venous outflow which is the right atrial pressure. Right atrial pressure, or central venous pressure, can be used interchangeably because their resistance from the great veins to the right heart normally is negligible. The stressed vascular volume and venous compliance determine the upstream MSFP: MSFP 

Arthur Guyton created a very effective way of analyzing the regulation of cardiac output by building on the work of Ernest Starling who also appreciated the importance of the return of blood to the heart as a determinant of cardiac output (Guyton 1955; Guyton et al. 1973). The basis of Guyton’s analysis is that two functions determine the steady state cardiac output. One is the cardiac function as described by Starling (Patterson et al. 1914) and includes heart rate and the determinants of stroke volume which are the preload of the heart, afterload, and contractility (Fig.  2.5). Starling’s function curve describes the output of the heart based on the preload of the right ventricle (right atrial pressure or Pra) and the properties of the flow to the aorta and all the structures in between, including the pulmonary arterial and venous compliances and resistances, and the right and left ventricles. Guyton’s original contribution was to add another function that describes the return of blood from the venous compliant regions back to

(2.4)

Where γ is the stressed volume and Cv is the venous compliance. With a constant blood volume and constant venous resistance, venous return is determined by the right atrial pressure which in turn is determined by the function of the heart. The venous return function is thus: VR 

Guyton’s Analysis

 Cv



MSFP  Pra Rv

(2.5)

where Rv is the venous resistance. The equation also can be written in terms of volume without pressure by substituting Eq. 2.4 into 2.5: VR 

  Pra Cv RvCv

(2.6)

The product of Rv and Cv is the time constant of drainage. It becomes evident that Pra is common to both the cardiac function, where it acts as preload, and to the return function where it is the downstream pressure for venous outflow. In his elegant experiments, Guyton examined the determinants of these two functions and how they interact.

Guyton’s, Graphical Approach A major contribution of Guyton’s was the development of a graphical approach to mathematically solve the interaction of the cardiac and return functions (Guyton 1955). He developed a

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function curve to describe the return of blood to heart (venous return curve) (Fig.  2.6) and combined it with Starling’s cardiac function curve (Fig.  2.5). He reasoned that the key role of the heart in the regulation of the return of venous blood is to lower right atrial pressure (Pra). He thus plotted his venous return curve with Pra on the x-axis and the associated return of venous blood on the y-axis (Fig. 2.6). The x-intercept of this plot is the MSFP and the slope is the negative inverse of venous resistance (−/Rv). This curve is a simple pressure-flow relationship but has the reversed pattern from the usual curve, because instead of flow being plotted against the inflow pressure, flow is plotted against the outflow pressure. This because the inflow pressure for venous return, MSFP remains relatively constant. Since the venous return curve has the same axis as the cardiac function curve, they can be plotted on the same graph and the intersection of the two curves gives the solution for the working right atrial pressure, working cardiac output, and working venous return (Fig.  2.7). An important point of confusion that often occurs when considering Guyton’s analysis is the difference between cardiac output and cardiac function and venous return and the venous return function. The functions describe a set of outputs or returns for a

Fig. 2.5  Starling’s cardiac function curve. The greater the Pra, the greater the cardiac output (Q) until a plateau is reached, and a further increase in Pra does not change Q. Each curve assumes a constant heart rate, contractility, and afterload. An increase in heart rate, or contractility, or a decrease in afterload shifts the curve upward so that for the same Pra, Q is higher. This indicates an increase in cardiac function

given set of conditions, whereas cardiac output and venous return describe actual values based on the intersection of these two functions. The following sections show how this analysis works. They are given as a pure change in one variable which of course does not happen in life because of reflex adjustments, but they still describe the dominant process that occurs when there is a primarily a change in cardiac function, return function, or both (Magder 2012). Change in Cardiac Function  Cardiac function is increased by an increase in heart rate, increase in contractility, or decrease in afterload (Fig. 2.5). All three of these shift the cardiac function curve upward and to the left. If there is no change in the return function, cardiac output increases and right atrial pressure falls (Fig.  2.8). It acts as if a

b

c

Fig. 2.6  Guyton’s venous return function. In the circulation model on the left (a), the right atrial pressure (Pra) equals MSFP and flow is zero. On the right (b), Pra is less than MSFP and flow can occur. The bottom (c) shows the graphical solution to the interaction of the pump and return functions. The beating heart lowers Pra and allows blood to come back to the heart; the heart here has a “permissive function (blue downward arrow). The heart then pumps the blood out and back to the compliant region and thus has a “restorative” function (upward red arrow). In the graph, the x-axis is pressure and the x-intercept is the MSFP.  The slope is the negative value of the inverse of venous resistance. When Pra is less than the surrounding pressure (i.e., atmosphere when breathing spontaneously), the veins collapse when they enter the chest and there is flow limitation. The downstream pressure for the return function then remains at ~0 mmHg

2  Volume and Regulation of Cardiac Output

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Fig. 2.7  Determination of CVP (Pra) by the interaction of the pump function (“Starling curve”) and the return function (“Guyton venous return function”). The limits of the cardiac and return functions also are marked. See text for details

Fig. 2.8  Change in cardiac output by change in cardiac function. The left side shows an increase in cardiac function with the same venous return curve. Cardiac output

rises and Pra falls. It is as if the heart was lowered relative to the rest of the body and increased the pressure difference for venous return

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the heart is lowered relative to the rest of the body. If cardiac function decreases, the opposite is seen; cardiac output falls and right atrial pressure rises.

tance does not change the x-intercept, i.e., MSFP, but rotates the venous return curve upward. This produces a rise in cardiac output with a rise in Pra as is seen with a fluid bolus (Fig. 2.9c).

Change in Stressed Volume  If stressed volume is increased by a bolus of intravenous fluid, MCFP and MSFP increase but not equally because the volume distributes through all compartments including the pulmonary vessels. Under normal physiological conditions, the difference is very small but the difference becomes more important if cardiac function is decreased, especially if left heart function is decreased. I am emphasizing this difference because measurements of MCFP and MSFP depend upon the experimental technique used to obtain them. What matters for cardiac output is MSFP because this is the upstream force driving the flow of blood back to the heart. A rise in MSFP, the x-intercept of the venous return curve, shifts the venous return to the right and intersects the cardiac function curve at a higher right pressure and higher cardiac output (Fig. 2.9b). If stressed volume is lost, for example because of a major hemorrhage, aggressive fluid removal, or large gastro-intestinal losses, MSFP falls and the venous return curve is shifted to the left. Cardiac output falls with a fall in Pra.

Change in Capacitance  As discussed above, a decrease in vascular capacitance recruits unstressed volume into stressed volume (Rothe 1983a, b; Rothe et  al. 1990). This increases MSFP without changing total blood volume. The effect on cardiac output and Pra, however, is identical to what is seen with a volume bolus (Fig.  2.10). Cardiac output rises with a rise in Pra. An increase in vascular capacitance, which commonly occurs with sedation, results in a decrease in cardiac output with a fall in Pra and looks identical to a loss of total active blood volume (Green et al. 1978).

Change in Venous Resistance  Venous resistance can be decreased pharmacologically by beta-agonists and nitrates (Deschamps and Magder 1992; Green 1977; Mitzner and Goldberg 1975). The overall resistance to venous return also can be decreased by redistribution of proportion of blood flow going to the splanchnic circulation versus the muscle compartment (Deschamps and Magder 1992; Mitzner and Goldberg 1975). This is discussed further in a later section. A decrease in venous resistance is likely a major factor for the increase in cardiac output that occurs in sepsis, but this has not been well established because almost all animal models of sepsis do not show the characteristic high output state seen in humans with sepsis (Magder and Quinn 1991). A decrease in venous resis-

 ummary of Interaction of Cardiac S and Return Functions An increase in cardiac output that occurs with a decrease in Pra indicates that the primary physiological change was an improvement in cardiac function (Fig. 2.8) (Magder 2012). If cardiac output falls with an increase in Pra, the primary problem was a fall in cardiac function. This could be from a decrease in right heart function, increase in pulmonary vascular resistance, or a decrease in left heart function. This analysis does not distinguish these and other tests or clinical assessments are needed. If cardiac output increases with a rise in Pra, the primary physiological change was an increase in the return function (Fig.  2.9). This could be due to an increase in total blood volume, a decrease in venous capacitance, or a decrease in venous resistance. A fall in cardiac output with a fall in Pra indicates that the primary physiological change was a decrease in the return function, which could be because of a loss of total blood volume, an increase in capacitance by removal of vascular tone, or an increase in venous resistance. These simple relationships between cardiac output and CVP can allow rapid assessment of the primary pathophysiological process and provide a physiological approach to management.

2  Volume and Regulation of Cardiac Output

a

b

Fig. 2.9  Change in cardiac output by a change in the return function. The first panel (a) shows the basic interaction of the cardiac and return functions. In the second panel (b), vascular volume is increased which raises MSFP.  The venous return curve shifts to the right and

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c

intersects the cardiac function curve at a higher Pra and a higher Q. In the last panel (c), the slope of the return function increases which indicates a decrease in venous resistance. Q again rises with a rise in Pra, but in this case, MSFP is unchanged

 imits of the Cardiac and Return L Functions

Fig. 2.10  A decrease in vascular capacitance. A decrease in vascular capacitance tightens up veins and venules (decrease in circumference). It is as if the opening of the container was lowered. More volume becomes stressed, which increases MSFP. This is seen as a shift to the left of the volume vs pressure relationship of the vasculature. A given volume then has a higher pressure. The venous return curve is shifted to the right and Q rises with a rise in Pra just as occurred when volume was given. This is the body’s way of giving an auto-transfusion

Both the cardiac and return functions have important limits (Fig.  2.7). The Starling mechanism requires that there be an increase in the length of cardiac myofibers during diastole for there to be an increase in stroke volume. However, cardiac muscle evolved differently from skeletal muscle so that it cannot be overstretched. If this were not the case, at high rates of venous return, stroke volume would decrease which would be highly disadvantageous. Under normal conditions, the pericardium constrains acute dilatation of the heart even before the sarcomere limit is reached (Holt et al. 1960). This produces a sharp break to the passive filling curve of the right heart which is seen as the flat part of Starlings cardiac function curve (Figs. 2.5 and 2.7). When right heart filling

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is limited, increasing the venous return function increases Pra but does not increase cardiac output. When cardiac filling is limited, only an increase in heart rate, increase in contractility, or a decrease in afterload can increase cardiac output. The venous return function has a limitation, too, because of flow limitation in large veins returning blood to the heart. In contrast to arteries, walls of veins are floppy, and when the pressure inside a vein is less than surrounding pressure, the vessel collapses (Permutt and Riley 1963). When breathing spontaneously, pleural pressure is less than atmospheric pressure. As discussed in the chapter on heart-lung interactions, the heart is surrounded by pleural pressure so that cardiac pressure, too, becomes negative relative to atmospheric pressure. It is important to appreciate that Pra is not actually “negative,” it just is negative relative to atmospheric pressure, which is around 760  mmHg at sea level. When the great veins enter the thorax, the pressure inside these veins is less than the surrounding pressure and they collapse. When this happens, lowering Pra further does not increase cardiac output. This brings up a rather subversive point. The maximum possible venous return, and thus maximum possible cardiac output, occurs when the heart is removed and the great veins just drain to atmosphere. This indicates that most of the time the heart just gets in the way. However, this glorious situation only lasts for an instant as blood volume drains from the body and is not returned to the upstream reservoir. When venous return is limited, only an increase in stressed volume or a decrease in venous resistance can increase cardiac output (Fig. 2.11). There is an important evolutionary rational for why veins developed with floppy walls and are collapsible, whereas arteries have rigid walls and are not easily collapsible. As discussed in the chapter on blood pressure, in the upright posture, the pressures in the veins draining the cerebral circulation are very negative relative to the heart, i.e., less than −40  mmHg relative to Pra in an average sized person. If the veins in the head were as stiff as arteries, and thus did not collapse and limit flow, every time a person stood up the stressed and unstressed blood volume in the head

S. Magder

Fig. 2.11  The limit to venous return. When the pressure in the great veins is less than the atmospheric pressure (or pleural pressure in someone on positive pressure ventilation), the vessels collapse and produce the equivalence of a vascular water fall. When this happens, a further lowering of Pra (B) does not increase Q. Only giving volume and shifting the venous return curve to the right increase Q

would almost instantly be sucked out and severely damage cerebral tissues.

Krogh’s Two-Compartment Model of the Circulation So far in this chapter, the analysis has been based on a model with all venous compliance lumped in one compartment. However, in 1914, August Krogh introduced a model that indicated that if there are venous regions in parallel that have different compliances and different time constants of drainage, the distribution of flow between the compartments alters the rate of flow return (Permutt and Caldini 1978; Krogh 1912; Caldini et al. 1974) (Fig. 2.12). Remember that the time constant of drainage is the product of the compliance of the region and the resistance draining it. The analysis is simpler if just two general types of compartments are considered, one with a fast and one with a slow time constant of drainage. Furthermore, it is the compliance term that has the most significant difference among different regions (Deschamps and Magder 1992; Caldini et al. 1974). The splanchnic circulation has been shown to have a slow time constant of drainage, which is in the range of 20–24 seconds, whereas

2  Volume and Regulation of Cardiac Output

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Fig. 2.12 Krogh’s two-compartment model and the effect of a change in the fractional distribution of cardiac output on venous return. In A, the distribution of flow is equal to the splanchnic bed (Ra-s) and the peripheral (muscle) bed (Ra-p). In B, the fractional flow to the

peripheral bed is increased. As a result, the resistance to venous return is decreased and venous return is higher for the same total volume. (From reference Magder and Scharf 2001). Used with permission of Taylor & Francis Group LLC)

the peripheral muscle bed has a fast time constant of drainage, which is in the range of 3–6 seconds (Deschamps and Magder 1992; Green 1977; Mitzner and Goldberg 1975). A shift in the fraction of the total blood flow to the fast time constant muscle vasculature increases venous return (Caldini et  al. 1974). This occurs because the increase in flow to the less compliant muscle results in an accumulation of volume and, consequently, an increase in the regional equivalent of MSFP.  This increases the gradient for venous return from this region. In the Guyton analysis, this is evident as an increase in the slope of the venous return function (decrease in resistance) with no change in the x-intercept. An increase in the fraction of flow to the splanchnic bed does the opposite. Venous return decreases because the splanchnic bed can accumulate more blood with a smaller increase in regional equivalent of MSFP. This is evident as a decrease in the slope of the venous return curve.

Summary The primary force driving flow around the circulation is the elastic energy produced by the distention of the elastic walls of the circulation by the volume they contain. When the closed circulatory loop is opened to atmosphere, this force can create flow even without a cardiac contraction. The heart cannot produce a flow rate higher than the instantaneous flow that would occur when the non-beating system is opened to atmosphere. Cardiac chambers act by transiently increasing the elastance of their walls in systole. This increases the pressure in the volume they contain and displaces that volume into the next compartment. This volume is then passed around the system as the pressure rises in each section compared to the next downstream section until the pulse of volume is back to the emptied right ventricle. Another key role of the heart is thus to reduce its elastance back to the resting state and

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allow the equivalent of the ejected bolus to come back to the heart. The pressure difference required for the stroke return from the veins is much smaller than that required for the ejection of the forward stroke volume, which makes the right atrial pressure a key determinant of flow around the system.

References Bishopric NH.  Evolution of the heart from bacteria to man. Ann N Y Acad Sci. 2005;1047:13–29. Caldini P, Permutt S, Waddell JA, Riley RL.  Effect of epinephrine on pressure, flow, and volume relationships in the systemic circulation of dogs. Circ Res. 1974;34:606–23. Deschamps A, Magder S. Baroreflex control of regional capacitance and blood flow distribution with or without alpha adrenergic blockade. J Appl Physiol. 1992;263:H1755–H63. Drees J, Rothe C.  Reflex venoconstriction and capacity vessel pressure-volume relationships in dogs. Circ Res. 1974;34:360–73. Green JF. Mechanism of action of isoproterenol on venous return. Am J Physiol. 1977;232(2):H152–H6. Green JF, Jackman AP, Krohn KA.  Mechanism of morphine-­ induced shifts in blood volume between extracorporeal reservoir and the systemic circulation of the dog under conditions of constant blood flow and vena caval pressures. Circ Res. 1978;42(4):479–86. Guyton AC. Determination of cardiac output by equating venous return curves with cardiac response curves. Physiol Rev. 1955;35:123–9. Guyton AC, Polizo D, Armstrong GG. Mean circulatory filling pressure measured immediately after cessation of heart pumping. Am J Phys. 1954;179(2):261–7. Guyton AC, Armstrong GG, Chipley PL. Pressure volume curves of the arterial and venous systems in live dogs. Am J Physiol. 1956;184:253–8. Guyton AC, Lindsey AW, Bernathy B, Richardson T.  Venous return at various right atrial pressures and the normal venous return curve. Am J Phys. 1957;189(3):609–15. Guyton AC, Jones CE, Coleman TG. In: Guyton AC, editor. Circulatory physiology: cardiac output and its regulation. Philadelphia: W.B. Saunders Co.; 1973. Holt JP, Rhode EA, Kines H. Pericardial and ventricular pressure. Circ Res. 1960;VIII:1171–80. Krogh A. The regulation of the supply of blood to the right heart. Skand Arch Physiol. 1912;27:227–48. Magder S.  An approach to hemodynamic monitoring: Guyton at the beside. Crit Care. 2012;16:236–43.

S. Magder Magder S. Volume and its relationship to cardiac output and venous return. Crit Care. 2016;20:271. Magder S, De Varennes B.  Clinical death and the measurement of stressed vascular volume. Crit Care Med. 1998;26:1061–4. Magder S, Quinn R. Endotoxin and the mechanical properties of the canine peripheral circulation. J Crit Care. 1991;6:81–8. Magder S, Scharf SM.  Venous return. In: Scharf SM, Pinsky MR, Magder SA, editors. Respiratory-­ circulatory interactions in health and disease. 2nd ed. New York: Marcel Dekker, Inc.; 2001. p. 93–112. Mitzner W, Goldberg H. Effects of epinephrine on resistive and compliant properties of the canine vasculature. J Appl Physiol. 1975;39(2):272–80. Moorman AF, Christoffels VM. Cardiac chamber formation: development, genes, and evolution. Physiol Rev. 2003;83(4):1223–67. Pascual-Anaya J, Albuixech-Crespo B, Somorjai IM, Carmona R, Oisi Y, Alvarez S, et  al. The evolutionary origins of chordate hematopoiesis and vertebrate endothelia. Dev Biol. 2013;375(2):182–92. Patterson SW, Piper H, Starling EH. The regulation of the heart beat. J Physiol. 1914;48(6):465–513. Permutt S, Caldini P.  Regulation of cardiac output by the circuit: venous return. In: Boan J, Noordergraaf A, Raines J, editors. Cardiovascular system dynamics, vol. 1. Cambridge and London: MIT Press; 1978. p. 465–79. Permutt S, Riley S.  Hemodynamics of collapsible vessels with tone: the vascular waterfall. J Appl Physiol. 1963;18(5):924–32. Rothe C.  Venous system: physiology of the capacitance vessels. In: Shepherd JT, Abboud FM, editors. Handbook of physiology. The cardiovascular system. Section 2. III.  Bethesda: American Physiological Society; 1983a. p. 397–452. Rothe CF.  Reflex control of veins and vascular capacitance. Physiol Rev. 1983b;63(4):1281–95. Rothe CF, Flanagan AD, Maass-Moreno R. Reflex control of vascular capacitance during hypoxia, hypercapnia, or hypoxic hypercapnia. Can J Physiol Pharmacol. 1990;68:384–91. Simoes-Costa MS, Vasconcelos M, Sampaio AC, Cravo RM, Linhares VL, Hochgreb T, et  al. The evolutionary origin of cardiac chambers. Dev Biol. 2005;277(1):1–15. Sylvester JT, Goldberg HS, Permutt S. The role of the vasculature in the regulation of cardiac output. Clin Chest Med. 1983;4(2):111–26. Xavier-Neto J, Castro RA, Sampaio AC, Azambuja AP, Castillo HA, Cravo RM, et al. Parallel avenues in the evolution of hearts and pumping organs. Cell Mol Life Sci. 2007;64(6):719–34.

3

Function of the Right Heart Sheldon Magder

Introduction Right heart dysfunction is common in critically ill patients. Causes include sepsis, dysfunction following cardiac surgery, chronic lung diseases, chronic pulmonary vascular disease, and pulmonary vascular obstructive processes. Right heart dysfunction can occur acutely or as an acute on chronic process. Acute processes occur primarily when there is an excessive pressure load for right ventricular (RV) ejection as with an acute pulmonary embolism or during mechanical ventilation, and it also can occur when cardiac function is decreased as in sepsis or after cardiac surgery. Failure from chronic right heart processes occurs when adaptive mechanisms reach their limits as in end stages of primary pulmonary hypertension or secondary causes of pulmonary hypertension. The right heart also can limit cardiac output because of iatrogenic actions, such as excessive volume loading, even without intrinsic dysfunction of cardiac muscle. There have been several recent excellent reviews on RV dysfunction. These have emphasized acute right heart failure (Harjola et al. 2016), overloaded right heart and ventricular interdependence (Naeije and Badagliacca 2017), right versus left ventricular S. Magder (*) Royal Victoria Hospital (McGill University Health Centre), Departments of Critical Care and Physiology McGill University, Montreal, QC, Canada e-mail: [email protected]

failure in congenital heart disease (Friedberg and Redington 2014), and right ventricular hypertrophy and right heart failure (van der Bruggen et al. 2017). The emphasis in this chapter is on physiological and pathological implications for the management of the RV in the critically ill. Some of these ideas have been previously discussed (Magder 2007). This chapter will refer to principles in Chap. 2 on volume and regulation of cardiac output, Chap. 4 on the left ventricle function, Chap. 50 on cardiogenic shock, and Chap. 18 on heart-lung interactions.

Origins of the Right Heart The earliest forms of the four-chamber mammalian heart evolved in vertebrates (Simoes-Costa et  al. 2005; Xavier-Neto et  al. 2007; Bishopric 2005; Pascual-Anaya et al. 2013). Knowledge of the evolution of the heart helps understand why the right heart functions the way it does and what limits its output. The heart had its evolutionary beginnings with a single circuit that had an atrium and ventricle in series (Simões-Costa et al. 2005). As an early example, in fish, blood is pumped from the equivalents of an atrium and a ventricle through a gas-exchange mechanism in the gills that oxygenates the blood and removes carbon dioxide (CO2). Upon exiting the gills, the circuit delivers oxygen (O2) and removes waste products from the rest of the body and the de-oxygenated blood

© Springer Nature Switzerland AG 2021 S. Magder et al. (eds.), Cardiopulmonary Monitoring, https://doi.org/10.1007/978-3-030-73387-2_3

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returns to the heart. In sedentary fish, the heart obtains its needed O2 from the returning blood by diffusion through trabeculations in the walls of the heart. Accordingly, this blood is the most deoxygenated in the body. In more active fish species, up to 35% of the O2 for the heart comes directly from the gills through a primitive coronary circulation (Bettex et al. 2014). Although this simple system is adequate for the low aerobic needs of lower level organisms, and for modest rapid burst in species with primitive coronaries, it does not allow for the high aerobic power of mammals and birds. Furthermore, gas-­ exchange vessels in the gills receive blood ejected from the heart with the highest vascular pressure in the organism. These species thus could not have the delicate alveolar structures that exist in birds and mammals. An adaptation that helps protect the gas-exchange region is the conus arteriosus just after the ventricle. This not only dampens the ejected pulse pressure that flows into the gills but also dampens the arterial pressure for the rest of the body. A three-chamber heart with two atria and one ventricle emerged in amphibians when species began to move on to the land and breathe air. This allowed the evolution of a separate pulmonary circuit. Respiratory function now could be separated from other organ function, and fully oxygenated blood returns to the heart, although this blood still is mixed with returning deoxygenated venous blood from the rest of the body (Bishopric 2005). This stage could be considered the dawn of the era of heart-lung interactions! At the reptilian stage, things became more complicated and various approaches evolved. Some reptiles, such as turtles, still have a three-chamber heart and low arterial pressure. Pythons and some lizards have almost complete separation of the ventricles and the outflow to the lungs is occluded during systole, thus separating the return of oxygenated and deoxygenated blood. Other reptiles, such as crocodiles, have true four-chamber hearts, but there are two aortic arches, which allow varying ratios of pulmonary to systemic flows (Bettex et al. 2014). Their arterial pressures generally are lower than in mammals and their aerobic power is much lower. Fully separated right (RV) and left ventricles (LV) only appeared with the evolution

of birds and mammals. This fully evolved structure allowed development of a low-pressure pulmonary system that has delicate high efficiency gas-exchange structures, coronary blood flow with a high-pressure source and fully oxygenated blood, and a high-pressure systemic circulation that allows rapid changes in regional flows by decreasing regional arterial resistances based on local metabolic needs (Chap. 8). It is noteworthy that a separate pulmonary gas exchange region only evolved when systemic arterial pressure exceeded 50 mmHg (Bettex et al. 2014). During fetal life, the RV functions as the systemic ventricle and provides more than half of the cardiac output to the body as it supplies blood to the lower part of the body and the placenta. Little blood flow is needed for the low-pressure pulmonary circuit because there is no ventilation (Friedberg and Redington 2014). After birth, the RV just has to face the pressure in the low-­ resistance pulmonary circuit, and its walls become thinner right after birth. Based on studies in animals, it has been argued that loading the RV early in life can maintain the right heart’s fetal transcriptional program and prevent RV muscle regression. This potentially can facilitate the development of RV hyperplasia in children who have congenital heart problems that are associated with rising pulmonary artery pressures later in life (Friedberg and Redington 2014; van der Bruggen et al. 2017; Apitz et al. 2012). Evidence for this is supported by the observation that children with Eisenmenger’s syndrome, or corrected transposition of the great arteries, tolerate increased pulmonary pressure for prolonged periods better than children with other congenital or pathological conditions in which the pulmonary load develops later in life (Hopkins 2005; Hopkins et al. 1996; Dos et al. 2005).

Differences Between RV and LV In 1998, it was discovered that the embryological development of the RV is directed by the transcription factor Hand 2, whereas that of the LV is directed by Hand 1, which actually evolved after Hand 2 (Thomas et  al. 1998; Srivastava and

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3  Function of the Right Heart

Olson 2000). The LV develops from an anterior heart field, whereas the RV evolves from a genetically more primitive field (Zaffran et al. 2004). These differences in transcriptional programs produce differences in the electrophysiological, pharmacological, and contractile properties of the RV and LV (Table 3.1). Electrophysiological  Sarcomeres from RV and LV respond differently to changes in frequency of contractions. When the frequency of sarcomere contractions is suddenly slowed, those from the RV have a small increase in their length of shortening, whereas those from the endocardial surface of the LV have a large increase in the length of shortening. Sarcomeres from the LV epicardium are more similar to those of the RV, and they only shorten a little with a decrease in frequency (Kondo et al. 2006). These differences in shortening patterns are associated with different electrophysiological properties in cell memTable 3.1  Differences in properties of right and left ventricles Transcriptional factor Electrophysiological

Response to α-agonists Endocardial endothelial cells

Normal resting end-systolic elastance Coronary flow

Right ventricle Left ventricle Hand-2 Hand-1 Less sarcomere shortening when contraction frequency slows Lower density of K+ channels and they turn off more slowly Decrease myofiber shortening Thinner; lower baseline intracellular [Ca2+] 4–6 mmHg/ ml Diastole and systole

Greater sarcomere shortening when contraction frequency slows Greater density of K+ channels and they turn off faster Small increase in myofiber shortening Wider; greater baseline Ca2+ 1.3–2.0 mmHg/ml Primarily diastole

branes, which ultimately contribute to differences in the force production of the RV and LV.  LV myocyte potassium currents turn off faster than those of the RV; the density of potassium channels, too, is greater in the LV.  These properties allow more time for calcium ions to enter LV myocytes during the fixed time of the action potential and result in the greater LV force production than that of the RV.

Endocardial endothelial cells (EEC)  Just as all blood vessels have a layer of endothelial cells, cardiac chambers, too, are lined by a layer of cells with properties that are similar to those of vascular endothelial cells, although they evolved from cardiac fields rather than the mesenchymal field, which is the source of vascular endothelial cells (Brutsaert et al. 1996; Brutsaert 2003). EEC have membrane receptors that allow peptide signaling from both the blood inside the cardiac chambers and peptides that come from the underlying cardiomyocytes (Jacques et  al. 2003, 2006a, b, c). These cells also secrete peptides such as endothelin-1 and neuropeptide Y, which can have both autocrine and paracrine effects on these cells. There are many differences in EEC of the right and left sides of the heart, which result in differences in the regulation of the blood-heart barrier on the two sides of the heart (Abdel-­Samad et al. 2012, 2016). EEC from the RV are thinner and have a lower baseline calcium ion concentration in both their cytoplasm and nuclei than EEC from the LV (Fig. 3.1). The increase in intracellular calcium ions in response to the same concentration of peptides, such as human polypeptide Y and endothelin, is much greater in right-sided EEC compared to left-sided EEC (Abdel-Samad et al. 2016). These differences in right- and left-sided EEC properties can allow each side of the heart to have its own regulatory responses to circulating vascular signaling molecules, as well as metabolic factors, such as tension of O2 and CO2 and hydrogen ion concentration.

Pharmacological Differences  Although alpha-­ adrenergic agonists are thought in general to have

S. Magder

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EECL

EECR a

b

control

c

Syto-11

d

control

Syto-11

10

0 10

10

255

10

Fig. 3.1  Higher basal intracellular Ca2+ level in left ventricular endocardial endothelial cells compared to those from the right ventricle. 3D quantitative confocal microscopy images (top view) showing the basal intracellular distribution and levels of Ca 2+-Fluo 3 complexes in endocardial endothelial cells derived from the right (EECR, a)

and left (EECL, c) ventricles. Panels (b) and (d) show labeling of the nuclei of cells in panels (a) and (c) respectively using the nucleic acid probe syto-11. The white scale bar is in μm. (Unpublished data. Used with permission of Dr Danielle Jacques)

little effect on cardiac myocytes, there are striking differences between their actions on the RV and LV.  Phenylephrine, a pure α-agonist, produces a small increase in the degree of shortening of LV muscle in situ, but it produces a marked decrease of RV muscle shortening (Wang et  al. 2006). A chronic infusion of  norepinephrine increases LV but not RV mass (Irlbeck et  al. 1996), although this could be because norepinephrine produces only a small increase in pulmonary artery pressure, but a large increase in systemic arterial pressure and thus produces a greater afterload effect on the LV (Datta and Magder 1999). The significance of this is that pharmacological agents can potentially affect the RV and LV differently.

RV diastolic compliance is much greater than that of the LV so that there only are small changes in RV pressure with an increase in RV volume. The diastolic filling curve of the RV, though, has a sharp break at which pressure rises steeply with little change in volume. Pressure loads on the RV and LV, too, are very different. The LV generates systolic pressures greater than 100 mmHg, whereas RV systolic pressure is usually 20 mmHg or less (Fig. 3.3). Accordingly, the LV wall normally is much thicker than the RV wall (Fig.  3.2). The peak-generated pressure for a given volume of the RV is only half that of the LV. These differences are obvious when the pressure-volume curves of the RV and LV are compared (Fig. 3.3).

Shape and Load Differences Between the Ventricles  The implications of these will be discussed in the next section but some brief points need to be made here. The LV has the appearance of an American football cut in half at its widest diameter, whereas the normal RV wraps around the LV and functions more like a bellows than a constricting circle (Friedberg and Redington 2014; Voelkel et al. 2006) (Fig. 3.2).

Right Ventricular Ejection Basic Principles It is important to start with statements of some basic underlying principles (Table  3.2). These may seem obvious, but they often are forgotten in the circular reasoning which is at the core of

3  Function of the Right Heart

RV

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LV

LV

RV

a

Normal

b

IPAH

c Fig. 3.2  Cast of the right ventricle indicating its complicated shape with a base and infundibular portion. The top shows the relative sizes of the RV and LV; on the left (a) a normal situation, and on the right (b) a markedly hypertrophied ventricle with a greatly reduced LV and the septum curved to the left. Bottom (c) shows MR angiogram of the RV and pulmonary arteries in a subject with idiopathic pulmonary hypertension. The RA and RV are prominent with heavy RV trabeculations. (From Voelkel et al. (2006). Used with permission of Wolters Kluwer Health, Inc.)

an understanding cardiovascular physiology. First, the LV only can put out what the RV gives it, and the RV only can put out what returns to it from the upstream venous reservoir. The cyclic nature of cardiac function sets the time available for filling and ejection from the ventricles. Normally, the RV does not reach its volume limit. Rather, filling of the RV in diastole is determined by the time available for the RV to fill and how fast blood comes back to it. In pathological conditions, RV filling can reach the maximum RV diastolic volume, and when this happens, it has major repercussion for the interaction of RV and LV and sets the limit of stroke volume. Finally, conservation of mass must

always be considered; if a distended RV decreases filling of the LV, the volume must accumulate somewhere else in the circulation, which means that pressure must increase somewhere else in the vasculature. Basic mechanisms of flow generation are similar for the RV and LV. During diastole, ventricular walls are stretched by the returning blood. The pressure created by the stretched ventricular walls is their preload and sets the length of cardiac myocytes based on the diastolic compliance of the walls. The return of blood is driven by energy from the elastic recoil of the compliant upstream venous region. For the RV, the upstream pressure is in the systemic veins and is called mean systemic filling pressure (MSFP) (Chap. 2, Magder). In a parallel way, blood returns to the LV from the elastic recoil of the pulmonary venous compartment. The total compliance of pulmonary vessels (arterial and venous), though, only is about one-seventh of that of the systemic veins and, accordingly, the volume reserve in the pulmonary veins is small (Lindsey and Guyton 1959). Thus, filling of the LV is dependent upon what the right heart gives it, whereas the large volume reserve in the systemic venous compliance means that the RV is not directly dependent upon LV output. The RV is considered by some to normally function below its stressed volume. However, this is unlikely because the RV demonstrates an active length-tension relationship and a force is needed to stretch the sarcomeres (Maughan et al. 1979; Redington et al. 1988a). More likely, what is happening is that in the range of normal stroke volumes, the passive filling curve of the RV is very compliant, and the very small changes in its diastolic pressure are hard to detect unless properly amplified. Since changes in diastolic volume produce changes in the generated systolic pressure, there must be some increase in stress on right-­sided sarcomeres for them to detect a change and this indicates that a force has to be present. It also is important to appreciate that negative values relative to atmosphere do not mean that the transmural pressure is negative as the heart is in the sub-atmospheric pleural space (Slinker et al. 1987; Watkins and LeWinter 1993).

S. Magder

26

120

Es-Rv

LV SV

RV SV Volume

Dog 17 kg

Pressure (mmHg)

Pressure

Es-Lv

90 Heart wt. 91 g 60

30

0

0

150 Volume (ml)

300

Fig. 3.3  Pressure-volume relationships of the right and left ventricles (left) and the pericardium (right). Details are in the text. Note that the end-systolic left ventricular pressure- volume (Es-LV) is much steeper than the end-­ systolic pressure-volume line (Es-Rv). The diastolic passive filling curve of the RV breaks more sharply than that

of the LV.  Although the LV systolic pressure is much higher than that of the RV, their stroke volumes are equal. The right side shows the steep break to the pressure volume relationship of the pericardium. (From Holt et  al. 1960). Used with permission of Wolters Kluwer Health, Inc.)

Table 3.2  Characteristics of ventricular pressure-volume relationship

In most people, the RV passive filling curve becomes very stiff at around 10 mmHg referenced to the middle of the right atrium, although there is a lot of individual variability of the actual value at the break of the curve (Magder and Bafaqeeh 2007). Pericardial tissue is very non-­ compliant and when intact, it creates a sharp limit to RV filling. This limit usually occurs before the steep part of the passive filling curve of the RV wall itself is reached (Fig.  3.3) (Watkins and LeWinter 1993; Holt et al. 1960). However, even without a pericardium, the cardiac cytoskeleton still sharply limits RV filling. In contrast to the diastolic properties of the RV, the compliance of the LV passive filling curve has a steeper slope even at low pressures, and the increase at higher values is less sharp (Fig. 3.3). The LV thus has a more progressive increase in diastolic pressure with volume, and the filling curve becomes much steeper and limiting at around 18 mmHg. However, when the pericardium is in place, or when the lungs, the RV itself, or the space available in the mediastinum becomes limited, LV filling, too, can become sharply limited by these other structures (Butler 1983).

Systolic peak pressure Diastolic filling

Right ventricle 120 mmHg

Left ventricle 20 mmHg

Low diastolic pressure (−2 to 4 mmHg) Low initial slope (high compliance)

Moderate diastolic pressure (5–10 mmHg) Moderate slope (moderate compliance) Gradually increasing slope of diastolic filling curve at peak volume Isovolumetric initial phase

Sharp break in diastolic filling curve at peak volume Initial systolic rise in pressure End of systole Stroke volume

Curvilinear initial P-V curve

Can have hang-out phase of stroke volume Large changes with either positive or negative inspiratory activity

Sharp end to stroke volume at max Es Moderate effects from either positive or negative inspiratory efforts

3  Function of the Right Heart

280 msec

80

200

60

Pressure (mmHg)

180 msec 40 180 20

140 0 msec

0 -10 0

10

20

30

40

50

Volume (ml) 80 280 msec 60

Pressure (mmHg)

Under normal conditions, the limit of RV filling provides a protective mechanism for the lungs by preventing excessive RV output from over-filling the LV. This prevents flooding of the lungs by excessive increases in LV diastolic pressure. This safety mechanism fails when RV function is maintained, and LV function is severely depressed. In this situation, the functioning RV keeps transferring volume to the pulmonary vasculature but the depressed LV cannot handle the volume and maintain the normally low pulmonary capillary pressure. This especially can be a problem in someone in whom the RV and LV are supported by mechanical devices, and the right-­ sided device generates more flow than the left-­ sided device. Ejection from both the right and left ventricles occurs by what Sagawa called, a time-varying elastance (Sagawa 1978; Suga et al. 1977) which is discussed more fully in Chap. 4 for the LV. Unlike other vascular tissues, the elastances of ventricular walls (and atria), i.e., change in pressure for change in volume, rhythmically increase during the cardiac cycle; this rising phase of the elastance of ventricular walls defines systole. The rise in elastance increases the pressure of the volume in the ventricles and is described by the slope of a line on a pressure versus volume plot (Figs.  3.3 and 3.4). The maximum slope of the end-systolic pressure-volume line is the maximum elastance (Es-max) during the cycle and is considered the best volume-­ independent indicator of ventricular function. Details of the time-varying elastance in the RV were nicely documented by Maughan et  al. and Sagawa (Fig. 3.4) (Maughan et al. 1979). The process occurs at a much lower scale in the RV than in the LV (Maughan et al. 1979; Redington et  al. 1988a; Dell’Italia and Walsh 1988a; Dell’Italia et  al. 1985; Redington et  al. 1990) (Fig.  3.4). When the outflow from the RV is blocked, the pressure rises to the value on the Es-max line for that volume (upper part of Fig. 3.4). When the pulmonary valve opens, the RV pressure rises until the pressure is greater than pulmonary arterial pressure. This opens the pulmonary valve and blood is ejected. Blood con-

27

180

180 msec

40

140 20 0 msec 0 -10 0

10

20

30

40

50

Volume (ml)

Fig. 3.4  Time-varying elastance of the RV.  The upper figure shows isovolumetric (clamped pulmonary artery) and pressure volume loops with RV ejection (bottom) obtained by Maughan et al. (1979) in rabbit hearts. The lines indicate the changing elastance over time (numbers in msec) indicating that cardiac muscle elastance progressively increases during systole. The final line, the end-­ systolic pressure-volume line, gives the maximum pressure that can be generated from any initial volume. Unlike the pressure-volume curves of the LV, the “isotonic “phase of the pressure volume loop is curvilinear, indicating that filling of the RV continues after the onset of systole (From Maughan et al. (1979). Used with permission of Wolters Kluwer Health, Inc.)

tinues to flow out of the ventricle until the ventricular elastance begin its cyclic fall. The time available for ventricular ejection is largely determined by the time it takes for the pressure at a given volume to reach the Es-max produced in a cycle (lower part of Fig. 3.4). This time is determined by the time available for Ca2+ to be released and taken up again by cardiac myocytes. This

28

time in turn is determined by the length of the plateau of the ventricular action potential (Chap. 7). RV systolic pressure (and LV pressure) peaks and begins to fall before Es-max is reached. This happens because the rising ventricular and pulmonary pressures increase the flow of volume out of the pulmonary artery and the flow out becomes faster than the flow of volume entering it. When RV cavity pressure falls below pulmonary artery pressure, the pulmonary valve closes and prevents backflow into the ventricle. This usually is considered the end of systole. However, the end of systole theoretically should be considered as the end of the active rise of ventricular elastance (i.e., at Es-max) and the beginning of relaxation of ventricular elastance. In the LV, this most often corresponds to aortic valve closure but this is not necessarily so in the RV.  Timing of pulmonary valve closure sometimes can be delayed past the time that RV elastance has started to decrease. This occurs because closure of the ventricular outflow valve depends upon the pressure differences across the valve. When pulmonary arterial volume run-off is sufficiently rapid because of very low pulmonary vascular resistance, or if the RV stroke volume is small and only adds a small amount of volume to the pulmonary vasculature, pulmonary artery pressure can fall faster than RV cavity pressure falls. This is seen as a “hang-out” of the ventricular systolic pressure tracing and can make it difficult to identify the end of systole, as defined by the period of actively produced ventricular elastance. Consistent with this, Dell’Italia et  al. showed that the hang-out is determined by the rate of pulmonary artery emptying and the size of the stroke volume that it receives (Dell’Italia and Walsh 1988b). A hang-­ out is not seen when pulmonary vascular resistance is increased because pulmonary pressure remains higher than RV cavity pressure and the pulmonary valve closes more quickly as is more typical for the LV.  The important point to take away from this discussion is that the value of the pressure and volume at closure of the pulmonary valve is not necessarily a point on the RV Es-max line. This complicates the assessment of the contractile function of the RV. In summary, Es-max is a very important physiological concept but it is

S. Magder

difficult to define in an intact organism. It is especially a problem when a single pressure-volume point is used to estimate RV Es-max (Trip et al. 2013). The Starling curve provides an accessible way of evaluating cardiac function. This function plots cardiac output against right atrial pressure (Pra). The derivation is shown in Fig.  3.5. The plot indicates that Pra is the preload for everything from the right atrium to the aorta. Cardiac output on the plot is what comes out of the aorta assuming a constant heart rate, constant afterload, and constant contractility for both the RV and LV and a constant pulmonary resistance. An advantage of this plot is that it emphasizes the importance of the RV in determining cardiac output because this plot also is the function curve for the RV.  However, it does not represent the LV function curve because that requires left atrial pressure on the x-axis. The plot emphasizes that the LV only can put out what the RV gives it.

 ressure-Volume Loops of the Right P and Left Ventricles There are important differences between pressure-­volume loops of the RV and LV.  The diastolic compliance of the RV is higher than that of the LV and the diastolic volume capacity at low pressures is much higher than that of the LV. The most striking difference between the RV and LV sides is that the slope of RV Es-max is much flatter than LV Es-max (Fig.  3.3). Thus, peak RV pressure is much lower than that of the LV, even though the two ventricles pump out the same stroke volume. The RV can do this because it ejects its stroke volume through the very low pulmonary vascular resistance. This is the evolutionary advantage for having a separate RV and pulmonary vasculature, in that it allowed the development of delicate gas-exchange structures. Unlike the systemic circuit, the pulmonary circuit also does not have to make major changes in distribution of flow by lowering regional resistances. Its systolic pressure thus can be kept low. Blood flow in the lung can be increased with little change in pressure by recruitment and distention

3  Function of the Right Heart

29

Pressure-Volume of RV

Cardiac function curve “Starling curve”

Q

P

4

5

3 2

10

5 4 3 1

1

2

0 V

Pra

Fig. 3.5  Derivation of the cardiac function curve from the pressure-volume relationship, in this case, of the RV. The cardiac function curve indicates that cardiac output from  the heart increases  with increases  in preload

(#1–5) while afterload (dotted line), “contractility” (slope of the end-systolic pressure volume line), and constant heart rate. The function curve has a sharp plateau when the limit of diastolic filling is reached

of vessels (see Chap. 5) (Mitzner and Goldberg 1975). However, when pulmonary pressure is higher than normal, the increased pressure cannot easily be acutely produced because the flatter Es-max line of the RV intersects the steep part of the passive filling curve at a much lower low pressure than the Es-max of the LV (Fig.  3.3). Because of this, when the required pulmonary arterial pressure is high for a normal flow, just dilating the RV is not enough to achieve the required higher systolic pressure, i.e., there is less preload reserve. Adaptations in both the diastolic pressure-volume relationship and the slope of Es-max must occur (Figs. 3.6 and 3.7) and are discussed further below. In the LV, pressure rises at the onset of systole without a change in volume until the aortic valve opens; this is called isovolumetric ventricular contraction. In contrast, the RV diastolic volume continues to increase after the onset of systole and the rising part of RV pressure-volume relationship is curved (Fig. 3.4). This has been attributed to distortions of the RV wall based on its complex structure with an inflow region and infundibular outflow region (Figs.  3.2 and 3.7).

However, this explanation is unlikely. Heart muscle acts as syncytium, and pressure is produced by the marked rise in the systolic elastance of all myocytes. A major delay in depolarization of one region would likely result in major arrhythmias (Vogel et  al. 2001). Also, if one region of the heart failed to significantly increase its elastance sufficiently fast enough, that region would expand as the pressure rises and would not produce efficient ejection. RV volume also would not increase unless more volume came in. Consistent with this reasoning, computer simulations that used linear equations to describe the rising elastance during systole to describe RV ejection still showed the same phenomena, that is, a curvilinear shape to the initial systolic rise in RV pressure (Fig. 3.8). What then can explain the continuing increase in RV volume at the onset of systole? Careful analysis of the curves reveals that continued RV inflow likely occurs because MSFP is still higher than the low RV end-diastolic pressure and this keeps the tricuspid valve open until the generated RV pressure is greater than MSFP, which usually is in the range of 8–10 mmHg (Fig.  3.8). This

S. Magder

30

SV

SV

1

3

1 2 3

Pressure

Pressure

2 3

3 2

2 1

1

Volume Fig. 3.6  Implications of the slope of the intersection of the RVEs and passive filling curve with normal, contractile function (left) and decreased RV-Es (right). When systole begins on the flatter part of the pressure-volume line, an increase in afterload (higher systolic peak pressure) can be accommodated by an increase in diastolic volume (#2) until the end-diastolic volume reaches the steep part

Volume of the diastolic passive filling curve. This indicates cardiac limitation without dysfunction. When that happens (Friedberg and Redington 2014), stroke volume must fall with a further rise in afterload. On the right, the RVEs is flatter and the end-diastolic needs to be higher to maintain normal stroke volume. Accordingly, the limit of RV filling is reached earlier, indicating RV dysfunction 30

Pressure (mmHg)

25

20

15

10

5

0 2

4 6 8 Volume (ccx10-1)

10

Fig. 3.7  Pressure-volume relationship of the RV obtained from angiograms and pressure measurements in a child. Note again the continuous filling of the RV after the onset of systole. The reconstructed loops (left side) also show

the complex shape of the RV. (From Redington et  al. (1988a). Used with permission of BMJ Publishing Group Ltd.)

phenomenon is very evident in the notching of the ascending pressure in the RV P-V loops obtained by Redington et al. (Fig. 3.7) (Redington et al. 1988a, 1990).

Pulmonary arterial diastolic pressure is normally only in the range of 10–15 mmHg. Thus, shortly after the tricuspid valve closes, the pulmonary valve opens, ejection begins, and RV vol-

3  Function of the Right Heart

31

140

Pressure (mmHg)

120 100

LV 80

MSFP 7.6 mmHg

60 40 20 0 10

RV 20

30

40

50

60

70

80

90

100

110

Volume (ml) Fig. 3.8  Comparison of pressure vs volume loops of RV and LV. In these computer-generated P-V loop of the RV and RV, which are generated by equations with no geometric factors, in contrast to the LV, the P-V loop has a curvilinear initial rise in pressure instead of the isovolu-

metric rise for the LV. Filling of the RV continues until the pressure in the RV is > MSFP. This value is close to the opening pressure of the pulmonary valve and RV ejection so that volume then begins to decrease with the rising pressure

ume begins to decrease as blood is ejected into the low-resistance pulmonary circuit. This further contributes to the concave curve of the rising systolic pressure in contrast to the isovolumetric rise seen in the LV (Figs.  3.4, 3.7, and 3.8). A lack of a curvilinear rise in the LV occurs because the much greater pressure rise in the LV obscures any slight delay of closure of the mitral valve, and the much lower compliance of the pulmonary venous system compared to systemic veins produces faster pulmonary venous drainage into the left atrium. Function of the RV often is considered in terms of coupling of the ventricular and pulmonary circuits (Vonk Noordegraaf et  al. 2017, 2019; Vonk-Noordegraaf et al. 2013; Fourie et al. 1992). This also can be examined in terms of the impedance to RV ejection. Although the impedance analysis is useful for characterizing the shape of the pulmonary pulse pressure, a factor that likely affects RV and pulmonary vascular transcriptional signaling (Urashima et al. 2008), it is less useful for understanding volume ejection from the RV and what limits stroke output by the

RV, which is discussed next. The dominant factor in pulmonary artery impedance is the pulmonary vascular resistance and this is what dominates the load on the RV.  In modeling studies, it can be pulse pressure. Flow from the RV is determined by the pressure difference between the walls of the RV and pulmonary artery, plus a small kinetic energy component, and some dampening of the pressure by the expansion of the walls of pulmonary vessels. As already discussed, the pressure generated in the ventricles during systole is determined by Es-max which is a function of the contracting cardiac myocytes. This point is evident at the extreme condition. When the pulmonary artery is clamped, peak RV systolic pressure solely is determined by the starting end-diastolic volume in the RV, which sets sarcomere length, and the pressure value associated with that volume on the Es-max line. In this case, peak isometric RV pressure is not affected by pulmonary elastance because it does not “see” it (Fig.  3.4). Second, when the RV ejects at a pressure below Es-max, blood is ejected until the volume in the ventricle

S. Magder

32

reaches the Es-max line in the time that is available for systole, although, as already noted, some volume may continue to empty due to the rapid run-off of blood from the pulmonary vasculature in the “hang-out” period (Dell’Italia and Walsh 1988b). A key factor limiting RV output is the limitation to its filling which occurs because of the sharp upward break of the diastolic passive pressure-­volume curve (Bishop et  al. 1964). As long as the diastolic volume of the RV is on the relatively flat part of the passive diastolic filling curve, stroke volume can be increased through the Frank Starling mechanism by increases in end-diastolic volume with only small increases in Pra (Figs. 3.5 and 3.6). However, when the steep part of the passive filling curve is reached, end-­ diastolic volume cannot increase further to any significant degree (Figs. 3.5 and 3.6). This state can be called the “volume limited” state of the RV.  The consequence is that any further rise in pulmonary arterial pressure, and thus rise in RV afterload, cannot be accommodated by the Starling mechanism. RV stroke volume will then decrease with any further rise in pulmonary artery pressure and so must cardiac output. Furthermore, the RV end-systolic pressure must rise, and so will RV end-diastolic pressure, which reduces the venous return of blood to the RV and lowers the steady-state cardiac output. When pulmonary artery pressure increases in the volume-­ limited state, stroke volume and cardiac output only can be maintained by an increase in heart rate or RV contractility. An important component of volume limitation is the interaction of the passive filling curve with RV Es-max. The maximum possible pressure that the ventricle can produce is where the RV Es-max line intersects the steep part of the passive filling curve because the ventricle cannot increase its volume to reach a higher pressure point on the line (Figs.  3.5 and 3.6). This explains the well-­ recognized clinical experience that an unconditioned RV cannot tolerate a pulmonary arterial systolic pressure much above 50–60 mmHg. This pressure is the upper limit of the intersection of the RV  Es-max and RV passive filling curve in most people. The flatter the RV Es-max line, the

lower the tolerable pulmonary artery  pressure. Also, the flatter the Es-max line, the less the benefit of an increase in systolic pressure on stroke volume with an increase in RV diastolic volume. Use of inotropes can produce some increase in RV Es-max in the short run and allow higher RV systolic pressure. Over time, there needs to be adaptation of RV muscle that increases the slope of RV Es-max which allows tolerance of higher RV systolic pressures (Redington et  al. 1988a; Faber et  al. 2006; Leeuwenburgh et  al. 2001; Redington 2006) (Fig. 3.9).

 ole of Pulmonary Arterial R Compliance Sunagawa and co-workers introduced the term “effective arterial elastance” (Ea) (Sunagawa et  al. 1983, 1985) to describe the ventricular-­ independent measure of arterial function which creates an outflow load on the ejecting LV, and this analysis has been applied to the interaction of the RV with the pulmonary circulation (Vonk-­ Noordegraaf et al. 2013; Vonk Noordegraaf et al. 2017). I will not go into the detail of all of the problems with this analysis, and I only make a few comments. The primary determinant of force generation of the RV is the slope of Es-max and this slope is independent of the pressure load on the ventricle. The term Ea refers to a “dynamic” elastance in that it has both resistive and elastic components. The primary determinant of flow though the pulmonary vasculature is the resistance draining it and the downstream pressure of the pulmonary circuit which is the left ventricular diastolic pressure when the mitral valve is intact or the left atrial pressure  when it is not;  in the presence of  non-West Zone III conditions it is  alveolar pressure. In modeling studies, it can be shown that even large changes in pulmonary arterial compliance do not have much effect on flow from the RV in the steady state, although compliance changes do affect the pulse pressure. There are major problems in making measurements of pulmonary arterial compliance in the intact human because to truly measure this static property, flow needs to be stopped to eliminate

3  Function of the Right Heart

33

a

b

100

100 BANDING

CONTROL

80 LV pressure (mmHg)

RV pressure (mmHg)

80 60 40 20

CONTROL

0 0

10

20 30 RV volume (ml)

40

50

BANDING

60 40 20 0 0

10

20 30 LV volume (ml)

40

50

Fig. 3.9  Pressure-volume loops of RV (left, a) and LV (right, b) during vena cava occlusion with and without pulmonary artery banding. Solid lines indicate control animals and dotted lines animals with pulmonary artery banding. The black solid line indicates end-systolic P-V

relationship (Ees). RV Es markedly increased with pulmonary banding but stroke volume fell. There was only a small increase in LV Es with pulmonary artery banding. (From Leeuwenburgh et al. (2001) Used with permission of The American Physiological Society)

changes due to volume loss through the downstream resistance and to identify the value of the downstream pressure. This latter factor is often dealt with by considering the value zero which creates a large error with small pressure difference across the pulmonary circuit. Another major limitation of the analysis is that it only considers what goes out of the heart but does not consider what is coming in and what likely is most important, the significance of RV volume limitation on the potential stroke volume (Fourie et  al. 1992; Vonk Noordegraaf et  al. 2017; Morimont et  al. 2008).

increased without an enlarged RV end-diastolic volume, there is a restrictive problem such as tamponade or a myocardial infiltrative process, rather than true mechanical failure of RV muscle shortening and pressure generation. In his seminal work on the law of the heart, Ernest Starling realized that even when the right heart stops pumping, RV end-diastolic pressure cannot be higher than the upstream MSFP, which normally is in the range of 8–10 mmHg (referenced to the mid-point of the right atrium) (Patterson and Starling 1914) (see Chap. 2). Thus, a central venous pressure greater than the normal values of MSFP only can occur generally  two ways;  either  volume  was given  by the clinician  or the  normal  volume intake orally was retained by decreased renal function. An initial adaptive process for the RV in the face of limited RV function also can be an increase in right atrial size. The large atrium then acts as a volume reservoir, which adds to the compliance of the venous system. This reduces venous pressures and allows for faster filling of the RV. It acts somewhat like the sinus venosus in fish and in the human fetal circulation. However, the consequence is dilatation of the tricuspid annulus and progressively increasing tricuspid

Pressure Load vs. Volume Load Decreased RV function often is considered in terms of either a volume load, which is defined by an increased RV diameter, or a pressure load, which relates to the potential of the RV to generate a pulmonary arterial pressure. To start, it should be clear that these cannot be separated because both an increase in pulmonary arterial pressure and a decrease in Es-max will increase end-diastolic volume and pressure. If cardiac output is decreased, and RV end-diastolic pressure is

34

regurgitation, which eventually makes the efficiency of the RV worse (Dos et al. 2005; Prieto et al. 1998).

Definitions of Right Ventricular Limitation, Dysfunction, Failure An issue that arises from this discussion is how should right ventricular dysfunction and right ventricular failure be defined? A simpler term first needs to be introduced  – right ventricular limitation (Table 3.3). This term indicates that the end-diastolic volume is at the steep part of the RV

Table 3.3  Definitions for limited output by the right ventricle 1. Right ventricular limitation  End-diastolic volume is on the steep part of the diastolic passive filling curve. This can be due to limitation by the pericardium, the myocardial cytoskeleton, or other mediastinal and thoracic structures.  End-diastolic volume cannot increase with a further increase in diastolic pressure. This does not necessarily indicate dysfunction and can be due to excess use of volume, inability of the heart to sufficiently adapt to an increasing volume return, or because of right ventricular dysfunction and a failure to adequately empty RV volume. 2. Right ventricular dysfunction  The RV is enlarged, a higher RV diastolic pressure and volume are required for a normal cardiac output, and the limit of right heart filling occurs at a value of cardiac output lower than normal.  This occurs because of a decrease in RV Es (contractile force of the ventricle). By this terminology, a low stroke output from the RV because of high pulmonary pressure would be limitation unless the load on the RV resulted in a subsequent decrease in the RV Es. 3. Right ventricular failure  This is similar to right ventricular dysfunction, but should be used to define a situation in which cardiac limitation also is present, the RV is enlarged, the cardiac index is 18

70-79

80-89

>80

100

Pressure

50

0

10 20 30 40

50 60 70 80

90 100 110 120

%

Decrease in circumference with age (%)

B •

Starts from higher diastolic vol but same SV

SV

A Volume

Fig. 8.10  Effect of age and initial volume on thoracic aortic elastance. Schematic pulse pressures are shown above a graph of hypothetical pressure–volume relationships in the aorta on the left and actual data of the aorta showing an increase in circumferential tension versus increases in aortic circumference in % from age 80 mmHg. (From Nakashima and Tanikawa 1971, with permission of SAGE Publications). The slope of lines in

the graphs is elastance or 1/compliance. With aging, the aorta becomes stiffer and shifts to the left. This results in increasing pulse pressure (top) for the same size SV (A) and from the same staring diastolic volume. The SV at B is the same size but starts from a higher initial volume diastolic volume and thus is on a steeper part of the aortic P-F curve. This results in a much larger pulse pressure for the same SV

120

increases the aortic volume at the end of diastole much the same way that a rapid breathing rate can lead to hyperinflation in the lungs, especially if arterial resistance is high (see Chap. 7, Heart Rate). Higher pressures also can produce a myogenic effect and increase critical closing pressures (Shrier and Magder 1993). The shape of the elastance curve also is important. Although true aortic elastance likely does not change acutely, the aorta becomes stiffer with age, with chronic hypertension, and potentially, with other chronic processes (Nakashima and Tanikawa 1971). A stiffer aorta produces a larger pulse pressure and the increased pressure leads to further increases in aortic elastance by the induction of compensatory transcriptional mechanisms in vascular walls. For all these reasons, pulse pressure is related to stroke volume, but the relationship is not direct.

Impedance The arterial waveform is determined by a pressure wave, a velocity wave, downstream resistance, the capacitance effect from the arterial elastance, and reflected waves (O’Rourke 1971, 1990). It thus is argued that the load on the heart is best evaluated by assessing the impedance to flow in the frequency domain instead of the time domain. This allows a decomposition of the various components of the waveform and is discussed in detail in Chap. 9. However, in the intensive care unit, we mostly are concerned with determinants of cardiac output, and the major determinant of this in the impedance analysis is the arterial resistance and the downstream critical closing pressure. The aortic elastance is a major determinant of the wave patterns, but elastance is a function of the make-up of the walls of the vessels and does not change rapidly in acute illness because changes are required in the composition of the vascular walls. In some studies, attempts have been made to assess arterial elastance under acute conditions, but what most likely is being observed is a change in downstream resistance or the critical closing pressures. Factors in the impedance analysis can affect the magnitude of

S. Magder

the pulse pressure, shape of the pulse, and the pattern of downstream transmission, but these only have a small impact on cardiac output and thus on O2 delivery, which is the primary concern for tissue perfusion. I thus have not included impedance as a major factor in this chapter (for discussion see Chap. 9). Its analysis also is not feasible in most critically ill patients.

 here Should Pressure W Be Measured and Which Pressure? Because of reflected waves, the pulse pressure increases the farther away the pulse is from the aortic valve. It thus has been argued that aortic pressure ideally should be measured at the aortic valve, and a tonometric technique has been developed to estimate this value. This central aortic pressure likely is important for understanding the physical forces that induce cardiac hypertrophy, but this pressure likely is not very significant determinant of regional flows. The assumptions required to estimate the central pressure in critically ill also likely are not valid in patients with distributive shock. More proximal sites for pressure measurement, such as the femoral artery or brachial artery, often are recommended for monitoring patients in shock. This is based on the potential for the more peripheral radial artery pressure to be damped. There also is data indicating that when these more proximal sites are used, less catecholamines are needed, especially in cardiac surgery patients (Lee et al. 2015; Dorman et al. 1998; Kim et  al. 2013). However, I have found that this often is just another tangible benefit. If it is known that proximal values are higher than a radial artery pressure, then why not just accept the lower radial artery value and save potential complications from the more proximal sites! The question also arises as to which pressure to use: systolic, mean, or diastolic. The mean is the most frequently used with indwelling catheters and today the mean is commonly obtained with automated cuff-compression techniques. Use of the mean avoids under or over-damping errors with indwelling catheters (Gardner 1981). It also is

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8  Physiological Aspects of Arterial Blood Pressure

thought that it better indicates organ perfusion pressures. Much of this has come from studies examining the ideal perfusion pressure for the kidney (Bersten and Holt 1995), and most from studies that were performed with some kind of controlled renal blood flow with a relatively nonpulsatile pump. However, most tissues in the body receive the largest percent of their flow during systole. Furthermore, the mean is very much affected by diastolic run-off and a low diastolic pressure lowers the mean. However, for the diastolic pressure to be disproportionately lower than expected based on the observed systolic pressure, flow must continue to be emptying from the aorta during diastole and thus some regions are still being perfused. I thus prefer the use of systolic pressure. To begin, this was the traditional measure in the emergency department or on the ward that triggered the call for the patient to be assessed when blood pressure was measured  by a sphygmomanometer. There are few caveats though. When using systolic pressure with an arterial cannula, it is essential to ensure that the waveform is valid. When I am concerned about the systolic pressure, especially if it is low, I compare it to a cuff pressure. For me the gold standard is not an oscillometer, which calculates the systolic pressure, but rather the auscultated pressure or palpated pressure with a cuff. I will often use the cuff pressure as my reference and use the arterial line to rapidly detect changes in the patient’s condition, which likely is the most important thing to know. In my experience, this leads to less catecholamine use than occurs with use of the mean pressure. However, there is no outcome data to know if this is valid. There is a school of thought that argues that diastolic pressure is an important value to monitor (Hamzaoui and Teboul 2019). The basis of the argument is that diastolic pressure is important for coronary blood flow, and therefore there is a minimal coronary perfusion pressure that needs to be maintained. On the other hand, coronary flow reserves are very large and can be maintained with very low pressures (Magder 2019). However, this assumes that there are no significant proximal coronary lesions which then require higher perfusion pressures. My approach is to not follow the diastolic pressure because this leads to higher

concentrations of vasopressors, which also have been found to be harmful. However, if signs of myocardial ischemia appear, arterial pressure likely should be increased. Of note, an increase in troponin is not a reliable measure of this. These issues were recently reviewed in an online debate (Hamzaoui and Teboul 2019; Magder 2019).

Conclusion I have not dealt in this chapter with the subject of what is the best clinical arterial target for blood pressure in the critically ill. This is because currently there is no satisfactory empiric data. An appropriate physiological prediction also cannot be made because of the complex determinants of what we measure. A crucial point is that pressure is not flow and what counts for tissue function is the flow that they receive. A central factor in the assessment of the ideal pressure is how flow is distributed based on arterial resistances feeding different organs. Unfortunately, this currently cannot be obtained in an intact person. It is likely that only carefully planned empiric studies which take into account the physiological principles linking pressure and flow will allow recommendations for best targets for blood pressure. These likely also will have to be specific for different patho-physiologies because best pressure targets will be different for haemorrhagic shock, septic shock, and cardiogenic shock. Even with general recommendations, target values will likely still need to be individualized based on the individual patient’s needs. In difficult cases, an estimate of cardiac output as well as tissue perfusion should help individualize care.

References Bellamy RF. Diastolic coronary artery pressure-flow relations in the dog. Circ Res. 1978;43(1):92–101. Berne RM. Metabolic regulation of blood flow. Circ Res. 1964a;15(Suppl):261–8. Berne RM.  Regulation of coronary blood flow. Physiol Rev. 1964b;44:1–29. Bersten AD, Holt AW.  Vasoactive drugs and the importance of renal perfusion pressure. New Horiz. 1995;3(4):650–61.

122 Burton AC.  Total fluid energy, gravitational potential energy, effects of posture. Physiology and biophysics of the circulation: an introductory text. Chicago: Year Book Medical Publishers Incorporated; 1965a. p. 95–111. Burton AC. Kinetic energy in the circulation. Physiology and biophysics of the circulation: an introductory text. Chicago: Year Book Medical Pulblishers Incorporated; 1965b. p. 102–12. Dole WP, Alexander GM, Campbell AB, Hixson EL, Bishop VS.  Interpretation and physiological significance of diastolic coronary artery pressure-flow relationships in the canine coronary bed. Circ Res. 1984;55(2):215–26. Dorman T, Breslow MJ, Lipsett PA, Rosenberg JM, Balser JR, Almog Y, et al. Radial artery pressure monitoring underestimates central arterial pressure during vasopressor therapy in critically ill surgical patients. Crit Care Med. 1998;26(10):1646–9. Gardner RM.  Direct blood pressure measurement-dynamic response requirements. Anesthesiology. 1981;54(3):227–36. Gould KL. Does coronary flow trump coronary anatomy? JACC Cardiovasc Imaging. 2009;2(8):1009–23. Gould KL, Lipscomb K. Effects of coronary stenoses on coronary flow reserve and resistance. Am J Cardiol. 1974;34(1):48–55. Gould KL, Lipscomb K, Calvert C.  Compensatory changes of the distal coronary vascular bed during progressive coronary constriction. Circulation. 1975;51(6):1085–94. Guyton AC, Carrier O Jr, Walker JR. Evidence for tissue oxygen demands as the major factor causing autoregulation. Circ Res. 1964;15(Suppl):60–9. Hainsworth R, Karim F, McGregor KH, Rankin AJ.  Effects of stimulation of aortic chemoreceptors on abdominal vascular resistance and capacitance in anaesthetized dogs. J Physiol. 1983;334:421–31. Hamzaoui O, Teboul JL.  Importance of diastolic arterial pressure in septic shock: PRO.  J Crit Care. 2019;51:238–40. Hoffman JI.  Maximal coronary flow and the concept of coronary vascular reserve. Circulation. 1984;70(2):153–9. Kim WY, Jun JH, Huh JW, Hong SB, Lim CM, Koh Y.  Radial to femoral arterial blood pressure differences in septic shock patients receiving high-dose norepinephrine therapy. Shock. 2013;40(6):527–31. Kloche FJ, Weinstein IR, Klocke JF, Ellis AK, Kraus DR, Mates RE, et al. Zero-flow pressures and pressure-flow relationships during single long diastoles in the canine coronary bed before and during maximum vasodilation. J Clin Invest. 1981;68:970–80. Lee M, Weinberg L, Pearce B, Scurrah N, Story DA, Pillai P, et al. Agreement between radial and femoral arterial blood pressure measurements during orthotopic liver transplantation. Crit Care Resusc. 2015;17(2):101–7. Magder SA.  Pressure-flow relations of diaphragm and vital organs with nitroprusside-induced vasodilation. J Appl Physiol. 1986;61:409–16.

S. Magder Magder S.  Starling resistor versus compliance. Which explains the zero-flow pressure of a dynamic arterial pressure-flow relation? Circ Res. 1990;67:209–20. Magder S. Phenylephrine and tangible bias. Anesth Analg. 2011;113(2):211–3. Magder SA. The highs and lows of blood pressure: toward meaningful clinical targets in patients with shock. Crit Care Med. 2014;42(5):1241–51. Magder S.  The meaning of blood pressure. Crit Care. 2018;22(1):257. Magder S.  Diastolic pressure should be used to guide management of patients in shock: PRO.  J Crit Care. 2019;51:241–3. Nakashima T, Tanikawa J. A study of human aortic distensibility with relation to atherosclerosis and aging. Angiology. 1971;22(8):477–90. O’Rourke MF.  The arterial pulse in health and disease. Am Heart J. 1971;82(5):687–702. O’Rourke RA. The measurement of systemic blood pressure; normal and abnormal pulsations of the arteries and veins. In: Hurst JW, Schlant RC, Rackley CE, Sonnenblick EH, Kass Wenger N, editors. The heart. 7th ed. New York: McGraw-Hill; 1990. p. 158–60. Patterson SW, Starling EH.  On the mechanical factors which determine the output of the ventricles. J Physiol. 1914;48(5):357–79. Permutt S, Riley S.  Hemodynamics of collapsible vessels with tone: the vascular waterfall. J Appl Physiol. 1963;18(5):924–32. Ross J Jr. Dynamics of the peripheral circulation. In: West J, editor. Best and Taylor's physiological basis of medical practice. 11th ed. London/Baltimore: Williams and Wilkins; 1985. p. 119–31. Shrier I, Magder S.  Response of arterial resistance and critical closing pressure to change in perfusion pressure in canine hindlimb. Am J Physiol. 1993;265:H1939–H45. Shrier I, Magder S. The effects of nifedipine on the vascular waterfall and arterial resistance in the canine hindlimb. Am J Physiol. 1995a;268:H372–H6. Shrier I, Magder S.  N G -nitro-L-arginine and phenylephrine have similar effects on the vascular waterfall in the canine hindlimb. Am J Physiol. 1995b;78(2):478–82. Shrier I, Magder S. Effects of adenosine on the pressure-­ flow relationships in an in vitro model of compartment syndrome. J Appl Physiol. 1997;82(3):755–9. Shrier I, Hussain SNA, Magder S.  Carotid sinus stimulation influences both arterial resistance and critical closing pressure of the isolated hindlimb vascular bed. Clin Invest Med. 1991;14(4):A13. Sylvester JL, Traystman RJ, Permutt S. Effects of hypoxia on the closing pressure of the canine systemic arterial circulation. Circ Res. 1981;49:980–7. Thiele RH, Nemergut EC, Lynch C III. The physiologic implications of isolated alpha 1 adrenergic stimulation. Anesth Analg. 2011a;113(2):284–96. Thiele RH, Nemergut EC, Lynch C III. The clinical implications of isolated alpha 1 adrenergic stimulation. Anesth Analg. 2011b;113(2):297–304.

9

Pulsatile Haemodynamics and Arterial Impedance David Fitchett and Michael F. O’Rourke

The arterial system acts as a conduit to deliver oxygenated blood to the tissues, and as a compliant cushion to dampen pressure and flow oscillations and convert the intermittent flow input from the left ventricle into a near-continuous flow output at tissue level. The intermittent ejection of blood from the left ventricle into the aorta results in pulsatile pressure and flow throughout the arterial system. The time sequence and magnitude of left ventricular ejection is determined by the coupling between the hydraulic load imposed by the arterial system and the contractile status of the left ventricle. The instantaneous value of the pulsatile pressure and flow in the arterial system is, in turn, determined by both the left ventricular ejection and the properties of the arterial system. In this chapter, we will consider the properties and models of the arterial system that provide a quantitative description of the observed physiology. The chapter will discuss how changes to both the arterial system and cardiac function influence pressure and flow waves measured at various locations in the arterial tree, to better understand problems of left ventricular/arterial D. Fitchett (*) Department of Cardiology, St Michael’s Hospital, University of Toronto, Toronto, ON, Canada M. F. O’Rourke St. Vincent’s Hospital, Department of Cardiology, St Vincent’s Clinic, Sydney, NSW, Australia e-mail: [email protected]

interaction which occur with ageing and in disease. The elastic properties of the arterial system afford a buffer or cushion, to limit the rise in pressure that occurs as the heart ejects the stroke volume into the aorta. Stephen Hales in the eighteenth century likened the arterial system to the air reservoir of a fire engine which changed the intermittent input of water from the hand pump, to a steady flow at the nozzle. (Fig.  9.1)  – now termed the Windkessel model. While the Windkessel model of the arterial system provides a simple quantitative description of pressure measurements resulting from intermittent flow, it fails to reproduce observed changes in arterial pressure and flow waves throughout the arterial network. In particular, pressure and flow waves travel at a finite speed in the arterial tree, and are reflected at points of discontinuity, particularly at the entry to high resistance, low-calibre arterioles (as originally suggested by William Harvey 1957). Figure 9.2 shows pressure in the ascending aorta of a rabbit under three conditions, which are also seen in humans when (1) blood pressure is high, the aorta is stiff and the rate at which the pressure wave travels (the pulse wave velocity) is high, (2) normal conditions where reflection from points of discontinuity is apparent as a prominent diastolic wave, and (3) an arteriolar dilating drug has decreased wave reflection (Wetterer 1954; O’Rourke 1970a). These pressure and flow waves

© Springer Nature Switzerland AG 2021 S. Magder et al. (eds.), Cardiopulmonary Monitoring, https://doi.org/10.1007/978-3-030-73387-2_9

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124 Windkessel (cushion)

Fire hose (conduit)

Fig. 9.1  The arterial system was compared to the fire engine of the eighteenth century by Stephen Hales. The ejection of water into the dome results in compression of the air – storing elastic energy which is returned between

pump strokes converting the intermittent flow from the pump into a continuous flow delivered to the fire hose. (Reprinted from Hales 1769)

150

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Fig. 9.2  Changes in ascending aortic pressure and flow in a rabbit (1) with severe hypertension induced with norepinephrine, (2) Control state (3) with hypotension induced with pilocarpine. Under normal resting states there is a prominent diastolic wave due to wave reflec-

tions. With severe hypertension the reflected wave returns earlier enhancing systolic pressure. During hypotension reflected waves are reduced. (Data from Wetterer (1954). Reproduced from O’Rourke (1970a) with permission of Wolters Kluwer Health, Inc.)

were recorded in the same animal just minutes apart. It is clear that the Windkessel model is inappropriate when blood pressure, heart rate, vasomotor tone, and cardiac properties are changing. The Windkessel model is of no value where it is needed most – in humans monitored during surgery and in intensive care units.

Arterial Wall Properties and Pulsatile Haemodynamics Increased arterial stiffness and an increase of peripheral resistance are the hemodynamic determinants of hypertension (Ting et  al. 1995). Hypertension increases the load on the heart and

9  Pulsatile Haemodynamics and Arterial Impedance

stress on the arteries, and results in left v­ entricular hypertrophy, an increased risk of stroke, heart failure, renal failure and dementia. The arterial wall shows viscoelastic properties, which can be quantified in  vivo. Arterial compliance is the change in volume for a unit change in pressure (ΔV/ΔP). Distensibility is the compliance normalized for the initial volume of the arterial segment (ΔV/ΔP)/V0. Peterson’s elastic modulus (Ep) is the distensibility described in terms of arterial diameter: Ep = Do (Ps − Pd) /(Ds − Dd): where Ps and Pd are the systolic and diastolic arterial pressures, and Ds and Dd the systolic and diastolic diameters of the artery. The arterial stiffness or elastance is the inverse of arterial compliance or distensibility. Calculation of local arterial Ep is readily determined by vascular ultrasound measurements of systolic and diastolic arterial diameter (Baltgaile 2012) with simultaneous pressure measurements and is applicable to measurements in the aorta (ascending, arch, and descending), carotid, brachial, radial, iliac, and femoral arteries. Ideally, pressure changes should be measured at the same location as the dimension change, or using calculated central pressure determined from peripheral pressure as described below. Arterial distensibility decreases in the axial direction (increasing when measured at a distance further from the heart) and as mean arterial pressure increases. At lower levels of distension, the elastic properties of the arterial wall result from extension of the arterial elastin, yet with greater stretch the less distensile collagen fibres take the load. Consequently, comparative measurements of elasticity, distensibility, or pulse wave velocity should be made at the same distending pressure. Pulse wave velocity (Co) in an elastic tube is related to the arterial elastance by the Moens-­ Korteweg or Bramwell-Hill equations:

Co  V  P / V  or Co 

 Eh / 2r  .

where E is Young’s modulus of elasticity of the artery, h wall thickness, V arterial volume, ΔP pulse pressure, and ΔV change in arterial volume. Pulse wave velocity should be distinguished from

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blood flow velocity: Pulse wave velocity measures the speed of transfer of energy through the arterial wall (velocity range 4–12 m/s), whereas blood flow velocity measures the transfer of mass along the blood column (velocity range 10–100 cm/s). Using measurements of the change of arterial radius (dR) between diastole and systole in an arterial segment, pulse pressure (dP), and blood density (ρ), the wave velocity can be calculated:

Co  RdP /  2 dR .

Arterial pulse wave velocity is usually measured using external pressure transducers, from the time difference between the foot of the pressure wave over long arterial segments (e.g. carotid (as a surrogate for central pressure wave) to femoral) (Nichols and O’Rourke 1998). Local arterial pulse wave velocity can be determined using either ultrasound or MRI measurements of arterial flow and dimensions, in the aorta and peripheral arteries. From the central aorta to the conduit arteries in the legs, the arteries become far more numerous, smaller, and have less elastin and more smooth muscle: consequently, pulse wave velocity increases. These changes are most marked in the thoracic and abdominal aorta, and least marked in the upper limb vessels. Increasing arterial pressure reduces distensibility of the arteries, resulting in an increase in pulse wave velocity. With ageing and hypertension, arteries become less distensile, consequently pulse wave velocity increases. Current indices of arterial distensibility are non-linear. We seek indices which have linear relationships or where non-linearities are small, or can be controlled or allowed for. Even peripheral resistance (Mean pressure / Mean flow) is non-linear. Up until 1960, the approach to pulsatile haemodynamics in arteries concentrated on measurements of arterial stiffness. Anatomists however pointed out how the dimensions of animals could alter wave transmission and result in reflected waves as well as waves traveling forward by increasing the number of peripheral

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Vascular resistance

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Σ Lower body Reflection site

Σ Upper body Reflection site Heart

Fig. 9.3  Asymmetric T model of the arterial circulation showing the location and very abrupt change of resistance over a very short distance. (Resistance is shown on a logarithmic scale). These marked impedance mismatches provide multiple reflecting sites that can be lumped into upper and lower body sites. In this model, the lower body is the major reflection site with less intense reflections

returning from the upper body as a consequence of the low resistance cerebral vasculature. The measured pressure and flow waves result from the interaction of forward and reflected waves from both sites. (Reproduced based on diagram from O’Rourke et al. (2018) with permission of Oxford University Press)

branches, their distance from the heart and their cross-sectional area (Milnor 1979). Moreover, such reflected waves have the potential to interfere with outgoing pulse wave in the proximal arteries augmenting systolic pressure and reducing mean diastolic pressure on the pulse wave velocity. The nature and degree of such augmentation will depend on the pulse wave velocity. Of great significance is the recent recognition of the importance of the location of the peripheral resistance vessels being just a few millimetres from the low resistance conduit arteries (O’Rourke et al. 2018). This confirms the work of Hamilton, Dow (Hamilton 1939), Remington (Remington and Wood 1956), and others that the peripheral resistance is the site of strong wave reflection (Fig. 9.3).

groups on his return from London to Sydney. Up until 1960, Taylor sought information on optimal function of the arterial tree as based upon optimal distensibility (Taylor 1967). He progressed to anatomical as well as physical preparations as required for a comprehensive study. Taylor pressed the search for answers through determination of vascular impedance in animals of different size and shape, and in humans; Michael O’Rourke was his graduate student with training in anaesthesiology and intensive care. The pressure developed by the contracting heart is determined by the force of contraction of the heart muscle and the external opposition to ventricular outflow. Characterization of the external opposition to left ventricular ejection needs to take into account the major components of the arterial load: (1) resistance, (2) blood viscosity, (3) arterial wall visco-elastance, (4) inertia, and (5) wave reflection. The implications of pulsatile haemodynamics on left ventricular/arterial coupling will be discussed in a later section of this chapter. Arterial resistance is determined by blood viscosity and is inversely proportional to the fourth power of the radius of the vessel.

Vascular Impedance A large jump in the field took place in the 1960s with the introduction of frequency domain analysis of the arterial pulse by an English group of McDonald, Womersley, and Taylor, and a US group at the NIH in Bethesda led by Donald Fry. Michael Taylor liaised with the UK and US

9  Pulsatile Haemodynamics and Arterial Impedance

R  8 L /  r 4

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 viscosity, L vessel length, r radius

Consequently, the most important component of peripheral arterial resistance is at the level of the smallest vessels or arterioles. If flow from the heart was constant, the steady pressure (Pmean) generated would depend only on arterial resistance: Pmean = R Qmean where Pmean and Qmean are the mean pressure and flow, respectively. However, the intermittent nature of left ventricular ejection presents a more complex case. In addition to the peripheral resistance, in the intermittent case, there is the possibility of reflected waves from more distant points in the vascular tree as well as the elastic nature of the arteries (which distend and recoil in systole and diastole respectively, resulting in a time-dependent elastic contribution to pressure). The pulsatile hydraulic load on the heart will reflect contributions from all of these. The pulsatile left ventricular hydraulic load on the heart could be described in either the time or frequency domains. Time domain assessments of the pulsatile arterial load include measures of total arterial compliance (e.g. stroke volume/ pulse pressure) or effective arterial elastance (end systolic pressure / stroke volume). However, frequency domain assessment of the arterial load by the aortic input impedance provides the best description of the components of steady state and pulsatile arterial load. Impedance is a frequency-dependent measure of opposition to pulsatile flow. Resistance is the opposition to steady or non-oscillatory flow and is the impedance at zero frequency. Input impedance is determined by the properties of the arterial system, which include peripheral arteriolar resistance, the viscoelastic properties of the arteries, inertial forces associated with changing flow, the viscosity of the blood and the impact of reflected waves, as well as the size and shape of the animal. Characteristic Impedance (Zc)  It is the relationship between pulsatile pressure and pulsatile flow (ΔP/ΔV) measured at the same site in the absence of reflected waves. Zc is determined by the physical properties of the arterial system such as the elastic modulus of the artery and the

inverse of the cross-sectional area. Consequently, Zc is linearly related to the pulse wave velocity (Co) and is also directly related to blood density.

Z c   co /  r 2

Wave velocity (Co), in turn, is directly related to ΔP/ΔV by the “Water Hammer” equation Co  P /  V where ΔV is the change in blood flow velocity, ΔP the change in pressure, and ρ is blood density. Zc can be estimated from the change in pressure occurring simultaneously with peak flow.

Z c  PQ / Q   Pi  Pd  / peak flow



where Pi is the pressure at the inflection point of aortic pressure where peak flow occurs (see Fig. 9.4), Pd is end diastolic pressure. This estimate assumes that aortic pressure up to this time point is not changed by wave reflections. Zc may also be obtained by Fourier analysis of arterial pressure and flow (as described below) by averaging the moduli of impedance at frequencies above the first minimum. There is good correlation between the peak flow method and the results obtained by Fourier analysis (Dujardin and Stone 1981). When Zc is determined from flow change in volume units of cm3/s, Zc has the same units as arterial resistance (dyne-s/cm5). However, characteristic impedance is not a true resistance and can only be considered in the context of oscillatory phenomenon. Zc is best expressed in terms of velocity (cm/sec) as dyne.s.cm−3. Zc numerically (in volume units) is about 5–7% of peripheral vascular resistance. Measurements of Zc in man show increasing Zc (in velocity units) with age, hypertension, and in patients with heart failure, as one would expect, on account of the r­ elationship of Zc with pulse wave velocity and arterial stiffness. Input Impedance (ZI)  It describes the actual relationship between observed pressure and flow,

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R

Impedance modulus

Fig. 9.4 Hypothetical aortic input impedance spectrum. The first minimum of the impedance modulus occurs close to the frequency where the phase crosses zero. The characteristic impedance (Zc) is the average of the impedance moduli beyond the first minimum. A measure of wave reflection (RF) is the amplitude of the impedance moduli variation above the first minimum. (From Yin 1987. Reproduced with permission of Springer Nature)

RF

Zo

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in the frequency domain. The measured pressure and flow waves are the summation of incident and reflected backward traveling waves. Thus, input impedance (Zi)

Z I   Pf  Pb  /  Qf  Qb 



in contrast to Zc reflects the effect of reflected waves in addition to the physical properties of the arterial system such as vessel radius and viscoelastic properties. In the ascending aorta, the aortic input impedance describes the actual arterial hydraulic load on the ejecting left ventricle. Fourier transform analysis of pressure and flow recordings taken in the arterial system yields a Fourier series – a weighted sum of sine waves at specified frequencies which would yield the observed pressure and flow recordings. These

frequencies are harmonics – multiples of the fundamental frequency (60/HR where HR is heart rate in beats/minute). The observed waveforms can be reconstructed with increased accuracy by including more harmonics. Zi is determined for each individual harmonic (h) as an amplitude (modulus) Zi(h) and a phase angle (θ(h)). The phase angle (θ(h)) is the phase difference between the pressure (β) and flow (ϕ) harmonic and is positive when the flow harmonic leads the pressure harmonic.

Zi h  P h / Q h

 h    



An aortic input impedance spectrum is a graphical representation of the phase angle and impedance moduli for each frequency (Fig. 9.4).

9  Pulsatile Haemodynamics and Arterial Impedance

Impedance values decline from a high value at 0  Hz (the peripheral vascular resistance) to a minimum at approximately 3.5 Hz. At about this frequency, the phase angle crosses zero. A negative phase angle indicates that the flow harmonic leads the pressure harmonic. The impedance modulus over a range of frequencies oscillates around a mean value due to the impact of wave reflections. The magnitude of the oscillations of impedance moduli approximates the reflection index. The mean value of input impedance at frequencies above approximately 3  Hz is taken to represent the characteristic impedance (Zc). The frequency at which the first minimum of impedance modulus and the zero crossing point of the phase angle allows calculation of the wavelength of the travelling wave and, consequently, the distance to the major site for wave reflection. The reflection site is at a quarter of a wavelength (λ/4) where λ = Co/f, Co is the pulse wave velocity and f the frequency of the first impedance modulus minimum. The distance to the effective reflecting site is d = Co/4f. In normal human studies, with an impedance minimum at 3.5 Hz and an average aortic pulse wave velocity of 750 cm/ sec, the distance from the ascending aorta to the effective reflecting site is 54 cm. The significance of the effect of wave reflections on the measured pressure depends upon their magnitude and the phase difference between reflected and incident waves, which in turn depends upon both the distance from the reflection site and the wave velocity. If the phase difference is 0°, the reflected wave will increase the net pressure (constructive interference), whereas if the phase difference is 180° the reflected wave will decrease the measured pressure (destructive interference). The validity of impedance as a measure of the relationship between pressure and flow and the application to transmission line theory depends upon the linearity of the relationship between pressure and flow. For example, in the Fourier analysis, each harmonic of pressure and flow is considered to be uniquely related and not dependent upon other frequency components, that is, there is no harmonic interaction. Theoretical and experimental studies have demonstrated that any

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non-linearities between pressure and flow are surprisingly small and can be neglected as a first-­ order approximation.

Arterial Wave Reflections The arterial system is a network of distensile tubes with multiple branches. The arterial pressure wave is transmitted along the conduit arteries at the local pulse wave velocity. On encountering the branching points and the arteriolar network, a proportion of the wave energy is reflected back. Such reflecting points are termed points of impedance mismatch. As above, the reflected backward travelling wave interacts with the forward wave with the resulting observed wave dependent on both the magnitude and the phase difference of the forward and backward waves. Using parameters acquired from impedance data, the arterial pressure and flow waves can be analysed into forward and reflected waves (Fig. 9.5). Reflected waves are seen in arterial pressure recordings, especially in animals and young people in whom the amplification is more pronounced and the wave velocity slower such that reflected waves return later and after the initial impulse (Fig. 9.6). Wave reflection is responsible for the increase in systolic pressure observed as the pressure wave travels from the central aorta to the periphery. Over the same distance, diastolic pressure falls with a consequent greater fluctuation of pressure around a slightly lower mean pressure (by just 1–2 mmHg) (Pauca et al. 1992). Wave reflection can be quantified by the magnitude of the variation of the input impedance at frequencies above the first minimum. An estimate of wave reflection can also be made by calculation of a reflection coefficient (Γ): Γ = (ZT − ZC)/(ZT + ZC), where ZT is the peripheral resistance and ZC is characteristic impedance. However, Γ is frequency dependent and falls from a value of approximately 0.8 at the fundamental heart rate frequency to very low levels above the first impedance maximum. Wave reflection can be modified by physiological, pathological, and pharmacological influ-

D. Fitchett and M. F. O’Rourke

130 Fig. 9.5 Measured aortic pressure is analysed into forward and backward (reflected) waves. The reflected wave results in the late systolic peak of pressure. (From Yin 1987. Reproduced with permission of Springer Nature)

P reflected

Q reflected

P measured P forward Q forward Q measured

Pm = Pf + Pr

a

Qm = Qf + Qr

b

Fig. 9.6  The arterial system is modelled as an asymmetric T with reflections originating in the upper and lower parts of the body. (a) In a young subject or in animals, systolic and pulse pressure increase as the pressure wave is transmitted from the central aorta to the periphery due to the impact of wave reflections. (b) In an older human

subject with stiffened arteries wave reflections return earlier augmenting late systolic pressure and a lesser effect on pressure amplification between central and peripheral pressures. (From Wave Travel and Reflection in the Arterial System. O’Rourke 1971. Modified with permission of Elsevier Science & Technology Journals)

9  Pulsatile Haemodynamics and Arterial Impedance

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Changes of Waveforms in the Arterial System

ences. Ageing and arterial disease are associated with an increase in late systolic aortic pressure due to increased wave reflection arriving early during systole as consequent to the increased pulse wave velocity. Laskey and Kussmaul (1987) show that exercise reduces the magnitude of the reflected wave. The reduction of the late systolic peak of aortic pressure observed during a Valsalva maneuver is associated with reduced fluctuation of the impedance spectrum indicating a reduction of wave reflections (Murgo et al. 1981). Vasodilators such as nitroprusside reduce mean arterial pressure and peripheral vascular resistance (Merillon et al. 1982). As a result of the reduction of pulse wave velocity, the input impedance curve is shifted to the left, suggesting a delay in the timing of wave reflections. In addition, the amplitude of the impedance modulus of the first two harmonics is reduced, indicating an overall reduction of wave reflections. In contrast, nitroglycerin reduces late systolic pressure in the ascending aorta as well as the fluctuations of the modulus of the impedance spectrum with little effect on peripheral arterial resistance or change in the frequency of the modulus minima (Fitchett et al. 1988a; Yaginuma et al. 1986). These observations indicate that nitroglycerin reduces wave reflection without changing peripheral arteriolar resistance. 137

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Differences between peripheral and central arterial pressures have important practical consequences. Pressure measured in the radial artery differs from both central aortic and intra-cranial arterial pressure. Central aortic pressure is both the pressure load encountered by the ejecting left ventricle and the driving pressure for coronary perfusion in diastole, while carotid arterial pressure is the cerebral perfusion pressure. Differences in central and peripheral pressures become accentuated during procedures that cause hypotension as shown in Fig. 9.7. Radial arterial pressure is frequently used for patient monitoring in critically ill patients. With vasoconstriction in patients with shock, wave reflections can be increased, potentially increasing pressure amplification and exaggerating the difference between central aortic and radial arterial systolic pressure (Fig. 9.8). However, radial arterial pressure can underestimate central aortic pressure in these patients possibly due to peripheral arterial vasoconstriction. Consequently, measurement of peripheral arterial pressure by radial arterial pressure monitoring provides only a limited assessment of aortic systolic pressure. There is a need for fur-

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Fig. 9.7  Pressure waves simultaneously recorded from the radial artery (dashed line) and from the abdominal aorta (solid line) during the course of a hypotensive reaction to rapid intra-aortic injection of isotonic electrolyte

solution (numbers represent mmHg). (From Remington and Wood 1956. Reproduced with permission of American Physiological Society)

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Fig. 9.8  Radial and aortic pressures in a patient with hypotension, vasoconstriction, and an obstructed carotid artery, showing the exaggerated diastolic pressure waves in the radial arterial pressure due to delayed wave reflection. Note the substantial difference between peak radial and aortic pressure indicating that radial arterial systolic pressure can overestimate central systolic pressure. (From O’Rourke 1970b. Reproduced with permission of Oxford University Press)

Aorta

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ther studies of pressure wave transmission between the central aorta and radial artery in patients with shock, to better understand the changes and clinical implications. Anaesthetists and intensivists are well aware of such problems which can also damp the pressure waveform. The appropriate action is to replace the radial cannula with a short catheter and advance it to the subclavian artery. Vasodilators such as nitroglycerine reduce ascending aortic systolic pressure more than is apparent from changes of radial arterial pressure (Fig. 9.9) due to the reduction of reflected waves as indicated by the changes of aortic input impedance without any significant reduction in systemic vascular resistance (Fig. 9.10).

Non-invasive Assessment of Pulsatile Haemodynamics and Central Aortic Pressure Analysis of the radial arterial pressure contour can provide information about the transmission of the pressure pulse from the aorta to the periph-

ery. The radial arterial pulse characteristically has an early systolic peak followed by a late systolic shoulder and another wave after the dicrotic notch as shown in Fig. 9.9. The relationship between recordings of aortic and radial arterial pressure waves and the moduli and phase of the individual Fourier harmonics – the output of any given input – can be expressed as a transfer function for each harmonic. In the upper limb, the transfer function is remarkably constant over a wide range of ages (Karananoglu et  al. 1997) and after the administration of vasodilators such as nitroglycerine (O’Rourke ­ et  al. 1990). Consequently, the same transfer function can be used to generate central aortic pressure from recordings of the radial pressure wave in a wide range of conditions. The radial arterial pressure wave can be reliably recorded non-invasively using applanation tonometry (Drzewiecki et al. 1983). By applying the transfer factor to the radial pressure harmonics, central aortic pressure may thus be synthesized from recordings of peripheral arterial pressure. Using this technique, it is possible to determine the impact of therapy (e.g. antihyper-

9  Pulsatile Haemodynamics and Arterial Impedance

133

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Nitroglycerin

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Fig. 9.9  Pressure waves recorded in the ascending aorta and brachial artery under control conditions (left) and after 0.3 mg sublingual nitroglycerine (right) in a human

Impedance (dyn/s/cm5

190 180 170 160 150 140 130 120 110 100 90 80 70 60 50 40 30 0

Phase (radians)

Fig. 9.10  The effect of nitroglycerin 15 μg on the aortic input impedance modulus and phase, before and during nitroglycerine infusion. Systemic vascular resistance was unchanged. There was a significant reduction in the modulus of impedance of the first harmonic and less oscillation of the modulus and phase angle spectrum indicating nitroglycerine reduces peak systolic pressure consequent to a reduction of reflections with no reduction of systemic vascular resistance. (From Fitchett et al. 1988b. Reproduced with permission of Oxford University Press)

adult. X, height the pressure pulse would be without reflection (R). (From Kelly et  al. 1990, Fig.  9.2. Reproduced with permission of Oxford University Press)

2 1 0 –1

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D. Fitchett and M. F. O’Rourke

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Fig. 9.11  Tonometric recordings of radial artery pressure with synthesized aortic pressure using the transfer function before and after the administration of s/l nitroglycerine. Similar pressure pulse changes in both the radial

artery and aorta are observed to those measured with intra- arterial measurements as shown in Fig. 9.9. (From O’Rourke et  al. 1993. Modified with permission of Informa UK Limited through PLSclear)

tensive and heart failure medications) on central BP and hence the impact on left ventricular loading (Fig. 9.11).



Pulsatile Haemodynamics and Ventricular Arterial Coupling The external work performed by the heart in a cardiac cycle (or stroke work) is the integral of the pressure and flow over a cardiac cycle:

W  stroke    PQdt



Total external work has two components: (1) steady flow power (Ws). Ws = Pm ∙ Qm, where Pm is the mean arterial pressure and Qm the mean ascending aortic flow and (2) oscillatory power (Wo) Wo = ½ ∑ (Qn)2 Zn cos θn. Where Qn is the nth harmonic of flow and Zn the nth harmonic of impedance, and θn the phase angle of impedance for the nth harmonic. The

9  Pulsatile Haemodynamics and Arterial Impedance

total hydraulic power is the sum of Ws and Wo. In the left ventricle, the oscillatory contribution to total power output is less than 20% of the total power (Nichols et al. 1977). The hydraulic power of blood flow (work/ time) in the ascending aorta is dependent upon (1) the ability of the left ventricle to perform external work and (2) the hydraulic load of the arterial system. Consequently, the hydraulic power generated is dependent upon both ventricular performance and the impedance to flow. The steady state achieved defines the coupling of the ventricle to the arterial system. As shown previously, ageing, the development of atherosclerosis, and hypertension decrease arterial distensibility and increase wave reflections which arrive back early in the ascending aorta during left ventricular ejection and boost systolic pressure. Consequently, both total and oscillatory power are increased, with a larger proportion of oscillatory power in the individuals with atherosclerosis and hypertension. (oscillatory/total power normal group 13 ± 1%, atherosclerosis and hypertension 19 ± 2.5%) (Nichols et al. 1977). In the oldest group with the highest characteristic impedance, the pulsatile component was 26% of total power. Exercise increases both steady flow and oscillatory power. In dogs, there was a modest increase in the proportion of oscillatory to total power during exercise 19–22% (Unpublished data presented in (Milnor 1989)). In man especially with exercise-induced systolic hypertension, it is likely that there is a greater proportion of oscillatory power/total power during exercise.

Conclusions 1. The arterial system is more than a conduit to deliver oxygenated blood to the organs. It acts both as a buffer to limit the rise of pressure and to deliver a more constant flow to the tissues. 2. The hydraulic load encountered by the left ventricle is the consequence of arteriolar resistance (constant flow) and both the elastance of the arterial system and the impact of reflected waves (pulsatile flow).

135

3. The arterial system behaves as a distributed network of distensible vessels with wave reflections originating from impedance mismatches that occur mainly at the arterial/arteriolar interface. 4. Characterization of the arterial load is best expressed in the frequency domain as the aortic input impedance. It allows assessment of both steady flow impedance (arteriolar resistance) and pulsatile impedance as determined by the characteristic impedance and the impact of wave reflections. 5. Arterial pressure and flow waves change during their transit through the arterial system due to their interaction with reflected waves. Consequently, pressures measured distally in a limb (e.g. radial arterial pressure) may not accurately reflect central aortic pressure: the pressure determining left ventricular afterload, coronary and cerebrovascular perfusion. Central aortic pressures can be synthesized from recordings of radial artery pressure waveforms. For further in-depth reading, see McDonald’s Blood Flow in Arteries. Theoretical, Experimental and Clinical Principles, 6th Edition, 2011. WW Nichols, MJ O’Rourke, and C Vlachopoulos. CRC Press, Boca Raton, USA. Hemodynamics, WR Milnor, 2nd edition 1989. Williams and Wilkins, Baltimore.

References Baltgaile G.  Arterial wall dynamics. Perspect Med. 2012;1:146–51. Drzewiecki GM, Melbin J, Noordergraaf A.  Arterial tonometry: review and analysis. J Biomech. 1983;16:141–52. Dujardin JP, Stone DN.  Characteristic impedance of the proximal aorta determined in the time and frequency domain: a comparison. Med Biol Eng Comput. 1981;19:565–8. Fitchett DH, Simkus G, Genest J, Marpole D, Beaudry P. Effect of nitroglycerin on left ventricular hydraulic load. Can J Cardiol. 1988a;4:72–5. Fitchett DH, et al. Reflected pressure waves in the ascending aorta: effect of glyceryl trinitrate. Cardiovasc Res. 1988b;22:494–500.

136 Hales S S Statical essays. Printed for W. Innys and R. Manby, at the west-end of St. Paul’s, and T. Woodward, at the Half-Moon between Temple-Gate, Fleetstreet, London. 1769. Hamilton WFDP. An experimental study of the standing waves in the pulse propagated through the aorta. Am J Phys. 1939;125:48–59. Karamoglu M A system for analysis of arterial blood pressure waveforms in humans. Computers and Biomedical Research 1997;30:244–55 Kelly RP, et al. Nitroglycerin has more favourable effects on left ventricular afterload than apparent from measurement of pressure in a peripheral artery. Eur Heart J. 1990;11:138. Laskey WK, Kussmaul WG.  Arterial wave reflection in heart failure. Circulation. 1987;75:711–22. Merillon JP, Fontenier GJ, Lerallut JF, Jaffrin MY, Motte GA, Genain CP, Gourgon RR. Aortic input impedance in normal man and arterial hypertension: its modification during changes in aortic pressure. Cardiovasc Res. 1982;16:646–56. Milnor WR.  Aortic wavelength as a determinant of the relation between heart rate and body size in mammals. Am J Phys. 1979;237:R3–6. Milnor WR.  Hemodynamics. Baltimore: Williams and Wilkins; 1989. Murgo JP, Westerhof N, Giolma JP, Altobelli SA.  Manipulation of ascending aortic pressure and flow wave reflections with the Valsalva maneuver: relationship to input impedance. Circulation. 1981;63:122–32. Nichols WW, O’Rourke MF.  McDonald’s blood flow in the arteries. Theoretical, experimental and clinical principles. 4th ed. London: Arnold Publishers; 1998. Nichols WW, Conti CR, Walker WE, Milnor WR. Input impedance of the systemic circulation in man. Circ Res. 1977;40:451–8. O’Rourke MF.  Arterial hemodynamics in hypertension. Circ Res. 1970a;27(Suppl 2):123. +.

D. Fitchett and M. F. O’Rourke O’Rourke MF.  Influence of ventricular ejection on the relationship between central aortic and brachial pressure pulse in man. Cardiovasc Res. 1970b;4(3):291. O’Rourke MF.  The arterial pulse in health and disease. Am Heart J. 1971;82:687–702. O’Rourke MF, Avolio AP, Karamaoglu M, Kelly RP. Derivation of ascending aortic pressure waveform from brachial pressure pulse in man. Aust NZ J Med. 1990;20:328. O’Rourke MF, Safar M, Dzau V, editors. Arterial vasodilatation. Mechanisms and therapy. London: Edward Arnold; 1993. p. 222. O’Rourke MF, Adji A, Safar ME. Structure and function of systemic arteries: reflections on the arterial pulse. Am J Hypertens. 2018;31:934–40. Pauca AL, Wallenhaupt SL, Kon ND, Tucker WY. Does radial artery pressure accurately reflect aortic pressure? Chest. 1992;102:1193–8. Remington JW, Wood EH. Formation of peripheral pulse contour in man. J Appl Physiol. 1956;9:433–42. Taylor MG.  The elastic properties of arteries in relation to the physiological functions of the arterial system. Gastroenterology. 1967;52:358–63. Ting CT, Chen JW, Chang MS, Yin FC.  Arterial hemodynamics in human hypertension. Effects of the calcium channel antagonist nifedipine. Hypertension. 1995;25:1326–32. Wetterer E. Flow and pressure in the arterial system, their hemodynamic relationship, and the principles of their measurement. Minnesota Medicine. 1954;37:77–86; passim. William Harvey. De Circulatone Sanguis 1649. Translated by Franklin KJ. Oxford: Blackwell; 1957. Yaginuma T, Avolio A , O'Rourke M, Nichols W, Morgan JJ, Roy P, Baron D, Branson J, Feneley M. Effect of glyceryl trinitrate on peripheral arteries alters left ventricular hydraulic load in man. Cardiovasc Res. 1986;20:153–60. Yin FCP, editor. Ventricular/vascular coupling. Clinical, physiological and engineering aspects. New  York: Springer Verlag; 1987.

Basics of Fluid Physiology

10

Sheldon Magder and Alexandr Magder

Introduction Administration of intravenous fluids is one of the commonest medical acts in hospitalized patients. This chapter will emphasize the physiological role of fluids, principles behind the movement and distribution of water and it solutes, and the characteristics of different kinds of commonly infused fluids. In Chap. 42, use of fluids for both resuscitation and maintenance of normal fluid balance is discussed. Some of these issues have been covered previously (Magder 2001), but in this review the principles are updated. An important influence on this discussion is the excellent review by Bhave and Neilson (Bhave and Neilson 2011a). We will emphasize four basic concepts. (1) Elements, especially sodium ion (Na+) and chloride ion (Cl−), have unique importance when compared to metabolizable organic molecules. (2) The amount of an element in the body can be regulated only by absorption or excretion. (3) The vascular space is in a dynamic equilibrium with the interstitial and other “third” spaces, such S. Magder (*) Royal Victoria Hospital (McGill University Health Centre), Departments of Critical Care and Physiology McGill University, Montreal, QC, Canada e-mail: [email protected] A. Magder Department of Pediatrics, Bernard and Millie Duker Childrens Hospital, Albany Medical Center, Albany, NY, USA e-mail: [email protected]

as the pleural and peritoneal compartments. Because of this, they all have approximately the same osmolality. This means that any administration of resuscitation fluids, or de-resuscitation of fluid, shifts water and elements between all compartments. Thus, volume management must not be confined to just the vascular space. (4) Colloids play a unique role in the maintenance of intravascular and intracellular volumes because they do not readily cross cell membranes.

 hat Is the Role of Water W in Organisms? Organic molecules and elements need to be in solution to react with each other and to move by bulk flow or diffusion from one region to another (Rawn 1989). With the odd exception, water is the solvent for all biological solutions. Life as we know it would not exist without water. This is because water has a unique property which readily allows dissolved substances to become part of its structure (Rawn 1989; Ball 2001). Bodily solutions are essentially mixtures of salts, with dissolved proteins, carbohydrates, lipids, and other small organic molecules. When original cell walls formed, and solutions of organic substances became walled off from the surrounding milieu, regulation of cell volume became an important physiological ­process. This is because cell walls are imbedded

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with complex protein structures, including channels, exchangers, and receptors (O'Neill 1999). A change in cell volume stresses the cell walls and alters the tertiary structure of these large membrane molecules (Macknight and Leaf 1977). Changes in their shape can lead to intracellular transcriptional and non-transcriptional processes that are directed at restoring steady state volume as part of the body’s stress response (O'Neill 1999; Orlov and Hamet 2006). A key message is that, independent of the role water has in altering intravascular volume, infusion of a fluid that results in changes in intracellular volume significantly alters intracellular signaling processes (key messages are listed in Table  10.1) Unfortunately, these processes are complex and not predictable; the consequences can be determined only by empirical studies on whole organisms or cells. These studies likely should be done on human tissues because of species and even cellular specificities. When multi-celled organisms developed an envelope that excluded interior structures from the surrounding environment, it became necessary to evolve systems to regulate the volume and Table 10.1  Key principles from physiology of fluid 1. Independent of the role water has in altering intravascular volume, infusion of a fluid that results in changes in intracellular volume significantly alters intracellular signaling processes 2. In fluid management, even though we only make assessments based on the plasma space, consideration must be given to the consequences of administered fluids for the vascular, interstitial, and intracellular spaces 3. A volume bolus of more than 1 to 1.5 L is not likely to remain in the vascular space because the vessels do not have the capacity to hold it 4. When considering volume therapy, the volume and composition of fluids in all extracellular compartments must be considered because they all are in equilibrium with the plasma space in the steady state 5. One must distinguish the concentration of elements from their actual amount in the body. The total amount of an element in the body is related to the total amount of the volume of water and the concentration of the element and the amount only can be regulated by absorption or excretion because elements cannot be created or metabolized 6. Edema can produce further edema, and large volume resuscitation can make things worse

S. Magder and A. Magder

concentration of substances in this interior space  which is inside the outer integument but outside cells (Stein 2002). This is called the interstitial space (Pitts 1968; Magder 2014; Aukland and Nicolaysen 1981). Without regulation of the concentration of electrolytes in this space, water would be lost or gained from the milieu outside the organism’s outer barrier. Thus, a key message is that in fluid management, even though we only make assessments based  on the plasma space, consideration must be given to the consequences of administered fluids for the vascular, interstitial, and intracellular spaces. Furthermore, it needs to be appreciated that the volume in red cells is not part of the extracellular volume, but it is part of the total intracellular volume, although its properties are different from other cells.

Volume and the Generation of Blood Flow In small organisms, O2 and nutrients can be adequately supplied, and waste excreted, by diffusion from the surrounding environment. However, in large multicellular organisms, diffusion is not adequate and a distribution system is required to allow more rapid conductive flow. This was provided by evolution of the cardiovascular system (Bishopric 2005). The role of volume in the regulation of cardiac output is covered in Chap. 2. In essence, cardiac output is controlled by the interaction of the return of blood to the heart (return function) and cardiac function. The primary force in both of these functions is the stretch of the elastic walls of cardiovascular structures by volume. As emphasized in Chap. 2, some of the blood volume in vessels simply rounds out vessel walls, and some of the volume stretches the walls, but only the portion that stretches the walls produces the elastic recoil force. This is called stressed volume. The remaining volume rounds out vessels but does not stretch their elastic walls; this is called unstressed volume. In a standard size male, total blood volume is approximately 5.5 L. Under resting conditions, about 30%, or 1.3 to 1.4 L, is

10  Basics of Fluid Physiology

Compartments Water makes up 60% of total body mass of an average male below the age of 40 and 50% in females and older males (West 1985; Mudge 1980). The differences between young males and females and older males are due to differences in the proportion of muscle mass relative to total body mass (Bhave and Neilson 2011b). In a 70 kg male, total body water is ~42 L. Of this total, approximately two-thirds of the water, i.e., ~ 28 L, are intracellular fluid (ICF) and 14 L are extracellular fluid (ECF) (Fig.  10.1). The ECF can be subdivided into five sub-compartments. These include plasma volume, interstitial and lymph fluid, dense connective tissue and bone fluids, transcellular fluids within cavities such as the pleural and peritoneal fluids, and the cerebrospinal fluid (Bhave and Neilson 2011a). Plasma volume accounts for 3–4 L of the ECF, and the other 10 to 12 L of the ECF, at least the exchangeable part, is primarily in the interstitial space. Adipose tissue can contain a large amount of water by weight. When body mass index is normal, and there is not a lot of fat, adipose tissue contributes a small amount to total ECF.  However, it can account for a very large proportion of total body water in the morbidly obese (Bhave and Neilson 2011a). Interstitial

Total ≈ 42 L in 70 kg male 35%

3.5 L

Plasma

stressed and an elastic recoil pressure is created in vessels (Magder and De Varennes 1998). The rest of the volume is unstressed and provides a reserve that can be recruited to produce the equivalent of an auto-transfusion as discussed below. Furthermore, with a hematocrit of 40% and a total blood volume of 5.5 L, the plasma volume is about 3.3 L. The proportions of red cell mass and plasma volume in the total blood is the same  in the stressed and unstressed volumes. Thus, only about 1 liter of plasma contributes to stressed volume. This means that the normal plasma component of stressed volume is only about 1 liter. A key point is that a volume bolus of more than 1 to 1.5 L is not likely to remain in the vascular space because the vessels do not have the capacity to hold it.

139

Interstitial

65%

IC

EC -12 L Fig. 10.1  Distribution of water in the body. See text for details. (EC extracellular volume, IC intracellular volume. The arrows indicate that plasma volume and the interstitial space are constantly interacting)

volume as a percentage of total body water can increase dramatically when edema develops. Fluid that accumulates in body cavities, such as the pleural and peritoneal spaces, also freely communicates with the interstitial and plasma spaces, and they all should be considered as one compartment. This becomes very important for understanding the distribution of an infused crystalloid solution. As will be discussed below, the volume and electrolytes in all compartments must equilibrate with the vascular space. A key point is that when considering volume therapy, the volume and composition of fluids in all extracellular compartments must be considered because they all are in equilibrium with the plasma space in the steady state.

Regulation of the Distribution of Body Water and Electrolytes Distribution of water between the extracellular (EC) and intracellular (IC) compartments is determined by hydrostatic pressure and osmosis. Steady state water distribution reflects the balance of these two forces across compartments:

Pic  Pec   ic   ec

(10.1)

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where P is the hydrostatic pressure and Π is the osmotic pressure inside (ic) and outside (ec) cells. Early life forms such as bacteria, fungi, and plants have rigid cell walls that are impermeable to water and produce hydrostatic pressure differences by pumping electrolytes into or outside of their intracellular compartment to maintain their volume. In contrast, animal species evolved non-­rigid cell walls that are permeable. This gave these cells  flexibility of movement, but it also meant that their intracellular volume needs to match the extracellular osmolality. Essentially, Pic − Pec becomes zero and Πic = Πec. In this case, the whole organism is separated from the outside world by a surrounding barrier (i.e., skin) and all inner compartments have the same osmolality. This is expressed in the principle of iso-­ osmolality (West 1985; Freedman 1997) which states that all compartments of the body have essentially the same osmolality. This occurs because capillary endothelium, and almost all cell membranes, are freely permeable to water, which easily moves from areas of lower concentrations of osmoles to areas of higher concentrations of osmoles by osmosis. As will be discussed, there is a small exception to this; osmolality of the plasma is slight greater than that of the rest of the body.

What Are Osmoles? Osmoles are discreet particles dissolved in a solution. They alter the properties of water such as its freezing and vaporization temperatures. Osmolality is the number of particles per mass (weight) of the solution; osmolarity is the number of particles per volume. The preferred term is osmolality because mass is a fixed property of a substance, whereas volume can vary with temperature and external pressure. However, volume is easier to measure and thus osmolarity is commonly used. The osmolality of a solution produces a pressure, which is defined by the Van’t Hoff equation (West 1985; Freedman 1997):

Osmotic pressure  n  c / M  RT

(10.2)

where n is the number of particles, c is the concentration of the substances, M is the molecular weight of the substances, R is the ideal gas constant, and T is the absolute temperature. The expression c/M defines the molar concentration. One mOsmol generates 19.34  mmHg at 37 °C (Fig. 10.2). Of importance, the size of the particle does not matter so that the osmotic effect of a 69 kD albumin molecule is the same as that of a single Na+ atom. Osmotic pressure from an electrolyte is modified by the valence (z) and the nonideality of the solution (φ),

Pressure effect of Osmoles

Fig. 10.2  Pressure effect of osmoles. In the panel on the left, the number of particles is equal on both sides of a semipermeable membrane that does not allow the particles to cross. On the right side, 1 mOsmol (one particle)



19.34 mmHg

was added and water moves from the left to make the concentrations equal. 19.34 mmHg of pressure would need to be added to make the heights of water equal on both sides of the membrane

10  Basics of Fluid Physiology

141

which indicates the deviation of the effective osmolality of a ­substance in a solution from that predicted simply by its mass and particle number. The osmotic pressure from one osmole in the body is thus:

 ec  37, mmHg   19.34    Z  [c]





Osmolatiy  mOsm / kg     Z  [c]



(10.3) (10.4)

Osmotic water movement is determined by how easily water can pass through a membrane, which is called hydraulic permeability (Lp) and the solute concentration gradient (Δ[c]) between two solutions on either side of a membrane.

Osmotic flux  Lp  19.34      c  (10.5)

Most solutes are at least partially permeable across the cell membrane and undergo convective transport with water, but their ability to cross membranes varies. This is described by the reflection coefficient, σ, which is a dimensionless number between 0 and 1. When a solute has σ = 1, no particles move across the membrane, and the observed osmotic gradient produces the maximal osmotic force as predicted based on the difference in the number of particles on each side of the membrane. When there is a difference in the concentration of substances on the two sides of the membrane, the system is in a more “ordered” state and has a lower entropy. This attracts water to

Fig. 10.3  Contents of serum and interstitial and intracellular spaces: Gamblegram

100% 90%

Urea Prot

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reduce the overall ordered state, thus increasing the overall entropy of the system in the same way as the energy in the form of heat moves from an area of higher temperature to an area of lower temperature. If some particles manage to cross the membrane, the osmotic force is reduced and σ is 15%

∆P < 15%

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Fig. 38.3  Differences between Bohr’s and Enghoff’s formulas. Data of 14 mechanically ventilated patients with ARDS (Tusman et  al. 2011b). Patients were ventilated with fixed ventilatory settings at baseline (B) and at four randomized PEEP steps. Bohr’s physiological dead space (VDBohr) and Enghoff’s index were calculated at the end of each PEEP step. Two kinds of responses to PEEP can be

0

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PEEP (cmH2O)

observed: a group of patients in whom driving pressure did not surpass 15% of baseline values (∆P  15%, n = 7). The gray area represents the shunt effect. Data presented as median and first to third quartiles

Table 38.1  Reference values of dead space parameters in adults Patient Healthy volunteer Healthy anesthetized Critically ill anesthetized Critically ill ARDS

Ventilation Spontaneous breathing VT 6 mL/kg PEEP 6 cmH2O VT 6 mL/kg PEEP 8 cmH2O VT 6 mL/kg PEEP 12 cmH2O

VD/VT 0.23 ± 0.08 0.28 ± 0.07

VDaw/VT 0.17 ± 0.09 0.18 ± 0.08

VDalv/VTalv 0.07 ± 0.06 0.09 ± 0.07

0.41 ± 0.07

0.23 ± 0.07

0.23 ± 0.08

0.55 ± 0.11

0.33 ± 0.09

0.29 ± 0.10

From Tusman et al. (2013) Different dead space parameters: Bohr’s physiological (VD/VT), airway (VDaw), and alveolar (VDalv/VTalv) dead spaces observed in spontaneously breathing volunteers and in mechanically ventilated patients

Blanch et al. 1999) and Enghoff’s index reaching even higher values due to the effect of the right-­ to-­left shunt (Beydon et  al. 2002; Kallet et  al. 2017; Doorduin et al. 2016). Monitoring dead space in mechanically ventilated patients transcends the evaluation of alveolar ventilation and the fine-tuning of baseline ventilatory settings. Dead space was found to be useful when conducting lung recruitment maneuvers at the bedside in experimental models (Tusman et  al. 2006, 2010), in morbidly obese patients (Tusman et  al. 2014), and in ARDS patients (Fengmei et  al. 2012; Rodriguez et  al. 2013). Recently, we described how dead space measurements in ARDS patients could be used to

evaluate different patterns in the response to PEEP (Gogniat et al. 2018). Those patients whose response to PEEP was positive based on a decrease in driving pressure (plateau minus PEEP) ≤15% of baseline values showed much lower physiological and alveolar dead space ratios compared to non-responders (Fig.  38.3). These findings are in line with the findings of Suter et  al. who found the lowest VD value at “best” PEEP (Suter et al. 1975). This best PEEP was also associated with lower shunt, higher respiratory compliance, and better cardiac output and oxygen delivery. In contrast, VD increased at low and high PEEP levels, probably because of lung collapse and overdistension, respectively. In

G. Tusman and S. H. Bohm

608

addition, Enghoff’s index calculated by VCap has an important value as a predictor of mortality in ARDS (Nuckton et al. 2002; Kallet et al. 2004). This index is an independent variable related to death in ARDS patients during the early and late evolution of the syndrome.

Monitoring Gas Exchange by VCap Gas exchange occurs by diffusion, a passive mechanism in which O2 and CO2 molecules move along a concentration or partial pressure gradient across the alveolar-capillary membrane. Diffusion is a process contemplated in Fick’s law (Brogioli and Vailati 2001), where the amount of CO2 molecules that cross the membrane per unit of time (Dg/Dt) is directly proportional to the solubility of CO2 in blood (λ), the area available for gas exchange (A), the partial pressure gradient of CO2 across the membrane (ΔP), and inversely proportional to membrane width (w):

Dg / Dt    A  P / w

The almost 20 times higher λ of CO2 compared to that of O2 in blood is responsible for the fact that neither the erythrocyte capillary transit time (i.e., anemia, exercise) nor any increment in w (i.e., interstitial pulmonary diseases) exerts any clinically relevant influence on CO2’s passage to the alveolar compartment. This means that these factors per se do not cause clinically important hypercapnia. However, the remaining two factors of Fick’s law affect the elimination of this gas more profoundly. The A is reduced not only by problems at the alveolar side (atelectasis, airways closure, mucus, pneumonia, etc.) but also by deficient pulmonary perfusion (embolism, excess of PEEP, or any global hemodynamic deterioration). In mechanically ventilated patients, A can be modified dynamically by changes in the underlying disease and changes in ventilator settings, both of which affect the V/Q ratio. Temporal and spatial V/Q inhomogeneities decrease the functional area of gas exchange and thereby reduce the amount of CO2 that can be eliminated by ventilation per minute (VCO2), which results in the retention of CO2 in the arterial side.

The remaining factor of Fick’s law that can affect CO2 diffusion is ΔP or the CO2 gradient between the pulmonary capillaries and the alveoli. While similar to the alveolar-to-arterial oxygen partial pressure gradient, the P(A − a)O2, CO2 elimination relies on a diffusion gradient across the alveolar-capillary membrane in the opposite direction which is abbreviated as P(a  −  A)CO2 (Tusman et  al. 2011a). The clinical meaning of both parameters is roughly the same as both reflect all factors that affect the exchange of these gases across the membrane. In the past, PACO2 could not be measured at the bedside, and thus, for practical purposes, this index was replaced by the difference between PaCO2 and the end-tidal partial pressure of CO2 (PETCO2) obtained by standard capnography. The P(a-ET)CO2 difference in healthy patients during anesthesia is between 3–5 mmHg; any V/Q mismatch increases this value (Nunn and Hill 1960). Nowadays, PACO2 can be accurately measured by VCap which makes P(a-A)CO2 available at the bedside; normal values are between 5–8 mmHg (Tusman et al. 2011b). This latter index makes more physiological sense than the often used P(a − ET)CO2 because PETCO2 represents lung units with a high expiratory-time constant that emptied later during the breath and therefore have more time to receive CO2 molecules from the capillary side. PACO2, on the other hand, represents all lung units with different expiratory time constants and V/Q ratios thereby constituting the average CO2 value within the alveolar compartment in the same way that PaCO2 represents an average value within the arterial blood (Tusman et al. 2011a). Considering the above thoughts about CO2 exchange, the elimination of CO2 can be manipulated clinically in two ways. One is by increasing alveolar ventilation (VA) at constant A, by ­augmenting tidal volume and/or respiratory rate, or by decreasing dead space. VD can be reduced by eliminating any unnecessary instrumental dead space, by adding an inspiratory hold, by limiting plateau pressure and PEEP, or by avoiding autoPEEP. The other way to improve the removal of CO2 is to increase A at constant VA. This can be done by reducing inflammation in alveoli by the use of antibiotics in pneumonia or by recruiting atelec-

38  Clinical Monitoring by Volumetric Capnography

609

tatic lungs. The increased area of gas exchange then allows more CO2 molecules per unit of time to cross the membrane, thereby decreasing not only the partial pressure of CO2 on both sides of the membrane but also the gradient (Fig. 38.4). In this context, P(a  −  A)CO2 becomes a surrogate for the size of A and resembles a more commonly used P(A − a)O2 gradient. Figure 38.4 shows how decreases in A, documented as lung collapse in CT images, alter the shape of capnograms and reduce the amount of CO2 eliminated per breath. The worse the V/Q ratio, the more the capnogram is deformed, and

Hemodynamic Monitoring Pulmonary perfusion transports CO2 molecules into the lungs to be eliminated by ventilation. The elimination of CO2 (VCO2) per minute is calculated by multiplying the area under the VCap curve (VTCO2,br = amount of CO2 eliminated per breath) by the respiratory rate (Fig. 38.1b). There is a direct relationship between pulmonary perfu-

12 PEEP

8 PEEP

CO2 (mmHg)

VCap

CT Images

14 PEEP

the higher the difference between arterial and alveolar PCO2.

70 60 50 40 30 20 10 0

0

50 100 150 200 Volume (mL)

70 60 50 40 30 20 10 0

0

50 100 150 200 Volume (mL)

70 60 50 40 30 20 10 0

0

50 100 150 200 Volume (mL)

Pa-ACO2 (mmHg)

9

13

25

Pa-ETCO2 (mmHg)

5

8

14

PA-aO2 (mmHg)

40

150

350

Fig. 38.4  Monitoring the diffusion of CO2 by volumetric capnography. Data belongs to a mechanically ventilated pig with ARDS after lung recruitment and PEEP titration. PEEP titration was performed in a volume-controlled mode of ventilation using fixed settings and the same alveolar ventilation. The open lung PEEP – the minimum amount of PEEP that keeps the lung without collapse  – was found at 14 cmH2O (left). The first dependent lung atelectasis appears at PEEP 12 cmH2O (middle), while almost half the lungs are collapsed at PEEP 8 cmH2O

(right). Looking at the CT findings, the corresponding changes in the area of gas exchange (A) become evident and are associated with characteristic modifications of the shape of the VCap and of the arterial (black dot), the end-­ tidal (red dot), and the mean alveolar (green dot) partial pressures of CO2. Corresponding differences between arterial and alveolar, between arterial and end-tidal PCO2, as well as between alveolar and arterial O2 are provided in the table below the curves

G. Tusman and S. H. Bohm

610

sion and VCO2, which was clearly demonstrated in mathematical models (Schwardt et  al. 1994; Schwardt et al. 1991), in animals (Tusman et al. 2010), and in humans during the weaning of cardiopulmonary bypass (Tusman et al. 2005, 2012). This close dependency is clinically useful for monitoring pulmonary blood flow noninvasively on a breath-by-breath basis. Such clinical monitoring can be conducted in a qualitative way simply by observing the real-time changes in expired CO2 during an acute hemodynamic problem. Typical examples are the sudden decrease of PETCO2 and VCO2 values during surgery resulting from low cardiac output states of any cause (Fig. 38.2d). Quantitative monitoring utilizes VCap to calculate effective pulmonary blood flow (EPBF) by applying a partial CO2 rebreathing and the capnodynamic method (Capek and Roy 1988; Peyton et  al. 2006). But what exactly is EPBF? This parameter is the portion of right heart cardiac out (CO) that comes in contact with ventilated alveoli and thus participates in gas exchange. Therefore, right heart cardiac output (CO) is nothing else but the sum of blood flowing through the effective (COEPBF) and shunted (COSHUNT) portions of the lungs per unit of time. COEPBF can be measured noninvasively by applying the inverse Fick principle and by using CO2 instead of O2:

CO EPBF  VCO2 /  CvCO2  CaCO2  ,



CvCO2 and CaCO2 are venous and arterial contents of carbon dioxide, respectively. In order to make the COEPBF calculation noninvasive, the kinetics of CO2 needed to be disturbed either by a short rebreathing maneuver (adding an instrumental dead space or adding CO2 to the inspired gases) or by a temporary change in alveolar ventilation (Capek and Roy 1988; Peyton et al. 2006). The following is the differential Fick equation applied before (b) and after (a) CO2 alteration: CO EPBF   VCO2  b   VCO2 a  / SCO2 

 PACO a   PACO  b 2

2



where SCO2 is the coefficient of solubility of CO2 in blood introduced in the equation to transform the alveolar partial pressure (mmHg) into a concentration (Lgas/Lblood) of CO2. The differential Fick equation assumes that the mixed venous CO2 concentration remains constant and that the capillary fraction of CO2 is similar to the FACO2 measured by VCap. The NICO monitor (Respironics, Murrysville, PA, US) calculates COEPBF using the partial CO2-­ rebreathing technique intermittently throughout an additional instrumental dead space (Capek and Roy 1988; Gedeon et al. 1980; Jaffe 1999). This additional dead space is a short circuit placed in parallel at the “Y” piece of the patient’s circuit. This parallel tubing makes contact with the main ventilatory circuit when an automatic valve is open. Thus, breathing gases that comes into this special tube are re-inhaled during 50 seconds. After a total time of 3  minutes and after CO2 concentration reaches the baseline values, the COEPBF is calculated. Shunt fraction is estimated from the SpO2 and FiO2 values entered into the device by using the iso-shunt plot and adding this to the COEPBF to obtain the global CO (Gedeon et  al. 1980). The NICO performance was compared to standard measurements of CO by thermodilution in many experimental and clinical studies with variable results (Gama de Abreu et  al. 1997; Nilsson et  al. 2001; Rocco et al. 2004; Allardet-Servent et al. 2009). In general, good agreement with the reference CO was found in healthy lungs, and limited agreement was observed in sick lungs with high shunt fractions. The capnotracking method described by Peyton et  al. calculates COEPBF based on brief modifications of tidal volume (Peyton et al. 2008; Peyton 2012; Peyton 2013). A complete 12-breaths sequence of six low VTs followed by six high VTs induces a change in alveolar ventilation and the corresponding CO2 balance from which COEPBF is derived. A series of equations, called calibration, capacitance, and continuous formulas, are applied to get the final COEPBF value (Peyton et  al. 2008; Peyton 2012, 2013). The global CO is calculated by adding COEPBF to the COSHUNT value estimated by the shunt equation,

38  Clinical Monitoring by Volumetric Capnography

taking into account pulse oximetry (SpO2) and by assuming a mixed venous saturation of 70%. Thus, the system delivers fully automated and quasi-continuous COEPBF using Fick’s principle and avoids any additional rebreathing loop within the ventilator’s circuit. The capnotracking method showed good accuracy and agreement with the standard right heart thermodilution technique and transesophageal echocardiography (Peyton 2012). The authors have recently improved the technique in a second-generation capnotracking prototype which was tested in 50 patients undergoing cardiac and liver surgeries (Peyton and Kozub 2018). Compared to thermodilution, the bias was −0.3 L/min, the percentage of error ±38%, concordance in the measurement of changes at least 15% in CO of 81%, and intra-class correlation coefficient of 0.91. Albu et  al. described another solution of the capnodynamic equation to obtain COEPBF and the effective lung volume (ELV) by using a mole balance equation for the carbon dioxide content in the lung (Albu et al. 2013). The equation can be implemented in a ventilatory algorithm which changes alveolar ventilation by inspiratory or expiratory holds maintaining VT stability. This pattern alters the alveolar fraction (FACO2) and the amount of expired (VCO2) carbon dioxide, including 3 unknown values in the formula  – ELV, EPBF, and CvCO2: ELV  (FACO2 n  FACO2 n 1 )  EPBF  t n  (CvCO2  CaCO2 )  VTCO2 where n is the current breath, n − 1 is the previous breath and ∆tn is the current breath cycle time. The left-hand side of the equation represents the difference in CO2 content in the lungs between two breaths, while the right-hand side expresses the circulatory supply of CO2 into the lung. The equation compares the content and elimination of CO2 during pause-induced fluctuations in alveolar ventilation without altering airway pressures or lung volumes. This approach avoids the impact of increases in VT or airways pressure on hemodynamics.

611

The actual algorithm includes a sequence of 6 normal breaths followed by 3 breaths with an expiratory hold of 5 seconds. This breathing pattern is controlled by software within the ventilator (Servoi, Maquet, Sweden) which cyclically varies the PETCO2 between 4–8  mmHg. The above formula is applied continuously, whenever a new breath replaces the previous one in the formula thereby outputting one COEPBF value per breath. Sander et al. compared the performance of the capnodynamic method and thermodilution with the reference ultrasonic flow probe placed around the pulmonary artery trunk in pigs (Sander et al. 2014). Different hemodynamic states were created by hemorrhage, fluid overload, infusions of vasoactive drugs, and occlusion of the vena cava. They found a bias (limits of agreement) of 0.2 (−1.0 to −1.4) L/min−1 and a percentage error (PE) for COEPBF of 47%. The trending ability of COEPBF was assessed by calculating delta values from CO readings before and during each intervention. The correlation between delta-COEPBF and delta-CO by ultrasound was high (r = 0.96, P 14% in minute elimination of CO2 (VCO2) and >15% in cardiac output (CO) compared to the values immediately before the maneuver. Similar changes were observed in systolic (SAP), diastolic (DAP), and

mean (MAP) arterial pressures (upper curves). By definition, this patient was preload-dependent and should respond positively to intravenous fluids. The arrow indicates the effect of a decreased expiratory tidal volume on VCO2 at the very moment PEEP was increased. The VCO2 is evaluated during the last breaths (dotted circle) and compared to previous values with 5 cmH2O of PEEP (more details in Monnet et al. 2013)

Summary

has great potential to provide information during unsteady-state conditions. This will allow the assessment of many of the individual components of cardiorespiratory functions at the bedside. However, the context sensitivity of CO2 kinetics must always be considered to avoid false conclusions.

VCap possesses all features of an ideal noninvasive monitoring methodology providing important clinical insights into gas exchange and hemodynamics breath-by-breath. Although VCap can be used to evaluate steady-state conditions such as the metabolic production of CO2, it also

38  Clinical Monitoring by Volumetric Capnography

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G. Tusman and S. H. Bohm Monge García IM, Gil Cano A, García Romero M, Monterroso Pintado R, Pérez Madueño V, Díaz Monrové JC. Non-invasive assessment of fluid responsiveness by changes in partial end-tidal CO2 pressure during passive leg-raising maneuver. Ann Intensive Care. 2012;2:9. Monnet X, Rienzo M, Osman D, Anguel N, Richard C, Pinsky MR, et  al. Passive leg raising predicts fluid responsiveness in the critically ill. Crit Care Med. 2006;34:1402–7. Monnet X, Osman D, Ridel C, Lamia B, Richard C, Teboul JL.  Predicting volume responsiveness by using the end-expiratory occlusion in mechanically ventilated intensive care unit patients. Crit Care Med. 2009;37:951–6. Monnet X, Bataille A, Magalhaes E, Barrois J, Le Corre M, Gosset C, Guerin L, Richard C, Teboul JL.  End-­ tidel carbon dioxide is better than arterial pressure for predicting volume responsiveness by the passive leg raising test. Intensive Care Med. 2013;39:93–100. Nilsson LB, Eldrup N, Berthelsen PG.  Lack of agreement between thermodilution and carbon dioxide-­ rebreathing cardiac output. Acta Anaesthesiol Scand. 2001;45:680–5. Nuckton TJ, Alonso JA, Kallet RH, Daniel BM, Pittet JF, Eisner MD, Matthay MA.  Pulmonary deadspace fraction as a risk factor for death in the acute respiratory distress syndrome. N Engl J Med. 2002;346:1281–6. Nunn JF, Hill DW. Respiratory dead space and arterial to end-tidal CO2 difference in anesthetized man. J Appl Physiol. 1960;15:383–9. Olsson SG, Fletcher R, Jonson B, Nordstroem L, Prakash O. Clinical studies of gas exchange during ventilatory support  – a method using the Siemens-Elema CO2 analyzer. Br J Anaesth. 1980;52:491–8. Orr JA, Jaffe MB.  Combining flow and carbon dioxide, Chapter 39. In: Gravenstein, et  al., editors. Capnography. 2nd ed. Cambridge University Press; 1994. p. 407–11. Payen D, de Pont AC, Sakr Y, Spies C, Reinhart K, Vincent JL, et  al. A positive fluid balance is associated with a worse outcome in patients with acute renal failure. Crit Care. 2008;12:R74. Peyton PJ. Continuous minimally invasive peri-operative monitoring of cardiac output by pulmonary capnotracking: comparison with thermodilution and transesophageal echocardiography. J Clin Monit Comput. 2012;26:121–32. Peyton PJ. Pulmonary carbon dioxide elimination for cardiac output monitoring in peri-operative and critical care patients: history and current status. J Health Eng. 2013;4:203–22. Peyton PJ, Kozub M.  Performance of a second generation pulmonary capnotracking system for continuous monitoring of cardiac output. J Clin Monit Comput. 2018;32:1057–64. Peyton PJ, Venkatesan Y, Hood SG, Junor P, May C.  Noninvasive, automated and continuous cardiac output monitoring by pulmonary capnodynamics: breath-by-breath comparison with ultrasonic flow probe. Anesthesiology. 2006;105:72–80.

38  Clinical Monitoring by Volumetric Capnography Peyton PJ, Thompson D, Junor P.  Non-invasive automated measurement of cardiac output during stable cardiac surgery using a fully integrated differential CO2 Fick method. J Clin Monit Comput. 2008;22:285–92. Preisman S, Kogan S, Berkenstadt H, Perel A. Predicting fluid responsiveness in patients undergoing cardiac surgery: functional haemodynamic parameters including the respiratory systolic variation test and static preload indicators. Br J Anaesth. 2005;95:746–55. Protti A, Andreis DT, Monti M, Santini A, Sparacino C, et al. Lung stress and strain during mechanical ventilation: any difference between statics and dynamics? Crit Care Med. 2013;41:1046–55. Rivers E, Nguyen B, Havstad S, Ressler J, Muzzin A, Knoblich B, et  al. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med. 2001;345:1368–77. Rocco M, Spadetta G, Morelli A, Dell’Utri D, Porzi P, Conti G, Pietrapaoli P.  A comparative evaluation of thermodilution and partial CO2 rebreathing techniques for cardiac output assessment in critically ill patients during assisted ventilation. Intensive Care Med. 2004;30:82–7. Rodriguez PO, Bonelli I, Setten M, Attie S, Madorno M, Maskin LP, Valentini R.  Transpulmonary pressure and gas exchange during decremental PEEP titration in pulmonary ARDS patients. Respir Care. 2013;58:754–63. Roizen MF, Schreider B, Austin W, Polk S. Pulse oximetry, capnography, and blood gas measurements: reducing cost and improving the quality of care with technology. J Clin Monit. 1993;9:237–40. Sander CH, Hallbäck M, Wallin M, Emtell P, Oldner A, Björne H.  Novel continuous capnodynamic method for cardiac output assessment during mechanical ventilation. Br J Anaesth. 2014;112:824–31. Sander CH, Hallbäck M, Suarez Sipmann F, Wallin M, Oldner A, Björne H.  A novel continuous capnodynamic method for cardiac output assessment in a porcine model of lung lavage. Acta Anaesthesiol Scand. 2015; https://doi.org/10.1111/aas.12559. Schwardt JD, Gobran SR, Neufeld GR, Aukburg SJ, Scherer PW.  Sensitivity of CO2 washout to changes in acinar structure in a single-path model of lung airways. Ann Biomed Eng. 1991;19:679–97. Schwardt JD, Neufeld GR, Baumgardner JE, Scherer PW.  Noninvasive recovery of acinar anatomic information from CO2 expirograms. Ann Biomed Eng. 1994;22:293–306. Sinha P, Flower O, Soni N. Deadspace ventilation: a waste of breath! Intensive Care Med. 2011;37:735–46. Siobal MS.  Monitoring exhaled carbón dioxide. Respir Care. 2016;61:1397–416. Suarez Sipmann F, Bohm SH, Tusman G.  Volumetric capnography: the time has come. Curr Opin Crit Care. 2014;20:333–9. Suter PM, Fairley HB, Isenberg MD.  Optimum end-­ expiratory airway pressure in patients with acute pulmonary failure. N Engl J Med. 1975;292:284–9.

617 Tusman G, Areta M, Climente C, Plit R, Suarez-Sipmann F, Rodríguez-Nieto MJ.  Effect of pulmonary perfusion on the slopes of single-breath test of CO2. J Appl Physiol. 2005;99:650–5. Tusman G, Suarez-Sipmann F, Bohm SH, Pech T, Reissmann H, Meschino G, et  al. Monitoring dead space during recruitment and PEEP titration in an experimental model. Intensive Care Med. 2006;32:1863–71. Tusman G, Scandurra A, Bohm SH, Suarez Sipmann F, Clara F.  Model fitting of volumetric capnograms improves calculations of airway dead space and slope of phase III.  J Clin Monitor Comput. 2009;23:197–206. Tusman G, Bohm SH, Suarez-Sipmann F, Scandurra A, Hedenstierna G.  Lung recruitment and positive end-­ expiratory pressure have different effects on CO2 elimination in healthy and sick lungs. Anesth Analg. 2010;111:968–77. Tusman G, Suarez Sipmann F, Bohm SH.  Rationale of dead space measurement by volumetric capnography. Anest Analg. 2011a;114:866–74. Tusman G, Suarez Sipmann F, Borges JB, Hedenstierna G, Bohm SH. Validation of Bohr dead space measured by volumetric capnography. Intensive Care Med. 2011b;37:870–4. Tusman G, Suarez-Sipmann F, Paez G, Alvarez J, Bohm SH. States of low pulmonary blood flow can be detected non-invasively at the bedside measuring alveolar dead space. J Clin Monit Comput. 2012;26:183–90. Tusman G, Gogniat E, Bohm SH, Scandurra A, et  al. Reference values for volumetric capnography-derived non-invasive parameters in healthy individuals. J Clin Monit Comput. 2013;27:281–8. Tusman G, Groisman I, Fiolo FE, Scandurra A, Martinez Arca J, Krumrick G, Bohm SH, Suarez SF.  Noninvasive monitoring of lung recruitment maneuvers in morbidly obese patients: the role of pulse oximetry and volumetric capnography. Anesth Analg. 2014;118:137–44. Tusman G, Bohm SH, Suarez SF.  Dead space during one-lung ventilation. Curr Opin Anesthesiol. 2015;28:10–7. Tusman G, Groisman I, Maidana GA, Scandurra A, Arca JM, Bohm SH, Suarez-Sipmann F. The sensitivity and specificity of pulmonary carbon dioxide elimination for noninvasive assessment of fluid responsiveness. Anesth Analg. 2016;122:1404–11. Vincent JL, Sakr Y, Sprung CL, Ranieri VM, Reinhart K, Gerlach H, et  al. Sepsis in European intensive care units: results of the SOAP study. Crit Care Med. 2006;34:344–53. Wiklund L. Carbon dioxide formation and elimination in man: recent theories and possible consequences. Ups J Med Sci. 1996;101:35–67. Wolff G, Brunner JX, Weibel W, Bowes CL, Muchenberger R, Bertschmann W.  Anatomical and series dead space volume: concept and measurement in clinical praxis. ACP Appl Cardiopulm Pathophysiol. 1989;2:299–307.

MRI in the Assessment of Cardiopulmonary Interaction

39

Ritu R. Gill and Samuel Patz

Introduction

sequences to enhance the signal from the lung parenchyma and assess cardiopulmonary interacPulmonary functional imaging combining ana- tions (Guimaraes et  al. 2014; Van Beek et  al. tomical and morphological evaluation is being 2004; van Beek and Hoffman 2008; Nakai et al. actively explored both in research and clinical 2008). practice (Matsuoka et  al. 2009). Functional In the current clinical practice, comprehensive assessment of the lungs can be achieved with sev- cardiopulmonary imaging evaluation cannot be eral techniques such as dual energy computed achieved with a single modality. The convention tomography (CT), lung scintigraphy, 18-FDG-­ is to perform serial exams in varying physiologiPETCT, and MRI, each with its own advantages cal and disease states, using a combination of and limitations. MRI of the lung offers a unique imaging techniques with and without ionizing advantage over other modalities, as it allows us to radiation and intravenous contrast. However, due map cardiopulmonary interactions without the to increasing concern over exposure to ionizing use of ionizing radiation. However, MRI of lung radiation and intravenous contrast, repeat examican be challenging due to the inherent lack of nations over time to assess treatment response, protons in the lung parenchyma, magnetic field map pathophysiology and cardiopulmonary interinhomogeneity due to susceptibility differences actions may not be a viable strategy. Alternatively, between air and tissues, and artifacts from car- single comprehensive MR scans combining both diac and respiratory motion (Biederer et  al. anatomical imaging and functional components 2012a). Novel MR techniques based on proton potentially could be used to provide all the inforimaging (described below) such as arterial spin mation. The latter approach would comprise of a labeling, Fourier decomposition, and hyperpolar- generic protocol, which could take 10 times lonized gas can be combined with routine clinical ger to perform as compared to a CT scan and could result in image degradation due to subject motion secondary to fatigue. Therefore, it is vital R. R. Gill (*) that the information needed from an MRI be Department of Radiology, Beth Israel Deaconess clearly defined, thus allowing for optimal Medical Center, Harvard Medical School, Boston, MA, USA sequence and protocol selection. e-mail: [email protected] In order to image cardiopulmonary interacS. Patz tions and to allow simultaneous evaluation of the Department of Radiology, Harvard Medical School, lungs during a routine cardiac or thoracic Brigham & Women’s Hospital, Boston, MA, USA ­evaluation (Cheng et al. 2017) the MRI workflow e-mail: [email protected] © Springer Nature Switzerland AG 2021 S. Magder et al. (eds.), Cardiopulmonary Monitoring, https://doi.org/10.1007/978-3-030-73387-2_39

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Fig. 39.1 (a) Pre-contrast coronal VIBE, post-contrast coronal TWIST and coronal FD TrueFISP respectively. Corresponding ROIs are indicated in (b) and (c) to indicate that the same region of interest was evaluated in both the cases. An additional ROI (green) was placed in the pulmonary artery to acquire the AIF in the DCE acquisi-

tion. (d–f) Calculated blood volume map (iAUC) from the dynamic TWIST acquisition, perfusion and ventilation maps from the FD TrueFISP sequence, respectively. (g) Time course curves for the two ROIs in (b) and (h) show the components of the Fourier Decomposition for the ROI in (c)

will need to be optimized to include sequences that can be combined with contrast-enhanced volumetric cardiac-resolved flow imaging (4D flow). Pulmonary functional assessment using MRI can map perfusion, ventilation, and measure gas exchange with and without intravenous and inhalational contrast agents. Dynamic contrast enhanced (DCE) MRI utilizes gadolinium-based vascular contrast agents tracked over time to generate pharmacokinetic indices that can be used to assess the pathophysiology of a disease process. Inhaled agents, such as hyperpolarized gases, can measure regional ventilation as well as gas exchange (Nakai et  al. 2008). Novel techniques that do not rely on any contrast material such as arterial spin labeling, Fourier decomposition, and Ultra short turbo spin echo techniques also can be used to map function in the lung (Guimaraes et al. 2014; Bauman et al. 2016; Körzdörfer et al. 2019; Biederer et al. 2012b) (Fig. 39.1).

and include strategies to minimize or eliminate motion artifacts from cardiac and respiratory motion. This includes cardiac gating synchronization of the MR acquisition with the ECG tracing; this is more reliable in normal sinus rhythm. Respiratory motion can be highly problematic. Apart from artifacts, it affects the reproducibility of pharmacokinetic parameters; hence breath-hold acquisition may be needed for short sequences. For longer sequences, however, other techniques such as reordering of phase-encoding steps are recommended. Bellows and respiratory triggering can be used in patients with regular respiratory rates. Some sequences provide a navigator to measure the position of the diaphragm. The pulse sequence is then programmed to limit the data acquisition to a narrow range of diaphragm positions. The navigator typically uses a fast line-scan (one dimensional readout) to monitor diaphragmatic position at the beginning of each radiofrequency (RF) excitation pulse of the nuclei.

Techniques for Mapping Cardiopulmonary Interaction Mapping cardiopulmonary interactions in the lung is challenging due to respiratory and cardiac motion. Several sequences are available for thoracic imaging that depend on the clinical indication

 ynamic Contrast Enhanced D (DCE) MRI DCE MRI is used to map perfusion of the lungs, by injecting gadolinium-based contrast agents and tracking the contrast over time (Gill et  al.

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2015). This is achieved by acquiring a three-­ dimensional acquisition of the whole chest over a 2–5  minute period during shallow breathing. Traditionally, sequences such as Fast low angle shot (FLASH) or time-resolved angiography with interleaved stochastic trajectories (TWIST) were used to map perfusion, but these sequences require motion correction prior to pharmacokinetic evaluation, and depending on the type of motion correction technique, the reproducibility of the pharmakokinetic parameters may be adversely affected (Tokuda et al. 2011). Motion robust sequences, such as Radial Acquisition of Volumetric Interpolated Breath-­hold Examination (RADIAL-VIBE) and Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAIPIRINHA-VIBE), can obviate the need for motion correction and improve the reproducibility of the pharmacokinetics (Ng 2020; Kim et al. 2016). Another strategy is to acquire timed runs through the chest using short breath-hold gradi-

ent echo sequences but these can be cumbersome as the patient needs to hold still in the same position for a period of 5 minutes, and also requires precision to align all the contrast enhanced sequences, prior to calculating pharmakokinetics. Additionally, time-resolved MR angiography using techniques such as TWIST can be used to evaluate pulmonary vascular flow with high temporal resolution. The acquired data is then postprocessed using softwares such as Osirix (Bernex, Switzerland) /Matlab (Boston, US) to generate the ­ pharmacokinetic parameters (Yamamuro et  al. 2007; Mamata et  al. 2011; Ingrisch et  al. 2014; Scheffler et  al. 2010a). The arterial input function can be obtained by placing a region of interest (ROI) in the aorta at peak arterial enhancement and another ROI on the lungs or the area to be assessed and then using a Toft’s model or extended Toft’s model to generate permeability parameters (Tofts 1997; Duan et  al. 2017) (Fig. 39.2).

Fig. 39.2  A 48-year-old man with right-sided epithelial mesothelioma who underwent MRI for surgical planning. Coronal post-contrast and multiparametric maps depicting perfusion parameters of the tumor. MR-determined pharmacokinetic parameters: AUC  area under the curve and represents blood volume, ktrans  transfer constant

(min−1), kep rate constant (min−1), Ve elimination constant (dimensionless). 3D FLASH sequence (TR/TE: 2.02/0.84 ms; flip angle: 10 degrees; acquisition matrix: 256 × 135; slab thickness: 168–200  mm; 42–56 slice encoding

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Proton Imaging Proton imaging is acquired without the administration of intravenous or oral contrast. Two main techniques are used in proton imaging of the lung: arterial spin labeling (ASL), and Fourier Decomposition (FD); these techniques are used to quantify regional perfusion (ASL and FD) and ventilation (FD) (Bauman et  al. 2013; Lederlin et  al. 2013; Miller et  al. 2014). These are free breathing non-contrast techniques based on mathematical modeling of vascular delivery and exchange within tissues (ASL) and spectral decomposition (using a Fourier decomposition) to determine time-dependent modulation in the parenchyma signal either at the heart rate or breathing rate (FD) to determine perfusion or ventilation dependent signal respectively. The imaging data is acquired while the patient is breathing and then postprocessed to derive ventilation and perfusion maps(Kjørstad et  al. 2014; Buxton et al. 1998).

Hyperpolarized Gases

MRI imaging using hyperpolarized Nobel gases requires special RF coils and typically a polarizer on site. For 3He, the lifetime can be many days if properly stored. However, for 129Xe, the polarization is very short lived with relaxation times on the order of an hour. Nobel gases such as 3Helium and 129Xenon can be hyperpolarized using a laser, and when inhaled during a MR exam can amplify the signal by a factor of 100,000 beyond thermal equilibrium (Van Beek et  al. 2004; Fain et  al. 2010). 3He is relatively easier to polarize compared to 129Xe, and has a stronger signal. Because of these reasons it was initially more popular of the two. Xenon polarizers, however, have improved and because the price of a liter of He gas is now ~$3000, 129Xe, whose price is at least 10 times less, is the gas of choice. 129Xe is soluble both in septal tissue and blood. In particular, it is lipid soluble, which allows diffusion into blood. The T1 for both 3He and 129Xe is highly sensitive to the concentration of oxygen. Hence, before the Oxygen Enhanced MRI gas is inhaled, it is kept in an oxygen-free conOxygen can be used to enhance the signal from tainer. The T1 for both 3He and 129Xe once inhaled the lung by using a fast imaging sequence such as is on the order of 10–20 seconds. Thus, for each single-shot fast spin echo with an inversion inhalation of hyperpolarized gas, one must comrecovery pre-pulse to introduce T1 contrast(Miller plete the imaging acquisition very rapidly et  al. 2014; Biederer et  al. 2014). Oxygen because the hyperpolarization decays quickly. enhances the magnitude of the microscopic fluc- Normally, this would be a great impediment to tuations of the local magnetic field that each imaging because one normally relies on waiting water molecule experiences and thereby enhances time TR between each excitation of the nuclei to the longitudinal magnetization (T1) recovery allow for T1 relaxation. When using hyperpolarprocess. This technique allows comparison of the ized gas, however, one can repeatedly excite the difference in lung parenchyma during breathing nuclei much faster because there is no need to normal air vs breathing higher concentrations wait for T1 recovery. Instead, a small fraction of and generate a qualitative map of parenchymal the nonrenewable hyperpolarization is used for oxygen concentration; the differential subtraction each excitation of the nuclei. The gyromagnetic maps can help in evaluating diffuse lung disease, ratio of 129Xe is approximately 1/3 that of 3He such as emphysema and interstitial lung and therefore its inherent sensitivity is less by disease(Nakai et  al. 2008; Biederer et  al. 2014; that factor compared to 3He. Besides the lower Kruger et al. 2016; Ohno et al. 2014). The signal-­ cost of 129Xe compared to 3He, a great advantage to-­noise ratio (SNR) provided by oxygen is lower of 129Xe is its solubility in tissue and blood. when compared to hyperpolarized noble gases, Further, the different tissue compartments, i.e. but its cost is much less. However due to the low parenchyma, RBC’s and plasma, all have differSNR, this technique has had limited clinical ent chemical shifts. This has led to the developimpact. ment of very elegant methods to measure the

39  MRI in the Assessment of Cardiopulmonary Interaction

dynamics of the signal in each compartment and then with the use of various models of diffusion, use the data to calculate a number of very useful pulmonary function parameters (Van Beek et al. 2004; Miller et al. 2014; Fain et al. 2010; Albert et al. 2000).

Fluorine Gas MRI Fluorine-19 (19F) MRI with inhaled inert fluorinated gases can provide functional images of the lungs. Fluorine-19 (19F) MRI is typically performed in humans by using a gas mixture containing 79% perfluoropropane (PFP) or sulfur hexafluoride (SF6) and 21% oxygen. It is relatively inexpensive and can be performed on any MRI scanner with broadband multinuclear imaging capabilities (Schmieder et al. 2016; Adolphi and Kuethe 2008; Couch et  al. 2019; Schreiber et al. 2001). Imaging with 19F can be acquired in a single breath-hold, or in a time-resolved multiple breath fashion, to measure ventilation defect percent (VDP), or quantify gas replacement (i.e., fractional ventilation), and map the kinetics of gas exchange (Schreiber et al. 2001) .

 pplications of MR Imaging A in Cardiopulmonary Interactions MRI can be used to assess cardiopulmonary interactions in a variety of diseases and clinical states both as a primary modality and as an adjunct to other modalities.

Pulmonary Perfusion Pulmonary perfusion can be mapped in a variety of lung diseases, such as emphysema, interstitial lung disease, asthma, tumors, and in primary and secondary pulmonary vascular pathologies. The most commonly used technique for mapping perfusion is dynamic contrast enhanced MRI.  The pharmacokinetics derived from the perfusion maps can be used to predict histology, angiogenesis, compare pre- and posttreatment scans to

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assess response and compare with outcome measures, to identify disease-specific imaging biomarkers (Mamata et  al. 2011; Ingrisch et  al. 2014; Scheffler et al. 2010a; Coolen et al. 2012; Tao et  al. 2016; Hochhegger et  al. 2011; Swift et al. 2014). The pulmonary arteries can also be simultaneously assessed using Magnetic resonance angiography, which consists of a heavily T1 weighted gradient echo sequence after injection of gadolinium-­ based contrast. Time-resolved MR angiography using techniques such as TWIST help evaluate pulmonary vascular flow with high temporal resolution. This enables evaluation of not only pulmonary vascular anatomy but can also assess pulmonary hemodynamics, including pulmonary perfusion. 3D whole heart navigator gated SSFP sequence is a non-contrast alternative to evaluate the pulmonary artery, especially in patients with severe renal dysfunction(Swift et al. 2014). Additionally, the pulmonic valve can also be evaluated by using cine SSFP images in the short axis plane and sagittal view during a focused MR exam for assessment of the heart and lungs. A flow quantification sequence is used for evaluating and quantifying pulmonic valvular and flow abnormalities. Quantitative pulmonary perfusion can be combined with magnetic resonance angiography (MRA) to enhance the accuracy of detection of pulmonary emboli and also assess the functional consequences of pulmonary emboli on the heart and the lung parenchyma and assess the degree of the resulting hemodynamic abnormality. Arterial spin labeling (also known as arterial spin tagging) also can be used for mapping perfusion abnormalities and can be a very powerful tool for mapping interventions after detection of ­pulmonary pathology because it does not need contrast, and the images can be acquired during free breathing at multiple time points. Pulmonary hypertension is characterized by abnormally elevated pulmonary arterial pressures and increased pulmonary vascular resistance. Primary or idiopathic pulmonary hypertension presents in the first or second decade Secondary causes of pulmonary hypertension are cardiac disorders, chronic lung disease, chronic pulmo-

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nary emboli and shunts. MRI can be used to depict the anatomic changes of pulmonary hypertension including dilated central pulmonary arteries, tapered or pruned peripheral pulmonary arteries, right ventricular hypertrophy, and systolic bowing of the interventricular septum. A comprehensive cardiac MR exam is indicated to evaluate the valves and the chambers. Features suggestive of pulmonary hypertension include dilatation of the main pulmonary artery >2.8 cm or greater than the size of ascending aorta, and features suggestive of right heart strain. The severity of pulmonary hypertension has been correlated with increased intraluminal signal intensity in the pulmonary arteries on spin-­ echo images (Swift et al. 2014), and can be characterized by high signal intensity in the lumen of the pulmonary artery in systole and early diastole due to slow blood flow in diastole as compared to signal void in both systole and diastole in normal individuals. RV function, including stroke volume, end diastolic volume and ejection fraction, can be assessed from MR images and provide important prognostic value (Peacock and Noordegraaf 2013). Additionally there may be decreased myocardial perfusion reserve, which inversely correlates with RV workload and ejection fraction and reduced biventricular regional function associated with increased RV load (Peacock and Noordegraaf 2013). Perfusion imaging of the lungs can identify abnormalities in pulmonary perfusion, such as infarcts and mosaic perfusion suggestive of chronic thromboembolic disease. Delayed mid-myocardial enhancement at the RV insertion points of interventricular septum can be seen in patients with pulmonary hypertension due to increased stress. Altered hemodynamics detected with time-­ resolved MRA has been shown to correlate with pulmonary arterial pressure and pulmonary vascular resistance (Peacock and Noordegraaf 2013). Therefore a carefully planned cardiopulmonary MRI exam can provide both anatomical and functional imaging and illustrate the cardiopulmonary interaction and guide management. Pulmonary embolism is the third most common acute cardiovascular disease and can be acute or chronic. CT is the most commonly used

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imaging modality in the evaluation of acute pulmonary embolism, MRI shows comparable diagnostic accuracy but has low specificity. A meta-analysis of studies using MRI for evaluating pulmonary embolism has shown that MRI has a sensitivity of 100% for detecting PE in central and lobar arteries, 84% in segmental arteries and 40% in subsegmental arteries (Oudkerk et al. 2002). There is limited sensitivity for distal PE and results can be inconclusive in up to 30–50% patients (Revel et al. 2012). Pulmonary perfusion can be assessed by noncontrast sequences such as ASL and FD before and after treatment to ensure perfusion abnormalities have resolved. However, these techniques are currently only qualitative; they show patterns of abnormality but cannot give the quantitative information provided by DCE MRI and nuclear medicine. Nevertheless, they can be combined with cardiac perfusion scans as they can be performed in 2–5  minutes during free breathing without contrast administration and can provide disease-specific functional information.

 ung Parenchyma (Diffuse Lung L Disease) CT is the modality of choice for evaluating lung parenchyma, and MR imaging of lung parenchyma is limited by low intrinsic proton density of the lung parenchyma and is further limited by magnetic susceptibility mismatch at air/tissue interfaces that creates gradients that cause intravoxel dephasing and signal loss (Wild et  al. 2012). Novel MR techniques including short echo times, ultrafast turbo-spin-echo acquisitions, projection reconstruction techniques, breath-hold imaging, ECG triggering, contrast agents (perfusion imaging, aerosols, oxygen), and hyperpolarized noble gas imaging allow both anatomical and functional imaging of the lung parenchyma. Several studies have explored the potential of MR to image (a) acute alveolar processes in chronic infiltrative lung disease, (b) detection and characterization of pulmonary nodules, (c) detection, characterization, and follow-up of pneumo-

39  MRI in the Assessment of Cardiopulmonary Interaction

nia, (d) differentiation of obstructive atelectasis from non-obstructive atelectasis and infarctions, (e) measure the lung water content (f) assess progression of interstitial lung disease, and (g) evaluate inflammation and infection in cystic fibrosis (Nakai et al. 2008; Fain et al. 2010; Hochhegger et  al. 2011; Capaldi et  al. 2015; Mamata et  al. 2012; Voskrebenzev et al. 2018; Altes et al. 2007; Bannier et al. 2010; Horak et al. 2007; Wielpütz et al. 2013; Mathew et al. 2011). Carefully chosen sequences can provide anatomical detail with enhanced tissue characterization. When combined with perfusion imaging, MR can provide information at the molecular level that can help differentiate between inflammation and infection and has the potential to assess treatment response earlier than CT scans (Voskrebenzev et  al. 2018). The pharmacokinetic parameters combined with structural imaging and enhancement characteristics can help differentiate inflammation, infection, and malignancy (Tao et al. 2016; Altes et al. 2007; Horn et  al. 2010; Koyama et  al. 2008). These techniques can also be used to image chronic bronchitis, bronchiectasis, asthma, and emphysema (Wild et al. 2012; Eichinger et al. 2010). Novel techniques such as Fourier Decomposition, Arterial Spin Labelling and proton imaging are under investigation for evaluation of pulmonary ventilation and perfusion without the administration of contrast (Bauman et al. 2013). These techniques combine static and dynamic MR imaging without the use of intravenous contrast and enable repeat imaging over time which can provide functional imaging of the lung, and the potential to provide information about regional lung function, including ventilation, perfusion, V/Q ratio, intrapulmonary oxygen partial pressure (PO2), gas exchange, and spirometric and biomechanical parameters at a lobar and alveolar level. They also have the ability to quantify disease processes, identify imaging phenotypes, help identify disease early and can aid in monitoring therapeutic interventions (Kjørstad et al. 2014; Capaldi et al. 2017; Kaireit et al. 2018). They can potentially be used to identify imaging signatures that could correlate to phenotypes and help develop disease-specific biomarkers.

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Ventilation (Gas Exchange) Conventional MR imaging has limited value in evaluating lung parenchyma due to the paucity of protons (1H) in lung parenchyma; however, by the inhalation of hyperpolarized gases (3Helium or 129Xenon) the signal can be enhanced many fold allowing for evaluation of pulmonary ventilation and alveolar microstructure. Alveolar microstructure is evaluated by measuring restrictions in 3He diffusion that depend on the alveolar size. The size and exchange rate of different pulmonary compartments, alveoli, septal tissue, and RBC, can be evaluated by examining the kinetics of different chemical shift components in the 129 Xe spectrum. Most of these measurements are possible from a single inhalation of hyperpolarized gas. This technique requires the use of a polarizer, special coils, and software modifications to conventional scanners (Matsuoka et  al. 2009; Biederer et al. 2014; Fain et al. 2010). Laser-polarized 3He MR images have very high SNR with superb images of ventilation and ventilation defects. 129Xe on the other hand due to lipid solubility and transmission across blood barrier allows imaging of all three compartments of the lung (airspace, septa, and red blood cells) and thus can map both ventilation and perfusion. This feature makes it attractive to image lung parenchymal abnormalities and assess progression and response to treatment (Fain et al. 2010; Mathew et  al. 2011; Kirby et  al. 2014). The hyperpolarized gases require a well-practiced and well-defined workflow in combination with special coils and ultrafast MR sequences, as there is relatively fast in vivo depolarization of hyperpolarized nuclei (on the order of 10–20 seconds) and lack of magnetization recovery. Ventilation also can be mapped using noncontrast techniques such as Fourier decomposition and ASL; however, these techniques are not currently quantitative, but can be repeated multiple times, which makes them desirable to assess treatment and interventions (Kjørstad et al. 2014; Capaldi et al. 2017; Guo et al. 2017). Quantitative ventilation can be assessed by calculating apparent diffusion coefficient value, which is a measure of gas diffusion across the

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Fig. 39.3  Volume rendered CT scan images (a) 1  year after right pneumonectomy, (b) 5 years after pneumonectomy showing left lung increase in volume, (c) volume rendered CT scan images showing equal distribution of

vascularity, (d) acinar airway dimensions obtained from hyperpolarized 3He study, demonstrating uniformity of the acinar radius, representing lung growth

alveolar capillary membrane and serves as a surrogate for gas distribution that is altered by changes in the lung microstructure and thus indirectly gives information about changes in function. The average distance traveled by the hyperpolarized gas in a given time is determined by the diffusion coefficient D, and is specific to that gas or gas mixture. For example the D of 3He gas under standard conditions (and without restricting wall and barriers), D is 2.05  cm2/sec and approximately 0.88  cm2/sec in an atmospheric concentration. However D is much smaller than predicted from free diffusion due to diffusion hindrance by the lung microstructure itself. ADC appears to be a sensitive and reproducible marker for early detection and progression of disease and other processes affecting the size of alveoli and small airways. It thus can be used to calculate pulmonary microstructure (alveolar size and wall thickness), regional oxygen partial pressure, regional oxygen uptake, and V/Q matching in patients with emphysema, interstitial lung disease, and lung regeneration after

pneumonectomy or partial resection (Van Beek et al. 2004; Fain et al. 2010; Butler et al. 2012) (Figs. 39.3 and 39.4). Oxygen-sensitive imaging can be used in conjunction with 3He MR to destroy its signal as it shortens the T1 of 3He. Therefore areas of lung with high PO2 will lose signal more rapidly than those with lower oxygen concentrations. This function can then be used to indirectly map oxygen partial pressures quantitatively in the lungs and also assess regional oxygen uptake and V/Q Alveolar–capillary transfer of oxygen. Interrupted uptake of oxygen from the alveoli into the blood due to either pulmonary arterial obstruction or a significant diffusion defect can be detected as an area of abnormally highV/Q using the oxygen-­sensitive 3He MR technique (Kirby et al. 2014). Molecular oxygen, which is weakly paramagnetic, can be used as an MR agent to assess ventilation in proton MRI. The diffusion of oxygen from the alveoli into the capillary blood and binding to hemoglobin results in a reduction in

39  MRI in the Assessment of Cardiopulmonary Interaction

627

a

b

c

d

e

f

RV

FRC

RV

Fig. 39.4  Demonstration of “opening volume” effects (a–c). A ventilation defect is seen when inhalation is started from a low lung volume (RV) and reverses after

starting the 129Xe inhalation from a higher lung volume (FRC). This ventilation defect was reproducible 1 week after the initial scan (d–f)

the T1 of the blood and thus an increase in lung signal on a T1-weighted sequence. By subtracting an image acquired with room air from one acquired with 100% oxygen, oxygen-enhanced MR lung scans are obtained. Unlike hyperpolarized 3He, in which the signal is obtained directly from the gas itself, the signal obtained using molecular oxygen as a ventilation agent results from its paramagnetic effect on the alveolar capillary blood and interstitial water. Hyperpolarized 129Xe readily diffuses into interstitial tissues and alveolar blood, therefore MR spectroscopic techniques can be used to measure separately the 129Xe signal in alveolar gas, interstitial parenchyma, and alveolar capillary blood. By following the diffusion of the gas from the alveoli into the blood and/or the intersti-

tium, the hyperpolarized 129Xe scans have the potential to map regional ventilation, perfusion, and V/Q all within a single examination. Moreover, the kinetics of gas transfer from airspace to capillary could potentially be used to calculate a host of other parameters, including alveolar wall thickness, capillary blood volume, mean alveolar transit time, and pulmonary perfusion. These techniques are still in the research arena and translation to clinical care will need improvement in workflow and improved patient compliance. In lung diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis (CF), ventilation defects are apparent in regions that the inhaled gas cannot access (Fain et al. 2010).

R. R. Gill and S. Patz

628

Heart Disease MRI is a valuable complementary imaging modality to echocardiography in the evaluation of both congenital and acquired heart disease. MR protocols for evaluation of valves include velocity-encoded sequences targeted towards the valve of interest. Cine SSFP images are utilized to evaluate morphology and valvular regurgitation/stenosis and quantify ventricular function and volumes. Delayed enhancement can be used to evaluate myocardial scar or cardiomypathies. Velocity-encoded phase contrast imaging can quantify both regurgitation and stenosis. On MRI, regurgitation is graded as mild (45%). The velocity of stenotic jet is measured, and using this the pressure gradient can be calculated using modified Bernoulli equation, Δp = 4v2. Aortic stenosis is characterized by restricted systolic opening of the leaflets, with systolic flow acceleration. The leaflets are thickened and calcified. The valve morphology is also exquisitely demonstrated using MRI, including abnormalities such as bicuspid, quadricuspid, and unicuspid valves. Aortic regurgitation is seen as a regurgitant jet in diastole. Pulmonic stenosis is identified during systole and regurgitation in diastole. MRI is the most valuable imaging modality for the quantification of pulmonary valvular abnormalities, mitral, and tricuspid stenosis. MRI also evaluates the consequences of valvular abnormalities such as ventricular hypertrophy, dilation, and systolic dysfunction (Revel et al. 2012; Stein et al. 2008). MRI is the modality of choice for evaluation of congenital cardiac abnormalities and is especially useful for adults who had pediatric congenital heart surgery (Hsiao et al. 2012). MRI is superior to echocardiograpy for evaluation of right ventricular volumes. It also is useful in the evaluation of surgically placed shunts in these patients including aortopulmonary shunts (Blalock-Taussing, modified Blalock-Taussing, Potts, Waterston), Glenn shunt (SVC to right pulmonary artery), and Fontan shunt (IVC to right pulmonary artery). MRI is a

good imaging modality in the evaluation of coronary artery abnormalities, both congenital anomalies and also atherosclerotic disease. It also allows functional assessment of the myocardium. Lung imaging can be added to the cardiac sequences, which allows mapping of cardiopulmonary interactions both in congenital valvular disease and also in acquired heart disease.

Imaging of Lung Mechanics Chest wall and lung mechanics can be assessed by MRI. The high temporal and spatial resolution of the volumetric MR image data sets can be used to obtain regional rather than global spirometric parameters and other measures of lung mechanics. Potential uses include the mapping of abnormal regional compliance in patients with pulmonary fibrosis or emphysema (Biederer et  al. 2012b; Scheffler et  al. 2010b). Two MR approaches have been used to quantify tissue deformation and localized lung inflation: tissue tagging to track lung motion and displacement of vector maps derived from the registration of serial images acquired during breathing by using the pulmonary vasculature and parenchymal structures as inherent spatial markers. Mapping of regional lung mechanics using these new techniques can add unique, previously unavailable functional information beyond anatomy and structure.

Summary Imaging of cardiopulmonary interactions can be high yield and provide vital prognostic information that can help design management strategies. Imaging can be accomplished by the administration of contrast agents by using DCE-MRI, or non-contrast images, such as ASL, FD, and inhaled hyperpolarized gases. Translation to clinical care needs optimization of MR protocols to include functional sequences, optimized to disease states and well-defined workflow.

39  MRI in the Assessment of Cardiopulmonary Interaction

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Part VI The Tools: Interaction

Respiratory Function of Hemoglobin: From Origin to Human Physiology and Pathophysiology

40

Connie C. W. Hsia

Abbreviations 2,3-BPG 2,3-bisphosphoglycerate AE-1 Anion exchanger-1 ATP adenosine triphosphate BYA Billion years ago CA Carbonic anhydrase CarHb Carbamylated hemoglobin CO Carbon monoxide CO2 Carbon dioxide Epo Erythropoietin Fe+2 Ferrous cation, a reduced state of iron Fe+3 Ferric cation, an oxidized state of iron G6PD Glucose-6-phosphate dehydrogenase H+ Proton, hydrogen ion H2S Hydrogen sulfide HA High altitude Hb A Human adult hemoglobin Hb F Human fetal hemoglobin Hb M Hemoglobin variant associated with methemoglobinemia Hb S Sickle cell hemoglobin Hb Hemoglobin

C. C. W. Hsia (*) Department of Internal Medicine, Pulmonary and Critical Care Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA e-mail: [email protected]

Hb(II)

Hemoglobin containing iron in the reduced ferrous (Fe+2) state Hb(II)O2 Oxyhemoglobin containing iron in the reduced ferrous (Fe+2) state Hb(III) Methemoglobin containing iron in the oxidized ferric (Fe+3) state Hb(III)NO Heme-nitrosylated methemoglobin with its iron in the oxidized ferric (Fe+3) state HbCO Carboxyhemoglobin HCO3− Bicarbonate anion HIF Hypoxia-inducible factor NADP+ Nicotinamide adenine dinucleotide phosphate (oxidized form) NADPH Nicotinamide adenine dinucleotide phosphate (reduced form) NHE Sodium-proton (Na+/H+) exchanger NO Nitric oxide NO2 Nitrite NO3 Nitrate O2 Oxygen ODC Oxyhemoglobin dissociation curve P50 Partial pressure of oxygen at 50% saturation of the heme-binding sites on hemoglobin PCO2 Partial pressure of carbon dioxide PO2 Partial pressure of oxygen ROS Reactive oxygen species SNO S-nitrosothiol

© Springer Nature Switzerland AG 2021 S. Magder et al. (eds.), Cardiopulmonary Monitoring, https://doi.org/10.1007/978-3-030-73387-2_40

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Introduction

C. C. W. Hsia

in extreme environments similar to the modern extremophiles found near geothermal vents and Hemoglobin is much more than an oxygen (O2) the methanogens found in marine sediments and carrier. Hemoglobin packaged inside erythro- the Earth’s crust (Gribaldo and Brochier-Armanet cytes stores, delivers, coordinates and actively 2006). These ancient lithotrophs (“rock-eaters”) regulates the exchange of multiple gases includ- utilized inorganic electron donors including ing O2, carbon dioxide (CO2) and nitric oxide hydrogen, carbon monoxide (CO), ammonia, (NO) among distant sites of uptake, production, nitrite, sulfide (H2S) and iron in chemiosmosis to utilization and elimination. Hemoglobin traces generate transmembrane electrochemical gradiits origin to the earliest anaerobic prokaryote that ents and produce adenosine triphosphate (ATP). ingested metals from rocks to produce hemopro- Eventually, a molecular cage, the porphyrin ring, teins and facilitate non-O2-based cellular respi- evolved to trap these ions. A porphyrin ring conration, only later acquiring O2-binding ability taining a central iron molecule became heme; as atmospheric O2 concentration rose. In multi-­ one that contained a central magnesium molecule cellular organisms, some hemoprotein-producing became chlorophyll (Fig.  40.1). Polypeptides cells either extruded their product or detached to became associated with heme in order to modify enter the circulation, becoming erythrocytes and its function, producing hemoproteins. Ancestral co-evolving with the microvascular system. Over hemoproteins were cytochromes that reduced time, hemoglobins acquired features favored by nitrite, NO and H2S (Hsia et al. 2013). natural selection (adaptation), co-opted existing features for purposes other than originally intended (exaptation), shed useless features via Hemoglobin as Oxygen Carrier negative selection (disaptation) and struck compromises to satisfy competing environmental The enduring cyanobacteria were initially anaerand organismal constraints (trade-off). The sheer obic H2S oxidizers (de Wit and van Gemerden diversity of hemoproteins across species and 1987) that formed enormous fossil colonies (strohuman hemoglobin variants directly reflects the matolite) near shallow water. Around 3.5 BYA, selection pressures for facilitated gas transport. cyanobacteria acquired chlorophyll for photosynThis article surveys the anaerobic origin and nat- thesis, producing water and O2. The O2 was scavural selection of hemoglobin as an O2 carrier, the enged by ferrous iron in Earth’s crust, producing sequestration of hemoglobin within erythrocytes the iron oxides seen in the geologic strata called allowing a multitude of interactions within and banded iron formations, while free O2 entered among subunits, with erythrocyte metabolism, the atmosphere, causing the first and possibly and with the microvasculature to enhance gas the greatest mass extinction ~2 BYA, termed transport efficiency, and how the evolutionary his- the Oxygen Holocaust (de Duve 1996). Species tory of hemoglobin informs its integrated respira- survived by: (a) hiding under deep sea, soil or tory function in human physiology, adaptation to rock while remaining anaerobic, (b) developing exercise and hypoxia, and clinical medicine in mechanisms to detoxify and eliminate O2 and (c) terms of understanding the pathophysiological co-­opting O2 for energy production. disturbances in hemoglobin quantity, quality and Detoxification of O2 is seen in anaerobic regulation of its function. worms that possess an ultrahigh-affinity hemoglobin (P50 0.0001  Torr) as an O2 scavenger and a deoxygenase (Minning et al. 1999). After Anaerobic Origin of Hemoproteins binding O2, the Fe+2 in hemoglobin is oxidized to Fe+3 methemoglobin in reaction with endogThe ancestral cell (so-called Last Universal enous NO; the bound O2 is converted to nitrite Common Ancestor) appeared ~4 billion years ago (NO2) and nitrate (NO3−) and excreted or used to (BYA) in an anoxic Earth. These organisms lived regenerate NO via reverse reducing reaction with

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Fig. 40.1  Upper: Cellular respiration in ancient bacteria harnesses chemical energy from electron transfer via transitional metals. A porphyrin-based molecular cage traps an iron molecule to produce heme. Polypeptides modify

the function of heme, producing hemoproteins. Lower: A porphyrin ring containing a central iron molecule became heme; one that contains a central magnesium molecule became chlorophyll. (Adapted from Hsia et al. 2013)

methemoglobin. Other cytochromes developed to utilize O2 as an electron acceptor. This evolutionary shift is deduced from extant hemoglobin variants in bacteria and yeast that support both aerobic and non-aerobic (nitrogen-based) respiration (Weber and Vinogradov 2001), the multifunctional hemoglobins in deep sea tubeworms living near thermal vents that reversibly bind both H2S and O2, (Zal et al. 1998), and the protoglobins of archaea species that reversibly bind O2, CO and NO (Pesce et  al. 2013). Several amino acid residues that covalently bind heme, and cysteine residues that form thermostable disulfide bridges (Freitas et al. 2004), are highly conserved and responsible for O2 detoxification coupled to NO. Thus, ancestral hemoglobins are highly flexible in their mode of redox energy production and in cross-protection against a combination of thermal, nitrosative and oxidative stress. The basic

motif of hemoglobin-binding sites is universally adaptable to different gas molecules. As ambient oxygenation increased, the inherent capacity for O2 binding by hemoglobin was preferentially exploited. Hemoglobins consist of monomers or oligomers of a basic single-domain O2-binding subunit (M.W. 15–17 kDa), with 153 residues, 8 α-helices and a hydrophobic interior. Variants of the subunit exist in muscle (myoglobin), nerve (neuroglobin) and cells (cytoglobin). Single-chain hemoglobins exist in bacteria, algae, protozoa and plants, while giant hemoglobin complexes exist in nematodes, mollusks, crustaceans and earthworms (Terwilliger 1980). The O2-binding metal may be iron (hemoglobin) or copper (hemocyanin). From an ancestral globin gene, hemoglobin diverged from cytoglobin and n­ euroglobin ~800 million years ago. Later, it diverged from myoglobin,

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and the α- and β-globin genes segregated onto separate chromosomes (Pesce et al. 2002). Most mutations occur on the β-chain, while the α-chain is more stable. All known aerobic respiratory pigments are sensitive to hypoxia under regulation by hypoxia-­inducible factor (HIF)-erythropoietin (Epo) signaling (Jelkmann 2007).

remaining heme groups), Bohr and Haldane shifts (sensitivity to pH and CO2) (Sterner and Decker 1994), allosterism (altered O2 affinity due to conformational changes brought about by the binding of another molecule to a site on hemoglobin other than the heme-binding site) (Strand et al. 2004), and high thermostability (Mangum 1998).

Erythrocytes as Hemoglobin Carrier

Bohr and Haldane Effects

As organisms became larger and ventilatory and circulatory systems evolved, some hemoglobin-­ producing cells extruded their products into the circulation while other became mobile erythrocytes to sustain the delivery function. Free hemoglobin circulates in the hemolymph of invertebrate species. Large free hemoglobin molecules increase plasma osmotic pressure and viscosity (Snyder 1977). Giant invertebrate hemoproteins (>100 subunits) exhibit high O2 affinity and act as an O2 reservoir but are incapable of regulating tissue O2 exchange (Mangum 1998). As O2 demands increased, smaller more versatile hemoglobins with fewer subunits, lower blood viscosity and higher O2 transport capacity were favored by natural selection. However, small hemoglobins are poorly retained; intravascular half-life of free human hemoglobin tetramers (2 α- and 2 β-subunits) is only ~4  hr (Bleeker et al. 1992). Retention of high concentrations of small hemoglobin was achieved by packaging it within mobile erythrocytes. The postulated origins of erythrocytes include fat cells lining the hemolymph channel, osmoregulatory epithelial cells and peritoneal endothelial cells (Glomski and Tamburlin 1989, 1990; Glomski et al. 1992, 1997; Paul et al. 2004). Confining hemoglobin within erythrocytes also facilitates interactions among subunits and with the erythrocyte membrane and blood; these interactions optimize not just O2 transport but also transport of CO2 and NO. Mechanisms for regulating O2-binding kinetics include: co-­operativity (O2 binding to one heme group alters the molecular conformation of the hemoglobin tetramer and increases the likelihood of O2 binding to the

Increasing blood CO2 concentration (lower pH) reduces hemoglobin affinity for O2, leading to a higher P50 (right shift of oxyhemoglobin dissociation curve, ODC), i.e., the Bohr effect that favors O2 unloading. Reciprocally, increasing blood oxygenation reduces hemoglobin affinity for CO2, i.e., the Haldane effect that favors CO2 release. Both effects arise from interactions with the erythrocyte (Fig.  40.2) (Hsia et al. 2016): CO2 from tissue diffuses into capillary erythrocytes and is converted via carbonic anhydrase (CA) to carbonic acid (H2CO3) that dissociates into bicarbonate (HCO3−) and proton (H+); the latter binds histidine residues on globin chain to stabilize the deoxyhemoglobin (Tense state) conformation, thereby facilitating unloading of O2. CO2 also directly binds oxyhemoglobin forming carbaminohemoglobin, a reaction that also facilitates O2 release. While H2CO3 can diffuse across the cell membrane, excess HCO3− anions are shuttled out of the cell via the membrane anion exchanger-1 (AE-1) in exchange with chloride (Cl-) (McMurtrie et  al. 2004), and excess H+ ions are shuttled out of the cell via the sodium (Na+)/proton (H+) exchanger (NHE) (Pedersen and Cala 2004; Matteucci and Giampietro 2007). The reverse reactions occur in pulmonary capillaries where CO2 diffuses along its pressure gradient into alveolar air; the decrease in PCO2 facilitates O2 loading onto heme, which stabilizes the oxyhemoglobin (Relaxed state) configuration and in turn favors unloading of CO2 from hemoglobin for elimination. Thus, changes in blood PCO2 and PO2 are reciprocally coupled; binding of one ligand regulates binding and release of the other.

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Fig. 40.2  Coupling of O2 and CO2 exchange within erythrocytes. AE-1 anion exchager-1, CA carbonic anhydrase, CarbaminoHb carbamino-hemoglobin, Cl- chloride anion, H+ proton, Hb-O2 oxyhemoglobin, Hb deoxyhemoglobin, H2CO3 carbonic acid, HCO3− bicarbonate anion, NHE Sodium/ proton (Na+/H+) exchanger. See text for explanation (under Erythrocytes as hemoglobin carrier: Bohr and Haldane effects). (Adapted from Hsia et al. 2016)

2,3-Bisphosphoglycerate

Effects of Temperature

Oxygen affinity is expressed by its partial pressure at which half of the heme-binding sites are saturated (P50) (Fig.  40.3a). As reviewed previously (Hsia 1998), at a given alveolar O2 tension, the pressure gradient from alveolar air to blood drives O2 loading onto hemoglobin, while the pressure gradient from blood to tissue mitochondria drives O2 unloading from hemoglobin. Shifting P50 alters the balance between loading and unloading. Erythrocytic glycolysis produces the intermediary compound 2,3-­bisphosphoglycerate (2,3-BPG) that binds deoxyhemoglobin between α-1 and β-2 globins stabilizing the deoxy(tense) conformation, thereby increasing P50 and favoring O2 unloading. Metabolic stress (e.g., increased temperature, hypermetabolism, moderate hypoxia) increases erythrocytic glycolysis and in turn 2,3-BPG production and O2 unloading (Fig.  40.3a). Alkalosis, reduced metabolism and extreme hypoxia have the opposite effects that favor O2 loading (Fig.  40.3b). By coupling cellular metabolic state to function, erythrocytes effectively act as an O2 sensor that utilizes their glycolytic by-product to gauge and optimize regional O2 delivery.

Circulating blood is exposed to large temperature changes from baseline core temperature (37 °C) to skin temperature (33 °C and lower especially in cold air or water without insulation), and rising to >39.5 °C in strenuously exercising muscles and with fever. An elevated temperature favors O2 unloading (higher P50) to meet tissue metabolic demands. In hypothermia, O2 is bound more tightly to hemoglobin (lower P50) and O2 unloading is reduced, matched by a corresponding reduction in metabolic activity and O2 demand (Mairbaurl and Weber 2012).

Erythrocyte as a Source of Bioactive NO Beyond the classical concept of hemoglobin as NO scavenger due to a high NO-binding affinity of heme, erythrocytes possess intrinsic NO synthase activity (Kleinbongard et  al. 2006) and nitrite reductase activity (Fens et  al. 2014). Nitrite–hemoglobin reactions preserve and modulate NO bioactivity under hypoxia (Sun et  al. 2019; Schmidt and Feelisch 2019; Huang

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a

∆P for unloading

∆P for loading ∆P

O2 content or saturation

∆P

H+ CO2 2,3-BPG Temperature Exercise Moderate HA Alveolar air

P50 P50

b

∆P

O2 content or saturation

∆P

O2 tension

Alkalosis Extreme HA

Alveolar air

P50 P50

O2 tension

Fig. 40.3  Regulation of O2 loading and unloading via adjustment of the oxyhemoglobin dissociation curve. (a) The P50 (O2 tension at 50% saturation of heme-binding sites) balances partial pressure gradients (∆P) for O2 loading from alveolar air onto hemoglobin and O2 unloading from hemoglobin to tissue mitochondria (PO2 4000  m), excessive hyperventilation leads to severe respiratory alkalosis that increases O2 affinity (lower P50) (West, 1983). In healthy fit subjects during a simulated ascent to Mt. Everest (barometric pressure 253  mmHg) (Wagner et  al. 2007), standard P50 (at pH  7.40, 37  °C) increased from sea level to the summit; the effect was balanced by progressive hypocapnia and alkalosis such that in vivo P50 remained unchanged. The reduced O2 saturation was balanced by an increased O2 extraction. In chronic HA exposure, upregulated erythropoiesis leads to polycythemia and susceptibility to chronic mountain sickness in lowlanders living at HA and native Andean highlanders whose ancestors migrated to HA ~15,000 years ago (Gassmann et al. 2019). In contrast, hemoglobin concentration in Tibetans is often within the normal sea-level range (Simonson et  al. 2015) accompanied by higher ventilatory and exercise capacities at HA, brisk hypoxic ventilatory responses, larger lung volumes, higher O2 saturation and lung diffusing capacities, higher offspring survival at HA and a lower incidence of chronic mountain sickness compared to Han lowlanders (Simonson et  al. 2015; Wu et  al. 2005; Beall 2007). There is continuing debate as to whether the superior phenotypic adap-

tation in Tibetans is inherited or acquired. Animals indigenous to HA typically possess high O2 affinity hemoglobins (lower P50) (Sillau et  al. 1976; Snyder 1985; Jurgens et al. 1988). A low P50 and absence of polycythemia or chronic mountain sickness are considered hallmarks of genotypic adaptation (Beall 2007). The P50 is typically normal in Andeans and either normal or lower in Tibetans compared to ethnically matched lowlanders (Beall 2007; Simonson et al. 2014). A large genome-wide association study found no preferential selection of HA adaptive genes among native Andeans (Gazal et al. 2019). In contrast, Tibetans inherited beneficial HIF pathway genes from archaic Denisovans who lived in Siberia ~40,000 years ago, including a gain-of-­function mutation in EGLN1 associated with higher O2 affinity (lower hypoxia sensitivity), an EPAS1 haplotype favoring anaerobic metabolism, and a PPARA haplotype favoring reduced fat oxidation (Simonson et al. 2012; Ge et al. 2015). As the descendents of Denisovans migrated to the Tibetan Plateau, these favorable traits were preferentially retained and likely contributed to their superior phenotypic features at HA.

Hemoglobinopathy More than 1,000 human hemoglobin mutations exist involving insertion, deletion or substitution of amino acids on the globin chain; most of these are asymptomatic. Clinically significant mutations are classified into hereditary or acquired disorders of hemoglobin production, structure and/or function (Table  40.1). The major hereditary forms are (a) sickle cell syndromes (Hb S), (b) α- and β-thalassemias, (c) unstable hemoglobins, (d) high O2 affinity and (e) low O2 affinity variants. Sickle cell, thalassemia and unstable hemoglobins accelerate erythrocyte destruction leading to hemolytic anemia. Sickle cells with impaired deformability can obstruct microvessels, causing tissue hypoxia, acidosis and necro-

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Table 40.1  Clinical alterations of hemoglobin structure and function and related erythrocyte abnormalities Disorder Reduced production α-Thalassemia β-Thalassemia minor β-Thalassemia major

Myelodysplastic syndromes Excess production Polycythemia

Abnormal structure Sickle cell anemia (Hb S disease) Spherocytosis

Schistocytosis

Altered function Unstable hemoglobin variants High O2 affinity variants

Low O2 affinity variants Methemoglobinemia

Carboxyhemoglobinemia Post-translational modification of hemoglobin

Etiology

Manifestations

Deletion/mutation of α-globin genes Heterozygous mutation/deletion of β-globin genes Homozygous mutation/deletion of β-globin Destruction of bone marrow erythroid progenitors Acquired reduction in α-globin gene expression

Anemia, hemolysis, splenomegaly, iron overload, bone deformities, heart failure Increased 2,3-BPG and P50

Anemia, neutropenia, thrombocytopenia

Polycythemia rubra vera, hematological malignancy Acquired—chronic hypoxemia, blood transfusion

Elevated blood viscosity, thromboembolism

Amino acid substitution causing hemoglobin polymerization, erythrocyte distortion and rigidity Hereditary spherocytosis Southeast Asian ovalocytosis Acquired autoimmune hemolytic diseases Microangiopathic hemolysis Infection Hematologic malignancy

Hemolytic anemia, tissue necrosis, vaso-occlusive crises, increased 2,3-BPG and P50 Hemolytic anemia Increased 2,3-BPG and P50

Heinz body hemolytic anemia G6PD deficiency Hereditary persistence of fetal hemoglobin (Hb F) Various amino acid substitutions that stabilize oxyhemoglobin conformation Various amino acid substitutions that stabilize deoxyhemoglobin conformation Congenital hemoglobin M or Cytochrome b5 reductase deficiency Acquired—drug and toxin exposure Increased endogenous CO production, smoking, CO poisoning Non-enzymatic glycation, e.g., Hemoglobin A1c Deamination, e.g., hemoglobin Providence Amino-terminal acylation by aspirin-like diacyl esters Amino-terminal carbamylation by cyanate

Hemolysis due to globin precipitation or oxidant and drug exposure Often associated with sickle cell disease and thalassemia Erythrocytosis, normal O2 saturation

Erythrocyte fragmentation

Cyanosis, low O2 saturation Normal hemoglobin level Cyanosis, normal hemoglobin level or mild anemia, responds to methylene blue O2 deficit, headache, dizziness, confusion, dyspnea Marker of glycemic control Reduced P50, erythrocytosis Reduced P50, no significant clinical manifestation Marker of uremia and adequacy of dialysis

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sis. Erythrocyte deformability is also impaired in hereditary spherocytosis and ovalocytosis where abnormal cell shapes and mechanics predispose to hemolysis. Schistocytes are seen in microangiopathic hemolysis due to infection or malignancy. In all hemolytic conditions, acidosis and increased erythrocyte 2,3-BPG reduce hemoglobin O2 affinity (higher P50) to preserve O2 unloading in the periphery. Several hemoglobinopathies, e.g., sickle cell trait, hemoglobins C, E, F, thalassemias, and mutations that alter erythrocyte cytoskeleton or membrane surface proteins, confer survival advantage and protection from severe malaria infection; this is an important example of coevolution and tradeoff between the Plasmodium falciparum parasite and the human hosts native to malaria-endemic regions. Numerous mechanisms of protection have been proposed, including accelerated hemolysis and splenic phagocytosis of infected erythrocytes, inhibition of intra-erythrocytic parasite growth by O2-­dependent hemoglobin polymerization (Archer et al. 2018), induction of hemeoxygenase-1 to catabolize heme and produce CO which protects the endothelium and preserves microvascular and blood–brain barrier integrity (Weinberg et al. 2008; Ferreira et al. 2011) and acquired antimalaria immunity (Williams et  al. 2005), among others.

Unstable Hemoglobins Unstable hemoglobins predispose to erythrocyte oxidative damage and hemolysis. In the rare congenital Heinz body hemolytic anemia (Gallagher 2015), globin chain mutations cause structural alterations, leading to altered solubility and intracellular precipitates (Heinz bodies) that bind to erythrocyte membrane, impair membrane deformability, increase permeability and predispose to hemolysis. More common is the X-linked recessive glucose-6-phosphate dehydrogenase (G6PD) deficiency (Frank 2005). G6PD mediates the first reaction in the pentose phosphate pathway that reduces NADP+ to NADPH; the latter prevents intra-erythrocyte ROS buildup. As erythrocytes lack other NADPH-producing

enzymes, mutations that cause G6PD deficiency promote ROS-induced damage to hemoglobin and hemolysis upon exposure to infection, certain drugs, toxins and fava beans. G6PD deficiency also weakens erythrocyte membrane and shortens erythrocyte life span, rendering the cell an unsuitable host for the life cycle of P. falciparum, thereby conferring malaria resistance to individuals native to malaria-endemic regions (Cappadoro et al. 1998).

 emoglobin Variants with Altered O2 H Affinity Human fetal hemoglobin (Hb F, P50 19.7 mmHg) is adapted to uterine hypoxia; Hb F also resists polymerization and sickling. Patients with sickle cell anemia and thalassemia often exhibit elevated Hb F, a hereditary trait that attenuates the complications of tissue hypoxia resulting from recurrent hemolytic crises (Akinsheye et al. 2011). Almost 100 hereditary high-affinity hemoglobin variants are known, involving amino acid substitutions that stabilize oxyhemoglobin, leading to full O2 saturation but reduced tissue O2 supply, which can stimulate Epo production with secondary erythrocytosis (Wajcman and Galacteros 2005). Nearly 70 low-affinity hemoglobin variants are known, involving amino acid substitutions that stabilize deoxyhemoglobin. As pulmonary O2 loading is impaired and tissue O2 delivery is enhanced, patients can be asymptomatic or present with cyanosis, arterial O2 desaturation, secondary reduction in erythropoiesis and chronic normocytic anemia (Yudin and Verhovsek 2019). Animal studies of low-affinity hemoglobin demonstrate gain-of-function physiology with reduced left ventricular work, enhanced tissue oxygenation and utilization, and increased exercise capacity (Berlin et al. 2002; Shirasawa et al. 2003).

Carboxyhemoglobinemia Carbon monoxide (CO), a normal product of heme breakdown, binds heme with an affinity ~200 times that of O2, forming carboxyhemo-

40  Respiratory Function of Hemoglobin: From Origin to Human Physiology and Pathophysiology

globin (HbCO) and competitively displacing O2 from heme-binding sites. In addition, CO binding to one heme increases O2 affinity of the remaining heme-binding sites (Hlastala et al. 1976). CO poisoning causes tissue anoxia, nausea, vomiting, dyspnea, chest pain, confusion, seizures, loss of consciousness and death. Hyperbaric O2 therapy is often necessary to replace CO with O2 on hemoglobin.

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ing glycated hemoglobin (HbAlc), a widely adopted marker of glycemic control in diabetic subjects. HbA1c has a higher intrinsic O2 affinity (lower standard P50 at pH  7.40) than Hb A (Coletta et al. 1988); HbA1c also alters erythrocyte metabolism to increase 2,3-BPG which tends to increase P50 (Solomon and Cohen 1989). These counterbalancing actions result in little net change of baseline in vivo P50 among diabetic patients. An elevated HbA1c level can also lead to Methemoglobinemia overestimation of arterial O2 saturation by pulse oximetry (Pu et al. 2012). An increase in methemoglobin containing non-­ (b) Deamination: Hemoglobin Providence is a O2-­binding oxidized iron (Fe+3) may be heredirare hereditary variant where a β-chain aspartary or acquired. Hereditary forms may results agine substitutes for lysine and is later deamifrom deficiency of cytochrome b5 reductase nated to aspartic acid in vivo during the life enzyme in erythrocytes only (Type I) or in all span of the erythrocyte. These changes reduce cells (Type II) (Lorenzo et  al. 2011) or from hemoglobin affinity for 2,3-BPG, leading to Hemoglobin M disease, a variant methemoglobin high O2 affinity and secondary erythrocytosis. that stabilizes oxidized Fe+3 (Mansouri and Lurie Hemoglobin Providence and several other 1993). Having one Fe+3 heme also increases O2 hemoglobin variants interfere with HbA1c affinity of the remaining Fe+2 hemes in the same immunoassay, yielding falsely low values hemoglobin tetramer, further reducing O2 deliv(Newman et al. 2017). Presence of hemogloery and resulting in “functional anemia” even bin variants should be suspected in diabetic when blood hemoglobin concentration is normal. subjects when HbA1c level is inconsistent Acquired methemoglobinemia is more common with other measures of glycemic control. and associated with exposure to oxidant drugs (c) Acylation: Aspirin and similar diacyl esters and chemicals, including dapsone and other sulcan transfer the acyl group to the amino terfonamides, chloroquine, nitrates, nitrite, inhaled minal of hemoglobin resulting in increased NO, local anesthetics and aniline dyes. Patients O2 affinity (Bridges et al. 1975). In the presappear cyanotic and may be asymptomatic or sufence of a high glucose concentration, aspirin fer manifestations of tissue hypoxia. O2 saturaalso inhibits glycation of hemoglobin and prevents the associated conformational tion should be verified with blood gas analysis changes (Bakhti et  al. 2007). Functional (Stucke et al. 2006) as pulse oximetry values are impact of these interactions is considered falsely high and fail to improve following suppleminimal. mental O2 administration. Methylene blue, ascorbic acid and riboflavin are the standard treatment (d) Carbamylation: Carbamylated hemoglobin (CarHb) is formed by non-enzymatic reacfor methemoglobinemia >30% or patients who tion of hemoglobin with cyanate, a product remain symptomatic despite supplemental O2 of in vivo urea dissociation. CarHb level is therapy (Cefalu et al. 2020). dependent upon blood urea concentration and duration of urea exposure; higher levels are seen in chronic than acute renal failure Post-translational Modification of Hemoglobin (Stim et al. 1995). CarHb hinders hemoglobin binding to 2,3-BPG and increases O2 (a) Glycation: Glucose non-enzymatically reacts affinity. However, urea also directly alters with the free amino group of globin produchemoglobin structure by stabilizing

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2,3-BPG-hemoglobin, which reduces O2 affinity. Owing to the opposing effects, uremic patients do not exhibit increased hemoglobin O2 affinity (Monti et al. 1995). CarbHb is a marker for the adequacy of hemodialysis and correlates with neuropathic complications (Abdelwhab and Ahmed 2008). Carbamylation and glycation both involve free amino groups. In diabetic uremic patients, glycation of hemoglobin reduces CarHb at a given urea concentration, likely by decreasing the available free amino groups (Hammouda and Mady 2001).

Conclusions Ancestral hemoproteins arose to harness chemical energy from nitrogen- and sulfur-based redox reactions and only later pivoted to O2 detoxification and eventually aerobic respiration. As organismal O2 demands increased, intricate molecular interactions permit dynamic regulation of O2 uptake, storage and delivery by hemoglobin, coupled to that of CO2 and NO.  Erythrocytes greatly enhance the respiratory function of hemoglobin; both co-evolved with the microvasculature. The astounding number of hemoglobin variants attests to robust selection pressures for meeting the demands of O2 transport while minimizing trade-offs under various organismal and environmental constraints. Knowledge of the origin and physiology of hemoglobin facilitates understanding of its respiratory functions, pathophysiological disturbances and the compensatory responses to guide clinical management of disease and provide a robust foundation for therapeutic explorations, e.g., to correct specific mutations in hemoglobinopathies, manipulate allosterism to optimize O2 uptake and delivery in accordance with metabolic needs, develop effective hemoglobin or erythrocyte substitutes, or engineer hematopoietic stem cells into mature erythrocytes with normal hemoglobin function. Acknowledgment  The author acknowledges the support by National Heart, Lung and Blood Institute grant R01

HL134373. The content of this manuscript is solely the author’s responsibility and does not necessarily represent the official views of the funding agency.

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650 Jelkmann W.  Erythropoietin after a century of research: younger than ever. Eur J Haematol. 2007;78(3):183–205. Jurgens KD, Pietschmann M, Yamaguchi K, Kleinschmidt T.  Oxygen binding properties, capillary densities and heart weights in high altitude camelids. J Comp Physiol B. 1988;158(4):469–77. Kay M. Immunoregulation of cellular life span. Ann N Y Acad Sci. 2005;1057:85–111. Kleinbongard P, Schulz R, Rassaf T, Lauer T, Dejam A, Jax T, et  al. Red blood cells express a functional endothelial nitric oxide synthase. Blood. 2006;107(7):2943–51. König MF, Lucocq JM, Weibel ER.  Demonstration of pulmonary vascular perfusion by electron and light microscopy. J Appl Physiol. 1993;75(4):1877–83. Lee JC, Gimm JA, Lo AJ, Koury MJ, Krauss SW, Mohandas N, et al. Mechanism of protein sorting during erythroblast enucleation: role of cytoskeletal connectivity. Blood. 2004;103(5):1912–9. Lorenzo FR, Phillips JD, Nussenzveig R, Lingam B, Koul PA, Schrier SL, et  al. Molecular basis of two novel mutations found in type I methemoglobinemia. Blood Cells Mol Dis. 2011;46(4):277–81. Mairbaurl H, Schobersberger W, Oelz O, Bartsch P, Eckardt KU, Bauer C. Unchanged in vivo P50 at high altitude despite decreased erythrocyte age and elevated 2,3-diphosphoglycerate. J Appl Physiol (1985). 1990;68(3):1186–94. Mairbaurl H, Weber RE. Oxygen transport by hemoglobin. Compr Physiol. 2012;2(2):1463–89. Mangum CP. Major events in the evolution of the oxygen carriers. Amer Zool. 1998;38(1):1–13. Mansouri A, Lurie AA. Concise review: methemoglobinemia. Am J Hematol. 1993;42(1):7–12. Matteucci E, Giampietro O.  Electron pathways through erythrocyte plasma membrane in human physiology and pathology: potential redox biomarker? Biomark Insights. 2007;2:321–9. McMurtrie HL, Cleary HJ, Alvarez BV, Loiselle FB, Sterling D, Morgan PE, et  al. The bicarbonate transport metabolon. J Enzyme Inhib Med Chem. 2004;19(3):231–6. Meeson AP, Radford N, Shelton JM, Mammen PP, DiMaio JM, Hutcheson K, et al. Adaptive mechanisms that preserve cardiac function in mice without myoglobin. Circ Res. 2001;88(7):713–20. Minetti M, Agati L, Malorni W.  The microenvironment can shift erythrocytes from a friendly to a harmful behavior: pathogenetic implications for vascular diseases. Cardiovasc Res. 2007;75(1):21–8. Minning DM, Gow AJ, Bonaventura J, Braun R, Dewhirst M, Goldberg DE, et  al. Ascaris haemoglobin is a nitric oxide-activated ‘deoxygenase’. Nature. 1999;401(6752):497–502. Mohandas N, Chasis JA.  Red blood cell deformability, membrane material properties and shape: regulation by transmembrane, skeletal and cytosolic proteins and lipids. Semin Hematol. 1993;30(3):171–92.

C. C. W. Hsia Monti JP, Brunet PJ, Berland YF, Vanuxem DC, Vanuxem PA, Crevat AD.  Opposite effects of urea on hemoglobin-­oxygen affinity in anemia of chronic renal failure. Kidney Int. 1995;48(3):827–31. Nagababu E, Ramasamy S, Abernethy DR, Rifkind JM.  Active nitric oxide produced in the red cell under hypoxic conditions by deoxyhemoglobin-­ mediated nitrite reduction. J Biol Chem. 2003a;278(47):46349–56. Nagababu E, Chrest FJ, Rifkind JM. Hydrogen-peroxide-­ induced heme degradation in red blood cells: the protective roles of catalase and glutathione peroxidase. Biochim Biophys Acta. 2003b;1620(1–3):211–7. Newman CN, Litwin CM, Bowlby DA, Lewis KA, Paulo RC. Hemoglobin Providence (beta82 Lys > Asn, Asp) and lower-than-expected HbA1c in a nonadherent teenager with type 1 diabetes: a case report and literature review. Clin Case Rep. 2017;5(12):2000–2. Paul RJ, Zeis B, Lamkemeyer T, Seidl M, Pirow R.  Control of oxygen transport in the microcrustacean Daphnia: regulation of haemoglobin expression as central mechanism of adaptation to different oxygen and temperature conditions. Acta Physiol Scand. 2004;182(3):259–75. Pedersen SF, Cala PM.  Comparative biology of the ubiquitous Na+/H+ exchanger, NHE1: lessons from erythrocytes. J Exp Zoolog A Comp Exp Biol. 2004;301(7):569–78. Pesce A, Bolognesi M, Bocedi A, Ascenzi P, Dewilde S, Moens L, et  al. Neuroglobin and cytoglobin. Fresh blood for the vertebrate globin family. EMBO Rep. 2002;3(12):1146–51. Pesce A, Bolognesi M, Nardini M. Protoglobin: structure and ligand-binding properties. Adv Microb Physiol. 2013;63:79–96. Podoltsev NA, Zhu M, Zeidan AM, Wang R, Wang X, Davidoff AJ, et  al. The impact of phlebotomy and hydroxyurea on survival and risk of thrombosis among older patients with polycythemia vera. Blood Adv. 2018;2(20):2681–90. Pu LJ, Shen Y, Lu L, Zhang RY, Zhang Q, Shen WF.  Increased blood glycohemoglobin A1c levels lead to overestimation of arterial oxygen saturation by pulse oximetry in patients with type 2 diabetes. Cardiovasc Diabetol. 2012;11:110. Schmidt H, Feelisch M. Red blood cell-derived nitric oxide bioactivity and hypoxic vasodilation. Circulation. 2019;139(23):2664–7. Shirasawa T, Izumizaki M, Suzuki Y, Ishihara A, Shimizu T, Tamaki M, et  al. Oxygen affinity of hemoglobin regulates O2 consumption, metabolism, and physical activity. J Biol Chem. 2003;278(7):5035–43. Sidell BD, O’Brien KM. When bad things happen to good fish: the loss of hemoglobin and myoglobin expression in Antarctic icefishes. J Exp Biol. 2006;209(Pt 10):1791–802. Sidell BD, Vayda ME, Small DJ, Moylan TJ, Londraville RL, Yuan ML, et al. Variable expression of myoglobin among the hemoglobinless Antarctic icefishes. Proc Natl Acad Sci U S A. 1997;94(7):3420–4.

40  Respiratory Function of Hemoglobin: From Origin to Human Physiology and Pathophysiology Sillau AH, Cueva S, Valenzuela A, Candela E. O2 transport in the alpaca (Lama pacos) at sea level and at 3,300 m. Respir Physiol. 1976;27(2):147–55. Simonson TS, McClain DA, Jorde LB, Prchal JT. Genetic determinants of Tibetan high-altitude adaptation. Hum Genet. 2012;131(4):527–33. Simonson TS, Wei G, Wagner HE, Wuren T, Bui A, Fine JM, et al. Increased blood-oxygen binding affinity in Tibetan and Han Chinese residents at 4200  m. Exp Physiol. 2014;99(12):1624–35. Simonson TS, Wei G, Wagner HE, Wuren T, Qin G, Yan M, et  al. Low haemoglobin concentration in Tibetan males is associated with greater high-altitude exercise capacity. J Physiol. 2015;593(14):3207–18. Snyder GK.  Blood corpuscles and blood hemoglobins: a possible example of coevolution. Science. 1977;195(4276):412–3. Snyder GK, Sheafor BA. Red blood cells: centerpiece in the evolution of the vertebrate circulatory system. Amer Zool. 1999;39:189–98. Snyder LR. Low P50 in deer mice native to high altitude. J Appl Physiol (1985). 1985;58(1):193–9. Solomon LR, Cohen K.  Erythrocyte O2 transport and metabolism and effects of vitamin B6 therapy in type II diabetes mellitus. Diabetes. 1989;38(7):881–6. Sterner R, Decker H.  Inversion of the Bohr effect upon oxygen binding to 24-meric tarantula hemocyanin. Proc Natl Acad Sci U S A. 1994;91(11):4835–9. Stim J, Shaykh M, Anwar F, Ansari A, Arruda JA, Dunea G. Factors determining hemoglobin carbamylation in renal failure. Kidney Int. 1995;48(5):1605–10. Strand K, Knapp JE, Bhyravbhatla B, Royer WE Jr. Crystal structure of the hemoglobin dodecamer from Lumbricus erythrocruorin: allosteric core of giant annelid respiratory complexes. J Mol Biol. 2004;344(1):119–34. Stucke AG, Riess ML, Connolly LA.  Hemoglobin M (Milwaukee) affects arterial oxygen saturation and makes pulse oximetry unreliable. Anesthesiology. 2006;104(4):887–8. Sun CW, Yang J, Kleschyov AL, Zhuge Z, Carlstrom M, Pernow J, et  al. Hemoglobin beta93 cysteine is not

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Acid-Base and Hydrogen Ion

41

Sheldon Magder and Raghu R. Chivukula

Introduction Hydrogen (H+) ion concentration [H+] in biological solutions is a very small number. In normal arterial blood, [H+] is 0.00000004, or better written as 40 × 10−9. Because it is such a small number, [H+] traditionally is given as a negative inverted logarithm which gives pH  =  7.4. The advantage of the pH notation is that the very small [H+] is easier to visualize over the very large range of possible values in chemical reactions. Furthermore, the electrodes that are used to measure [H+] have a logarithmic output. However, in the clinical setting, the range of changes in [H+] is linear, and the pH notation can obscure significant changes of [H+] that occur in the small range of biological values. The normal pH of human blood at body temperature is 7.40 ± 0.02, and values between ~6.80 and ~ 7.80 are compatible with life at least transiently. These observations highlight both how low and how

S. Magder (*) Royal Victoria Hospital (McGill University Health Centre), Departments of Critical Care and Physiology McGill University, Montreal, QC, Canada e-mail: [email protected] R. R. Chivukula Harvard Medical School, Massachusetts General Hospital, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Boston, MA, USA e-mail: [email protected]

tightly blood normal [H+] is controlled. If [H+] is used instead of pH, the concentration varies from 0.00000038  mM to 0.00000042  mM, which is orders of magnitude less than most of the commonly measured serum electrolytes, although it is still in the range of vasoactive active peptides. By convention, blood pH values greater than 7.42 are defined as an alkalemia and those below 7.38 as an acidemia, although as will be seen, biological solutions are mainly alkaline solutions. In contrast to the terms acidemia and alkalemia, the terms acidosis and alkalosis refer to the process that created the acidemia or alkalemia. Despite being such a small number, the [H+], with the odd exception, is maintained in a relatively constant range in all living organisms extending from bacteria to humans. The reason why this is important biologically is that H+ only has a proton and no neutron in its nucleus and thus has the greatest charge density and field effect of any atom. Because of this, H+ has strong effects on surrounding charged substances. Thus, [H+] affects the binding of organic molecules as well as the function of many proteins by altering their tertiary structures. To deal with this, nature has evolved many processes to regulate [H+] in a tight range, but these processes often are disturbed in the critically ill. To understand what can disturb [H+] in disease, it is important to understand the normal determinants of [H+] in biological solutions and how therapies can restore normal [H+]. This topic usually is discussed as acid–base

© Springer Nature Switzerland AG 2021 S. Magder et al. (eds.), Cardiopulmonary Monitoring, https://doi.org/10.1007/978-3-030-73387-2_41

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disorders, but from a physical–­ chemical point of view, it really is about regulation of [H+]. As already noted, biological solutions are almost always alkaline. Disease is just about more or less alkalinity. Traditionally, pH and [H+] in biology have been analyzed based on the equilibrium equation for carbon dioxide (CO2) dissolved in water, the formation of carbonic acid (H2CO3), and its dissociation into H+ and HCO3− (Adrogue et al. 2009). When written in the logarithmic form of the Henderson–Hasselbalch equation and grouping of the constant that accounts for the solubility of CO2 in its gas form in water with the dissociation constants of the components of the reactions, the equilibrium of this system can be written as:

pH  K  log  PCO2 / HCO3 ]]



where K is the grouped constants. Based on this equation, an increase or decrease in PCO2 indicates a respiratory acidosis or alkalosis, respectively, and an increase or decrease in HCO3− indicates a metabolic alkalosis or acidosis, respectively. Empiric equations have been derived to determine whether the process is acute or chronic, compensated or uncompensated (Schwartz and Relman 1963). This equation is valid, and this approach describes the system. However, its limitation is that just examining the CO2/HCO3− equilibrium fails to take into account other components of the solution that affect this equilibrium, as well as the role of other components of the solution including the spontaneous ionization (i.e., dissociation) of water, a Fig. 41.1  Strong ions. Strong ions in water are almost completely dissociated. Thus, putting NaCl in water gives equal Na + and Cl−

potentially very large source of H+. This classical approach thus does not lead to a physiological and physical–chemical understanding of the underlying chemical processes. The late Peter Stewart went back to basic physical chemistry and identified the components of biological solutions that need to be taken into account to predict [H+] balance (Stewart 1978, 1981, 1983; Magder and Emami 2015). In his analysis, [H+] is a dependent variable, and its value is determined by the composition of the fluid being studied. H+ is not an independent variable that can be moved from compartment to compartment. Instead, movement of other components determines [H+]. The three independent determinants of [H+] are as follows: (1) the differences in concentrations of strong positive and negative ions, which is called the strong ion difference [SID] (Fig.  41.1); (2) PCO2 and the weak volatile acid that it forms as in the standard approach; and (3) the concentration of non-­ volatile weak acids, of which the dominant one in plasma is albumin (Fig. 41.2). The effect of each of these components is based on not only their total mass in the body but also their concentrations so that the total amount of water in the body also has an impact.

Importance of Water To understand the significance of [H+], it is best to start with pure water at standard temperature. The concentration of water molecules, i.e., [H2O] in “water”, is 55.3 moles. Compare this concen-

41  Acid-Base and Hydrogen Ion

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Fig. 41.2  The three independent determinants of [H+] and HCO3−. The three independent factors are the SID, CO2, and weak acids (HA) which in blood is primarily albumin. H+ and HCO3- are dependent variables

tration to that of [Na+] which is 0.140 moles in normal serum. Thus, water has a large amount of H+ atoms, but only a very small amount of the H2O dissociates into H+ and OH−. The dissociation constant of H2O at standard temperature and pressure is 14 × 10−9, and [H+] is 100 × 10−7 (pH  7.0). If a beaker of pure water is heated to body temperature, 36.7 °C, pH decreases to ~6.8. One might ask, is this solution acidic? The answer to this question is central to an understanding of [H+] and acid–balance and discussed next. Acids have been defined as electron acceptors or proton donors; these definitions were created to deal with [H+] in all types of solutions. However, the solvent in biology is water and thus the behavior of [H+] in water is what needs to be understood. This allows for the much simpler, and biologically useful, definition, created by Arrhenius in the eighteenth century. He defined an acid solution as one in which [H+] is greater than [OH−], a neutral solution as one in which [H+] = [OH−], and an alkaline solution as one in which [OH−] is > [H+]. Thus, even though at a temperature of 36.6  °C, pure water has a pH of 6.8, it still is a neutral solution because [H+] equals [OH−]. What happened is that the dissociation of H2O increased at the higher temperature, and both [H+] and [OH−] increased equally. This had to occur to maintain conservation of mass and electrical neutrality, i.e., all positives need to match all negatives, and there are no other substances to allow the positive and

negative charges to differ. This is called the principle of electrical neutrality. The message here is that the values of [H+] or pH do not indicate whether the solution is acidic. As already noted, all bodily solutions, with exception of the fasting stomach and lysozymes, are alkaline, and when we say that the blood is acidic, it really is just less alkaline. This is true down to a pH of around 6.6. In Arrhenius’ definitions, an acid is a substance that increases the [H+] of the solution and base in one that decreases [H+]. Despite the point that acid–base disorders are really about more or less alkalinity, this is too cumbersome a usage and goes against the long historically used terminology. Accordingly, we still will continue to describe a pH less than 7.4 as being acidic, and a pH greater than 7.4 as being alkaline for biological purposes.

Importance of Strong Ions A key factor was introduced in the last paragraph and that is the principle of electrical neutrality. This says that in a macro-solution, the concentration of all positive and negative charges must be equal. This is because a small difference between positive and negative charges creates a large electrical force that can change the dissociation of substances that are weakly associated such as carbonic acid, albumin, phosphate, and importantly, even H2O.

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A quantitative example of this force will help illustrate its importance (Fig.  41.3). Consider a sphere that has a radius of 1  mm and contains a solution with an ionic concentration difference of 1 × 10−7 Eq/L. Also remember that [Na+] and [Cl−] in normal blood are in the 10−3 range or 1000 times greater than this. The volume of the sphere is 4.2 × 10−6 L, and the net positive charge is thus 4.2 × 10−6 × 10−7 = 4.2 × 10−13. One Eq is 96,500 coulombs (coul) so that the charge on the sphere is 4.2 × 10–13 × 96,500 = 4.0 × 10−8 coul. The electrical potential of a sphere of radius r (in meters) carrying a charge of Q (coul) is given by Q/1.1 × 10−10r (volts), which results in 400,000 volts! The value is huge, and thus, even this small difference in charge cannot be sustained in solution and must be discharged by changing the equilibrium of weaker ionic substances. This means that the concentrations of ions that are strongly dissociated in the solution must be matched by the concentration of weaker charged ions. Strong positive ions include Na+, K+, Ca2+, and Mg2+, and strong negative ions include Cl− and SO4−. The addition of NaCl to a beaker of pure water results in Na+ and Cl− ions but no NaCl (Fig. 41.1). The solution still is neutral because [Na+] and [Cl−] are equal. However, if the solution is made from solutions in which [Na+] and [Cl−] are not equal, [H+] and pH must change. The difference in concentration is called the strong ion difference [SID]. This occurs because the electrical force created by the difference in charge of the [Na+] and [Cl−] must be accommodated by a change in the balance of dissociation of water. If the [Na+]

Fig. 41.3 Significance of electrical neutrality. See text for calculations. A charge difference between strong positive and negative ions of only 1 × 10–7 Eq in a sphere with a radium of 1 mm is 400,000 volts and must be quickly discharged

S. Magder and R. R. Chivukula

increases relative to the [Cl−], the solution must have more [OH−] to balance the charge from Na+, and it will have a lower [H+] (Fig. 41.4); the new solution thus will be less acidic (or more alkaline). If [Cl−] increases relative to the [Na+], the solution will need to have a higher [H+] to balance the Cl− and a lower [OH−], and the solution will be more acidic (less alkaline) (Fig.  41.4). The change in [H+] with a change in [SID] of this simple system that only has strong ions requires a quadratic equation to solve two equations for the two unknown values ([H+] and [OH−]), i.e., the electrical neutrality equation and the water equilibrium equation. As already noted, almost all bodily solutions are alkaline, i.e., [OH−] is greater than [H+]. This primarily is because [SID] always is positive, i.e., [Na+] is greater than [Cl−] (Fig.  41.4). The advantage of having a positive [SID] is that it means changes in [H+] are much smaller for any change in [SID], and thus there is smaller effects from changes in [OH−] on molecular structures while the concentrations of ionic elements can be regulated to maintain normal osmolality. To appreciate the contribution of positive and negative ions in blood, it is useful to examine “Gamblegrams”, developed by the American physiologist James Gamble (Harvey 1979). In these figures, the concentrations of cations and anions are plotted as individual stacked columns (Fig. 41.5). Because of the principle of electrical neutrality, the cation and anion column heights must be identical. It is evident that strong cations are higher than strong anions, and the normal

41  Acid-Base and Hydrogen Ion

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Fig. 41.4  Changes in H+ and OH− with change in concentration of Na+ relative to Cl− (strong ion difference). When the SID is negative, changes in Cl− match changes in H+, but when SID is positive, changes in H+ are much smaller than changes in Cl− (or Na+). The solution is also Normal

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alkaline which is the case for almost all bodily solutions. To be able to display the changes, the SID is presented only for differences of 1 × 10−6 (micromolar), whereas in plasma, the difference in 40 × 10−3 (millimolar) and not seen on this scale

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Fig. 41.5  Gambelgrams of normal blood, lactic acidosis, hyperchloremia, hypoalbuminemia, and hyponatremia. Cations are on the left of each set and anions on the right. The [Na+] (light blue) is 140 mEq in all but the hyperhydration (hyponatremia example). [Cl−] is in yellow. The standard is 102, and it is increased to 112  in the hyperchloremia example and 88 in the hyperhydration example.

The baseline [HCO3−] is 25 mEq/L, it is 14 in the lactic acidosis (increase of 10 mEq/L) and in the hyperchloremic example, 30  mEq/L in hypoalbuminemia and 21  mEq/L with the hyponatremia. The SID is 41  in the baseline, 31  in the hyperchloremia, 41  in the hypoalbuminemia, and 35 mEq/L in hyponatremia

[SID] is approximately 40 mEq/L in plasma. The difference in strong ions is primarily made up of HCO3- and the ionic effect of weak acids, which is primarily albumin in plasma. [H+] and [OH-] are not seen on this graph because their concentrations are so much smaller. Under normal conditions, the [SID] ~ [A-] + [HCO3-] in which A- is the concentration of the charge due to dissociated weak acid. Two common clinical conditions illustrate the significance of the [SID]. There currently is a lot of discussion about the hyperchloremic

acidosis that is seen when too much normal saline is given (Magder 2014). What simply is happening is that a solution with equal [Na+] and [Cl−] is being added to plasma which normally has a [Na+] that is around 40 meq/L greater than [Cl−] (i.e., [SID]  =  40] (Fig.  41.5). We normally consume [Na+] and [Cl−] in almost equal amounts, and our bodies can handle it. However, when an excess load of [Na+] and [Cl−] is given, the kidney can rapidly excrete Na+, but the mechanisms for excreting Cl− are much slower. The [SID] thus is

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narrowed and [H+] is increased (actually, less of a ­difference from [OH−]), and there is a metabolic acidosis (actually less alkalosis). The simple solution is to give less Cl−. As a simple quick guideline, a narrowing of the difference between [Na+] and [Cl−] is an acidifying process, and a widening of the difference between [Na+] and [Cl−] is an alkalizing process. A second example is a dilutional acidosis. When the amount of pure water is doubled in a solution with different concentrations of Na+ and Cl−, the [Na+] and [Cl−] are equally diluted (Fig. 41.6). As a result, the [SID] is decreased and the solution becomes more acidic (or less alkaline) because not as much [OH−] and other weak negative ions are needed to balance [Na+]. The same occurs in plasma when plasma is diluted by an increase in water, and a metabolic academia is the result. In the opposite direction, a loss of water and hemoconcentration produces an alkalemia. These occur without any action required by the kidney. As long as PCO2 remains constant as regulated by the brain, the HCO3− will also go down with the dilution and increase with the hemoconcentration simply by the interaction of all the elements in the system and the need for electrical neutrality. As noted, Na+ and Cl− are the dominant strong ions in plasma, but under pathological conditions, other relatively strong ions can become important.

Fig. 41.6  Dilutional effect on the SID.  Doubling the amount of water reduces the concentration of Na  +  and Cl- by half and thus the SID is half which will reduce the alkalinity of the solution (i.e., make it more acidic)

S. Magder and R. R. Chivukula

These are almost always negative ions. The two most common ones are lactate and the ketones, acetoacetate, and beta-hydroxy-­butyrate, but there are many others, which are the well-known causes of the traditional wide-anion gap acidosis. These include salicylate, formate (derived from methyl alcohol), glycolate (from ethylene glycol), some amino acids, sulfates (renal failure), and iron (which acts by binding OH−). The effect on [H+] of any of the strong anions that cause a wide anion gap acidosis is the same as an increase in [Cl−].

Importance of CO2 The amount of CO2 in the blood is a balance between production and clearance by the lungs (Jones 2008). The lungs can eliminate CO2 as fast as it is delivered by the circulatory system so that the concentration of CO2 remains in tight limits and the body normally is an open system for CO2. Furthermore, CO2 in the blood is directly proportional to PCO2 in the gas phase so that PCO2 in blood can be used as an indication of dissolved CO2 and therefore indicates how fast and which way CO2 will move between two solutions. Thus, it can be said that when the cardiorespiratory systems are intact, CO2 is a controlled or independent variable, and the total amounts of the concentration of all the other components of the CO2 equilibrium are fixed by PCO2. It is worth noting, however, that reaction time of dissolved CO2 with H2O or OH− to form H2CO3 is slow, in the order of 30 seconds, but this is reduced to milliseconds in the presence of the enzyme carbonic anhydrase, which is widely available in the body, but importantly, not everywhere. The role of CO2 in the determination of [H+] is the same as in the classical approach, but it is important to consider how its equilibrium is altered by the [SID]. H2CO3 is a weak acid. Unlike Na+ and Cl−, it is equilibrium with multiple forms including PCO2, HCO3−, and CO32−, although by far, HCO3− is the dominant form by a ratio of ~24 to 1 at normal pH.  The differentiation of the activity of a strong and weak acid can be appreciated by considering the effect of

41  Acid-Base and Hydrogen Ion

creating a negative [SID] on the equilibrium of the CO2 system; all carbamate forms are driven to CO2. This property is the basis for the electrode measurement of [HCO3−] in plasma; what actually is measured is “total” CO2, but because most of the total is in the form of HCO3−, total PCO2 is a close approximation of HCO3−. On a blood gas sample, HCO3− actually is not measured but calculated based on the measurement of PCO2 and [H+] and the known equilibrium constants. Significantly, when [SID] is negative, adding CO2 has no effect on [H+] (or pH) of a solution because carbonic acid does not dissociate. As long as central mechanisms keep PCO2 constant, adding HCO3−, for example in the form of NaHCO3−, only very briefly increases plasma [HCO3−] because the CO2 form is quickly blown off. However, if CO2 is given in the form of NaHCO3−, the Na+ is left behind, widens the [SID], and alkalinizes the blood. This, too, will not last long. Because [Na+] in the body is a major determinant of the body’s osmolality, its concentration is tightly regulated and the Na+ is quickly excreted (if the kidneys are working). On the other hand, when CO2 is added to a solution with a positive [SID], and cannot be cleared by ventilation, [H+] always increases.

Fig. 41.7  Properties of weak ions

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Importance of Albumin The third independent determinant of [H+] is the concentration of non-volatile weak acids (Fig. 41.7). To know the effect of a weak acid on the solution, it is necessary to know the total concentration of all species of the substance, including the un-dissociated and dissociated species, the dissociation constant, and the most difficult part for a large molecule, its ionic activity (Fig.  41.8). It has been shown empirically by Figge and coworkers that weak acid activity in blood is dominated by albumin (Figge et  al. 1991; Figge et  al. 1992). They performed titration studies by adding different amounts of albumin to blood samples and derived a linear equation that describes the ionic effect of a total concentration of albumin depending upon the [H+] (or pH). The consequence of a decrease in [albumin] is an alkalinizing effect, and an increase in [albumin] is an acidifying effect. Thus, a decrease in [albumin] can off-set the acidifying effect of an elevated lactate. On the other side, hemoconcentration due to excess water loss can produce an acidemia by increasing [albumin] (Wang et  al. 1986).

S. Magder and R. R. Chivukula

660 Fig. 41.8  Factors that determine the charge related to a weak acid (the major A− is from albumin). The factors include (1) the total protein in all its form, (2) the dissociation constant, and (3) the ionic equivalent of the charged ion

H+

1.Total protein HA

A-

H+

2. Dissociation HA

Ka

A-

HA+ + A-

-

A-

HA+ -

-

3. Charge on A-

-

 pproach to Identifying the Cause A of a Disturbance of Blood [H+] (pH) Stewart and others after him (Kellum et al. 1995; Jones 1990) presented a physical–chemical approach to determining the cause of a deviation of [H+] from normal reference values by calculating the difference in all major strong positive ions in blood, which include [Na+], [K+], [C2+], and [Mg2+] and the strong negative ions [Cl-], and [lactate-] and comparing the difference in positive and negative charge to the charge accounted for by [HCO3-], albumin (with the charge obtained from the Figge equation (Figge et al. 1992), and phosphate (with the charge, too, determined by a Figge equation) (Figge et  al. 1992). They called this difference the strong ion gap [SIG]. If [SIG] is greater than [SID], there must be unmeasured anions which are the known substances in a wide anion gap. However, this approach is more complicated than necessary. The contributions of [K+], [C2+], and [Mg2+] are very small because their baseline values are low, and large deviations in their concentrations are fatal. The [SIG] approach also does not readily identify the contribution of changes in [SID] due to dilution or concentration of electrolytes, changes in albumin, nor the change just related to [Cl-] and thus does not allow quantitative insight into the underlying pathological process and allow for targeted therapy. An alternative pragmatic bedside approach involves use of the base excess (BE) which that is calculated with all blood gas machines, but first this concept of BE needs to be explained.

A-

-

-

 ase Excess: Direct Estimation B of Metabolic Derangements in Acid–Base Status Driven in part by the recognition during the Danish polio epidemic of 1952 that an isolated measurement of total CO2 in the blood could not distinguish between such processes as a metabolic alkalosis and a respiratory acidosis, in the early 1950s, there was an interest in developing a way to separate these two processes, which led to the concept of base excess (BE), described first by Ole Siggaard-Anderson working in the laboratory of Poul Astrup (Siggaard-Andersen 1962, 1966). These investigators took blood samples from patients and fixed the value of PCO2 in the samples by inserting a tonometer to maintain a PCO2 of 40 mmHg. They then measured the pH at a PCO2 of 40 mmHg and titrated the solutions with a strong acid or base to bring the pH back to 7.4. This was essentially a titration of the [SID] back to the normal value (i.e., eliminating any [SIG]). As such, BE is a direct measure of the net metabolic component of an acid–base disorder, independent of the effect of ventilation through control of pCO2. BE should be 0 under physiological conditions of pH 7.40, temperature 37°C, and pCO2 40 mmHg. Samples with a pH > than 7.4 after PCO2 correction were said to have a base excess (BE) because an acid was necessary to bring the pH back to 7.4. Samples with a pH < 7.4 after PCO2 correction were called a base deficit. Rather than using two terms, just BE is used, and when the solution has a metabolic acidosis and a base deficit, it is called a negative

41  Acid-Base and Hydrogen Ion

BE, which is the way we use it in this chapter. Siggaard-Anderson created a nomogram based on the PCO2 , pH, and bicarbonate of the samples they studied, which is still what is used in blood gas machines today (Siggaard-Andersen 1962). Some confusion arises around the term standard base excess versus base excess. This arose because of two misconceptions. It was initially thought that because only a blood sample was analyzed in a test tube, and without the interactions with the larger interstitial space factors that could affect blood pH, adjustments needed to be made for the potential effect of electrolytes in the interstitial space. However, the [H+] in a solution only is affected by the substances in that solution at that time. The interstitial space has a different [SID] and only can alter the [SID] and [H+] of blood by movement of substances over time, which would be a new condition. In the steady state, they can have different values. For example, [HCO3-] is lower in the interstitial space because the [Albumin] is lower than in plasma even though the [SID] is similar. Secondly, SiggaardAnderson used hemolyzed red cells because he wanted to include the buffering effect of hemoglobin. However, the effect of hemoglobin on the SID is minimal because binding of strong ions to hemoglobin is minimal, and its own dissociation has a very small effect. Furthermore, when whole blood is tested, it is inside red cells and thus not part of the plasma space. There also is confusion of the term “standard” base excess which was used to avoid the need to have a hemoglobin value and instead used an assumed standard value. When examining the equations for BE, it can be seen that the hemoglobin effect on BE is very small and beyond the accuracy needed for use of BE for practical clinical purposes (Schlichtig 1997).

 E Approach to Evaluation of B Acid–Base Disorders (Table 41.1) The initial approach to analyzing an abnormal pH in blood starts the same way as the traditional approach (Magder and Emami 2015; Fencl and Leith 1991). If pH is less than the normal reference value of 7.4, there is an acidemia, and if it

661 Table 41.1  Step-wise acid–base analysis 1. Assess the pH. pH > 7.42 = acidemia and pH 0 mEq/L, there is a metabolic alkalosis. If BE is 20 mmHg mPAP >20 mmHg PAWP ≤15 mmHg PVR ≥3 Wood U

Post-capillary PH

mPAP >20 mmHg PAWP >15 mmHg PVR 7 mmHg)

 (i) Isolated post-capillary

 (ii) Combined pre- and post-capillary

WHO PH group(s) All WHO group 1 PAH WHO group 3 PH (lung diseases/hypoxia) WHO group 4 CTEPH WHO group 5 PH (unclear/multifactorial) WHO group 2 PH (left heart disease) No additional pre-capillary PH component

Additional pre-capillary PH component due to:  Contribution of WHO group 1, 3, 4, or 5 PH  Remodelling due to chronic post-capillary PH

Data compiled from Simonneau et al. (Simonneau et al. 2019) Abbreviations: DPG diastolic pressure gradient (diastolic PAP – mean PAWP)

 ulmonary Hypertension (PH) P PH has traditionally been defined as an elevation of mean pulmonary artery pressure (mPAP) ≥25  mmHg at rest. This definition was revised at the 6th World PH Symposium in Nice in 2018 and now emphasizes that mPAP should not exceed 20  mmHg in healthy subjects (Table  54.3) (Simonneau et  al. 2019). Furthermore, hemodynamic characterization of PH allows differentiation between pre-capillary PH and post-capillary PH, primarily through measurement of the pulmonary artery wedge pressure (PAWP). Conditions characterized by pre-capillary PH are hemodynamically defined by PAWP ≤15 mmHg and pulmonary vascular resistance (PVR) ≥3 Wood units (WU). Precapillary PH is typical of WHO Group 1 PAH but the same hemodynamic pattern of PH is also seen in patients with hypoxia from lung

diseases (WHO Group 3), chronic thromboembolic PH (CTEPH; WHO Group 4), and some conditions in WHO Group 5, such as those with sarcoidosis and pulmonary vasculitis (Table 54.3). Increased PVR and the resulting load on the RV in PAH can arise from a broad range of distinct pulmonary vascular pathophysiological processes which primarily are due to proliferative remodeling of the vascular wall and a variable degree of vasoconstriction. Collectively, these result in the narrowing of pulmonary arteries and reductions in the normal compliance which can affect the pulse-pressure and capacitance of these vessels. Moreover, rarefaction of the lung microcirculation and loss of small arteries has become a more commonly recognized pathologic changes in pulmonary capillaries and venous system (Humbert et al. 2019).

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Other Pulmonary Vascular Physiologic Parameters Introduction Calculation of PVR assumes a simple linear pulmonary circulation and ignores two key physiologic aspects: (i) normal pulsatile pulmonary arterial flow, and (ii) the presence of a non-zero downstream pressure, as per the “vascular waterfall” hypothesis of in  vivo vascular function (Vonk-Noordegraaf et  al. 2013; Permutt et  al. 1962; Naeije et al. 2013). As such, there are other parameters of pulmonary artery function which can be measured and better reflect the pulsatile nature of pulmonary artery blood flow. These parameters include compliance (and its inverse, elastance), as well as impedance (Table  54.4) (Thenappan et  al. 2016; Murgo and Westerhof 1984; McCabe et al. 2014; Lankhaar et al. 2006). Pulmonary Vascular Compliance The compliance of the entire pulmonary circulation reflects the ability of all arteries, capillaries, and veins to accommodate blood ejected by the RV during systole and is defined as the change in volume for a given change in pressure (∆V/∆P). In contrast, capacitance includes two volumes, the “unstressed” volume that fills a cylindrical vessel and makes it round but does not create a pressure, and the “stressed” volume that creates the change in pressure. These two terms are often incorrectly and interchangeably used. For example, pulmonary circulation angiographic imaging

techniques including magnetic resonance imaging report total volume at a single or changing pressure but cannot differentiate stressed and unstressed volumes. The situation is also complicated by vascular recruitment which increases total cross-sectional area and thus compliance. During right heart catheterization, pulmonary vascular compliance can be estimated, based on the assumption that the pulmonary circulation is closed at the distal venous end, from the increase in pulmonary artery pressure (pulse pressure [PP] = sPAP − dPAP) due to the stroke volume (SV) during a single cardiac systole, such that ∆V/∆P can be approximated by SV/ PP.  Pulmonary vascular compliance has an inverse hyperbolic relationship with PVR in the normal pulmonary circulation (Lankhaar et  al. 2008; Saouti et al. 2010; Vonk Noordegraaf et al. 2017; Ghio et  al. 2015). However, it should be appreciated that this measurement is in reality a “dynamic” compliance which incorporates resistance because of blood flow at the venous end. Pulmonary Artery Elastance Elastance (or stiffness) is the inverse of pulmonary vascular compliance, and thus is subject to the same issues discussed above for compliance. Increased elastance captures the important concept of loss of vascular distensibility. Elastance is most commonly estimated from the dicrotic notch pressure of the PAP waveform, which reflects mPAP at rest (McCabe et  al. 2014; Chemla et al. 1996).

Table 54.4  Other pulmonary vascular physiologic parameters related to the pulsatile nature of pulmonary artery (PA) blood flow Parameter Units PA compliance (C) mm2/mmHg PA capacitance (Ca) mm3/mmHg PA impedance (PVZ) dynes s/cm5 PA elastance (Ea) mmHg/ml

Physiologic definition Change in PA cross-sectional lumen area for a given change in PAP Change in pulmonary circulation volume for a given change PAP Relationship between pulsatile PA blood flow and pressure Relationship between end-systolic PAP and RV stroke volume

Normal range 3.2–7.9

Reference (Thenappan et al. 2016)

7.9 ± 4.1

(Murgo and Westerhof 1984)

20 ± 1

(McCabe et al. 2014)

0.3 ± 0.1

(Lankhaar et al. 2006)

Although both two-dimensional compliance and three-dimensional capacitance are static measures independent of flow, and thus not affected by vascular resistance, the common clinical measurements are done under conditions of flow, such that resulting “dynamic” values inherently include the element of resistance

54  Cardiopulmonary Monitoring of Patients with Pulmonary Hypertension and Right Ventricular Failure

Pulmonary Artery Impedance Impedance is a measure of the relationship between pulsatile blood flow and pressure. As such, pulmonary vascular impedance reflects the time-dependent continuous variation of the PAP waveform and the blood flow waveform over an entire cardiac cycle. Calculation of impedance requires synchronized, high-fidelity recordings of PAP and pulmonary artery flow, spectral analysis of pressure and flow waveforms, and mathematical Fourier analysis of the relationship between these two parameters to derive an impedance spectrum (Ghio et al. 2015).

Pulmonary Hypertension Introduction PH is characterized by increased PA stiffness (reduced compliance) and decreased capacitance, which are the result of several potential mechanisms, including PA distension-related strain in larger pulmonary arteries, as well as pulmonary macro- and microvascular arterial wall thickening due to both remodeling of the extracellular matrix and smooth muscle cell hyperplastic/ hypertrophic responses (Humbert et  al. 2019; Schäfer et al. 2016). Increased impedance (which incorporates reduced compliance and increased resistance) as well as enhanced pulmonary arterial vasoconstrictor reactivity of medium- and small-sized pulmonary arterioles all worsen pulmonary gas-exchange, as well as increase RV afterload (Naeije 2013). Dynamic PA elastance provides the most comprehensive assessment of the pulmonary vascular load on the RV, incorporating resistive, pulsatile, and passive components (Vonk Noordegraaf et  al. 2017; Schäfer et  al. 2016). Increased PA elastance in PH decreases the capacity of the proximal PAs to accommodate RV stroke volume and also enhances arterial pressure wave reflection. Along with the predominant contribution of increased resistance to RV load, increased elastance can further increase the load on the RV, both of which contribute to progressive RV failure and risk of death (Thenappan et al. 2016; Naeije 2013; Castelain et al. 2001).

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Relationship Between Pulmonary Vascular Physiologic Parameters There is an uncertain relationship between alterations in pulmonary vascular compliance and pulmonary hemodynamics in PH. In the most robust study of the relationship between PA compliance and pulmonary hemodynamics in 719 patients with incident IPAH, dynamic PA compliance was only modestly related to mPAP (Chemla et  al. 2018) although it previously had been suggested that vascular stiffness and resistance were directly related, i.e., PA compliance has an inverse hyperbolic relationship with PVR such that the product of PVR and compliance (RC-time) was constant in health and PH (Lankhaar et  al. 2006, 2008; Saouti et al. 2010; Vonk Noordegraaf et al. 2017). As mentioned above, the clinical measurement of compliance (and sometimes even capacitance) fails to appreciate the dynamic nature of the measurement which is likely dominated by resistance. It has become clear that the PVR-compliance relationship is disturbed in PH, and is significantly different from that observed in the normal pulmonary circulation (Hadinnapola et al. 2015; Chemla et al. 2015). PVR and PA compliance are inversely related in PH, in large part because of the use of dynamic compliance. However, PA compliance is poorly correlated with PVR, so that it cannot be estimated from PVR alone, which may indicate that PAH differentially affects PA stiffness and PVR (Chemla et  al. 2018). Early stages of pulmonary vascular disease can result in a marked loss in PA compliance with only minimal increases in PVR, because of both pulmonary circulatory distension and recruitment. Increasingly severe pulmonary vascular disease is characterized by significant increases in PVR with only minimal further decline in PA compliance (Ghio et al. 2017). Clinical Significance of Altered Pulmonary Vascular Physiology The changes in pulmonary vascular physiology discussed above all result in an increase in the RV afterload and likely contribute to the risk of progressive RV failure and death, although there is

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limited direct clinical evidence that the altered pulmonary vascular physiologic parameters play a role. Specifically, the term PA capacitance, which was already discussed is a combination of PA compliance and more importantly resistance, has been shown to correlate with clinical severity of PH and was an independent predictor of mortality in patients with idiopathic PAH in some studies (Ghio et al. 2017; Mahapatra et al. 2006; Gan et al. 2007; Stevens et al. 2012), but not in the largest study of (n  =  719) treatment-naïve incident IPAH patients (Chemla et al. 2018). It is noteworthy that PH-targeted therapy increases PA capacitance in proportion to the reduction of PVR, which confirms the important contribution of PVR to such dynamic measurements (Lankhaar et al. 2008; Ghio et al. 2017).

 ther Aspects of Pulmonary O Physiology

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is driven by the rarefaction of the pulmonary microcirculation typical of PAH.  Severely reduced DLco (typically 3 cm above the sternal angle and RV heave best discriminated the presence of PH. The combination of RV heave, JVP >3 cm above the sternal angle and peripheral edema were 100% predictive of having a severely elevated mPAP ≥45 mmHg. Presence of a loud P2 was not shown to be independently predictive of PH (Braganza et al. 2019). Recommendations for Clinical Practice Overall, the physical exam can suggest the presence of PH, but cannot be used to reliably diagnose or exclude the presence of PH.  Furthermore, the accuracy and utility of

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regular physical examination of PH patients in order to monitor the severity of PH or RV failure has not been formally studied. Given the poorer reliability of physical exam prediction of PH in newer versus older studies, we suspect that decreased sensitivity and specificity is a result of the deterioration of modern physical exam skills. We recommend that clinicians not rely on the presence or absence of a loud P2 to suspect or exclude PH.  We recommend regular physical examination of all PH patients for evidence of RV failure (elevated JVP, RV heave, peripheral edema), which is of particularly worrying clinical concern. If signs of RV failure are present or worsening, thorough investigation for contributing factors as well as appropriate management should be promptly undertaken.

Cardiopulmonary Monitoring of the PAH Patient: Exercise Capacity Introduction PH is typically associated with symptoms on exertion, and many patients experience reduced activity tolerance to the point of functional limitation and disability. As such, objective assessment of specific functional capacity is an important aspect of the assessment of disease severity in PH patients. This is most commonly done with two specific exercise testing methods, 6-minute walk test (6MWT) and treadmill or cycle cardiopulmonary exercise testing (CPET). 6-Minute Walk Test (6MWT) Introduction This is a simple, safe, reproducible test which assesses submaximal functional capacity of patients using the most common daily activity, walking. 6MWT is typically performed according to recommended technical standards, including physical space, procedure, and monitored variables (Crapo et  al. 2002). The 6MWT distance (6MWD) walked by a patient is typically reported as an absolute value in meters. Normative values have been established in a single study of

R. A. Davey et al.

290 subjects including 173 females, resulting in sex-specific reference equations (Enright and Sherrill 1998). Reporting of individual patient data as % predicted has not yet been considered by guidelines, as prognostic ability does not appear superior to that of absolute 6MWD (Lee et  al. 2010). A simpler weight-adjusted 6MWD (6MWD [m] × weight [kg]) has also been proposed (Oudiz et al. 2006). Effects of PH on 6MWT Registry data from studies in France, United States, China, and Spain have reported reduced 6MWD in patients with many subtypes of PAH, which is proportional to the clinical severity of PH as assessed by NYHA FC (Benza et al. 2010; Brenot 1994; Miyamoto et al. 2000). Moreover, there is an association between reduced 6MWD and lower CPET VO2PEAK (Miyamoto et al. 2000), although the correlation between weight-adjusted 6MWD and VO2PEAK appears to be significantly stronger than for the unadjusted 6MWD (Oudiz et  al. 2006). 6MWD is importantly related to health-related quality of life (HRQoL) in individual patients. For example, 6MWD correlated with seven of eight subscales of the SF-36 (Halank et al. 2013). Although 6MWD is reduced in most patients with PH, it is generally only modestly correlated with hemodynamic PH severity, including some cardiopulmonary parameters (e.g., CO, RAP, and TPR), but typically not with mPAP (Miyamoto et al. 2000). Effects of PH-Targeted Therapy on 6MWT In PAH patients who are treated with PH-targeted medical therapy, 6MWD typically improves significantly, with a range from 15 to 90 m depending on the specific PH-targeted medication (Galiè et al. 2009). Improvement is usually less marked following the addition of a 2nd medication in monotherapy-treated PAH patients (Lajoie et al. 2016). Functional capacity of PH patients, including those with PAH, also improves significantly following exercise rehabilitation therapy. This includes slight increases in VO2PEAK (e.g., 2.2  mL/kg/min; 95% CI 0.4–3.9) and more marked increases in 6MWD (e.g., 73 m; 95% CI 46–99 m) (Buys et al. 2015).

54  Cardiopulmonary Monitoring of Patients with Pulmonary Hypertension and Right Ventricular Failure

The improvement in 6MWD in PAH patients treated with PH-targeted medications is inversely correlated with the decline in PVR (Savarese et  al. 2012). Moreover, improved NYHA FC in PAH patients treated with current PH-targeted medications was associated with greater increases in 6MWD as compared to patients with unchanged NYHA FC (Barst et  al. 2013). However, a minimal clinically important difference (MCID) for improvement in 6MWD has not been clearly defined in PH. A statistical minimal important difference (MID) was identified at approximately 33  m, using both distributional and anchor-based methods in PAH patients treated with the PDE-5 inhibitor tadalafil (Mathai et al. 2012). Similarly, in sildenafil-treated PAH patients, a MID of 42 m corresponded to a statistically significant reduction in clinical events (Gilbert et al. 2009). Prognostic Value of 6MWT in PH Many studies support the strong negative prognostic value of poor baseline 6MWD in newly diagnosed PAH patients. For example, IPAH patients with baseline 6MWD less than the median value (332 m) had a significantly lower survival rate than those walking farther than the median (Miyamoto et al. 2000). Similarly, there was a significant difference in survival (p = 0.002) based on a baseline 6MWD >330 m vs 15% reduction from baseline) are at high risk; a cut-off value of 165  m performed best in prognostication of mortality, whereas patients above 440 m appear to be at low risk of death in 1-year (Zelniker et al. 2018).

Cardiopulmonary Exercise Test (CPET) Introduction Treadmill or cycle ergometer, incremental, symptom-­limited CPET provides the most com-

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prehensive non-invasive assessment of integrated exercise responses involving the pulmonary, cardiovascular, hematologic, and neuromuscular systems. Technical aspects of CPET are critical for safe assessment of PH patients and to obtain clinically relevant data, but will not be reviewed here as they are well-summarized elsewhere (Ross et al. 2001). CPET has proven value in the evaluation of patients with symptoms such as dyspnea and exertion intolerance, based on classic patterns of dynamic abnormalities suggestive of respiratory, cardiac, or other conditions including unfitness (Ferrazza et al. 2009). CPET Patterns in PAH In patients with PH, a panel of characteristic submaximal and peak exercise CPET abnormalities have been consistently described which may identify the presence of significant pulmonary vascular disease. As well, CPET’s comprehensive assessment of dyspnea and exercise limitation, key clinical features of PH, can categorize the functional severity of PH and associated RV failure (Sun et  al. 2001a; Arena et  al. 2010; Weatherald et  al. 2017; Farina et  al. 2018a). Moreover, classic cardiopulmonary abnormalities may be prognostic in individual patients and may also be responsive to PH-targeted therapy (Groepenhoff et al. 2013). Several large case series have reported findings on incremental, symptom-limited, maximal CPET in PH patients, most commonly idiopathic PAH patients (Sun et  al. 2001a; Wensel et  al. 2002). It is clear that CPET parameters typically are markedly abnormal in most PAH patients compared to healthy control subjects. The classic CPET pattern is characterized by a marked reduction in overall exercise capacity, as reflected by reduced work rate (WR) and VO2PEAK, with a blunted VO2/WR relationship, associated with evidence of impairment of both cardiovascular and ventilatory responses (Fig. 54.1). Cardiac abnormalities largely specific to the RV are central to the symptoms and exercise limitation in patients with PH. RV output is normally inversely correlated with PAP, such that PH results in a decreased ability of the RV to increase stroke volume (as reflected by the impaired increase in oxygen pulse) and cardiac output. In

R. A. Davey et al.

884 Fig. 54.1 Characteristic disturbances in CPET parameters in patients with PAH vs healthy controls. Abbreviations. VO2 oxygen uptake, WR work rate. PaCO2 arterial carbon dioxide tension. PETCO2 end-tidal carbon dioxide tension. AT anaerobic threshold. SpO2 arterial oxygen saturation by pulse oximetry. VE minute ventilation, VD dead space volume. VT tidal volume, P(A–a)O2 alveolar–arterial oxygen tension difference

Peak VO2 Overall Exercise

Peak Work Rate (WR) VO2/WR

Peak HR Peak O2-pulse

Cardiovascular

Slope of HR response

VO2/WR PaCO2, PETCO2 (rest)

VE (rest, exercise)

PETCO2 (AT)

VD/VT (rest, exercise)

SpO2 (early, without

P(A-a)O2 differences (exercise)

Ventilatory

PaCO2 rise)

addition, based on principles of ventricular interdependence, RV enlargement and resulting septal flattening or leftward shift can result in important alterations in LV geometry, diastolic function, and systemic perfusion. The consequence of impaired cardiac function and reduced CO/CI is inadequate tissue O2 delivery and early-onset lactic acidosis (Weatherald et  al. 2017; Groepenhoff et  al. 2010; Holverda et al. 2006), resulting in lower AT, and a resulting excessive HR response for any WR, which is also driven in part by sympathetic hyperactivity. Moreover, cardiac responses at the onset of exercise appear to be delayed in PAH patients, as CO may actually transiently decline, possibly due to increased venous return resulting in RV distension and reduced stroke volume (Lador et  al. 2016). Overall, there is a less efficient coupling of O2 delivery and VO2, contributing to impaired VO2 kinetics in PAH and blunted VO2/WR relationship. In healthy subjects, normal levels of VE, VE/VCO2, and PaCO2 (PETCO2) reflect the appro-

priate matching of ventilation and perfusion at rest and exercise. In PAH patients, the ventilatory pattern is characterized by increased VE at rest and at any exercise WR (Hoeper et  al. 2007; Velez-Roa et al. 2004; Farina et al. 2018b). This excessive ventilation is a function of several stimuli, including: (i) the above cardiovascular abnormalities which result in early lactic acidosis due to impaired CO, (ii) arterial hypoxemia and carotid body chemoreceptor stimulation, (iii) high levels of physiologic (total) dead space (VD/VT) characterized functionally by areas of either absent or markedly impaired perfusion with relative over-ventilation, as well as (iv) increased chemosensitivity of peripheral chemoreceptors (driven by hypoxia, catecholamines, as well as lactic acid and lower pH), potentiated by neural sensing of central pulmonary artery and right atrial/ventricular stretch (Velez-Roa et  al. 2004; Farina et  al. 2018b; Naeije and Van De Borne 2009). Importantly, despite the increased total VD/VT, the increase in VE in PAH is typically excessive

54  Cardiopulmonary Monitoring of Patients with Pulmonary Hypertension and Right Ventricular Failure

and inefficient, resulting in a reduction of resting PaCO2 and often a further decrease during exercise. This ventilatory inefficiency is best reflected by an increased VE/VCO2 ratio (and less reliably by increased VE/VO2 ratio), which is most evident at specific exercise levels (e.g., anaerobic or ventilatory threshold), as well as the increased slope of the overall, continuous VE/VCO2 relationship (Hoeper et al. 2007; Velez-Roa et al. 2004; Farina et  al. 2018b). Moreover, this ventilatory inefficiency is reflected by a widened difference between PaCO2 and PETCO2; specifically, PETCO2 is even more dramatically reduced than PaCO2 at rest and declines further during exercise. Hypoxemia in PAH is common, typically only mild at rest but commonly significantly worse on exercise. Hypoxemia is the result of multiple cardiopulmonary physiologic disturbances most prominent on exercise, including lower SvO2 (impaired cardiac output), impaired alveolar– arterial O2 diffusion, as well as increased right-­ to-­left shunting which can be both intrapulmonary as well as through a patent foramen ovale (Hoeper et al. 2007; Dantzker et al. 1984; Sun et al. 2002). In summary, PH is characterized by marked abnormalities of V/Q matching resulting in excessive VE and higher VE/VCO2, due both to increased VD/VT as well as hyperventilation and reduced PaCO2 (and PETCO2) both at rest and during exercise compared to normal subjects (Velez-Roa et al. 2004; Farina et al. 2018b). Such ventilatory abnormalities are clinically important, as the reduction in PETCO2 correlates inversely with the elevation in mPAP, and is proportional to the impairment of VO2PEAK (Yasunobu et al. 2005).

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(e.g., higher VD/VT at rest and exercise, higher VE/VCO2 at AT and overall slope, lower PETCO2 at AT) (Arena et  al. 2010). For example, exercise cardiac index correlated with VO2PEAK, and furthermore, was the only independent predictor of VO2PEAK in multivariate stepwise linear regression analyses (Blumberg et al. 2013). In addition, the reduction in PETCO2 correlates inversely with the elevation in mPAP, and is proportional to the impairment of VO2PEAK (Yasunobu et al. 2005).

Prognostic Value of CPET Parameters in PAH CPET parameters correlate importantly with the severity of PAH and RV failure, and correspondingly, may have clinical utility as markers of prognosis in individual PAH patients. One of the strongest predictors of worse survival in PAH remains an impairment of overall exercise capacity, as reflected by reduced VO2PEAK. In various studies, different threshold values of VO2PEAK have been identified, e.g., 10.4, 11.5, and 13.2 ml/min/ kg, below which mortality is increased (Wensel et  al. 2002; Deboeck et  al. 2012; Groepenhoff et al. 2008). Similarly, PAH patients with VO2PEAK more than 65% predicted have a good prognosis for 5-year survival (Wensel et al. 2013). Similarly, other markers of impaired overall exercise capacity are also associated with worse survival, e.g., lower HRPEAK (Groepenhoff et al. 2013). Aerobic capacity can improve following treatment of PAH patients with PH-targeted medications; a greater change in VO2PEAK was associated with better survival, and this change in aerobic capacity was significantly related to changes in RVEF (Groepenhoff et al. 2013). Correlation of CPET Parameters Other CPET parameters also appear to be with Severity of PAH important prognostic markers in PAH patients, The degree of abnormal CPET cardiopulmonary particularly markers of impaired cardiac function responses are strongly associated with the sever- (e.g., lower peak sBP, reduced O2-pulse) and ity of PAH (Yasunobu et  al. 2005; Sun et  al. ventilatory inefficiency (higher VE/VCO2, reduced 2001b; Correale et  al. 2017). Worsening PAH, PETCO2) (Groepenhoff et al. 2013; Wensel et al. especially in association with RV failure, is asso- 2002; Groepenhoff et  al. 2008). For example, ciated with progressively worse overall exercise increased VE/VCO2 slope (e.g., greater than 48) is capacity (e.g., WR, VO2PEAK), lower VO2/WR associated with worse survival (Groepenhoff ratio, as well as greater abnormalities in cardio- et al. 2008, 2013). Similarly, another study found vascular (e.g., lower exercise HRPEAK, lower O2-­ worse survival with VE/VCO2 slope >62, as well as pulsePEAK, earlier AT) and ventilatory responses poor prognosis with VE/VCO2 >54 specifically at

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AT (Deboeck et al. 2012). Impaired oxygenation on exercise also appears to be an important prognostic marker. Indeed, exercise-induced right-to-­ left shunt strongly predicts death or transplantation in PAH patients, independently of hemodynamics and other exercise parameters including VO2PEAK (Oudiz et al. 2010). Following exercise, post-exercise HR recovery (1  min) response, which is delayed in PH patients vs controls, appears to be a marker of poor prognosis (Ramos et  al. 2012). Indeed, patients with more rapid HR recovery had better NYHA FC, resting hemodynamics and 6MWT distance. It is unclear whether all parameters are responsive to treatment. For example, the change in O2-pulse following PH-targeted therapy predicted survival whereas changes in VE/VCO2 slope did not (Groepenhoff et al. 2013). Rather than using individual CPET parameters, the prognosis may be better predicted by using either combinations of several CPET parameters, or a combination of CPET and other parameters, e.g., hemodynamic. For example, in multivariate analysis, reduced VO2PEAK and low sBP at peak exercise were both independent predictors of survival; specifically, patients with combined lower VO2PEAK (18 mm IVC diameter  5 mm there should be suspicion of an underlying chronic component to their RV dysfunction (Matthews and McLaughlin 2008). With chronic RV afterload, remodeling occurs resulting in hypertrophy of the RV. As the RV becomes thicker its shape changes, losing its typical triangular shape resulting in tricuspid annular dilation. With the change in the shape of the RV, the TRV increases and therefore the RVSP increases (Gerges et al. 2014).

Computed Tomography Angiography (CTA)

Computed tomography angiography (CTA) is the imaging modality of choice to help with the diagnosis of PE.  CTA can identify the degree of thrombus load as well as identify the presence of both acute and chronic thrombi formation. There are multiple scores, such as the Miller, which quantify the degree of pulmonary vasculature obstruction by reviewing the number of segments Transthoracic Echocardiography involved and the degree of obstruction in each lung zone on a four-point scale (Yu et al. 2011). TTE has become a very useful tool in the diagno- While it is helpful to understand the level of clot sis of massive and submassive PE. The presence burden, at the current time there is no established of RV dysfunction found on TTE in the setting of association between clot burden and mortality normotensive patients has been associated with a (Abrahams-van Doorn and Hartmann 2011). higher rate of developing shock and increased Similar to TTE, CTA when viewed in the axial hospital mortality in comparison to normotensive cuts can display the RV to LV ratio. A RV/LV patients without RV dysfunction (Grifoni et  al. ratio >0.9 has been proposed as an indication of 2000). RV dysfunction on TTE can present as RV right ventricle dysfunction and a higher likelito LV ratio greater than 1  in an apical four-­ hood for decompensation (Gibson et al. 2005). chamber view, global hypokinesis of the RV free wall also known as McConnell’s sign, reduced inferior vena cava collapse with inspiration (less Management than 40%), right atrium pressure elevation, elevation in the RV end-diastolic diameter, increased The management of hemodynamic comprising tricuspid regurgitation and deviation of the inter- PE should focus on initially stabilizing the patient ventricular septum creating a “D-sign” in the with supportive measures including oxygen therparasternal short axis (Fig. 55.3) (Matthews and apy, blood pressure support in the form of vaso-

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a

c

RV LV

b

d RV

RA

LV

LA

Fig. 55.3  Classical echocardiographic signs of massive and submassive pulmonary embolisms. (a) “D-Shape” of the left ventricle (LV). Thrombus formation in the right ventricle (RV). (b) Dilation of the right atrium (RA) and

right ventricle (RV). (c) Jet of tricuspid regurgitation corresponding with pulmonary hypertension. (d) Abnormal tricuspid regurgitation

pressors, and/or inotropy and then move on to consideration of definitive treatment which can include anticoagulation, thrombolytics (pharmacological, catheter-directed, surgical embolectomy) or mechanical circulatory support (extracorporeal membrane oxygenation).

however, it may not be the therapy of choice for hemodynamically unstable patients (Leentjens et al. 2017). For patients who present with intermediate and high-risk PE, systemic thrombolysis at the earliest time point can be the key to reduction in pulmonary vascular bed obstruction and restoring stability. There are numerous medication options for systemic thrombolysis which include alteplase (tPA), streptokinase, urokinase, reteplase, and tenecteplase (Belohlavek et  al. 2013b). Alteplase has been the most widely studied and frequently used in clinical practice. Alteplase is typically given in one of two forms. Alteplase is bolused 50  mg over

Anticoagulation and Thrombolytics Anticoagulation has been the foundation of PE management. Anticoagulation in the form of low molecular weight heparin, unfractionated heparin, or direct oral anti-coagulants (DOACs) is a first line therapy for patients with low-risk PeEs;

55  Monitoring and Management of Acute Pulmonary Embolism

2  minutes and can be rebolused 50  mg after 15 minutes or alteplase is administered with a 10 mg IV bolus over 1–2 minutes followed by 90  mg IV over the course of 2  h. Although alteplase thrombolysis can be performed in combination with a heparin infusion, many providers will choose to start heparin after the alteplase infusion has discontinued. The role of systemic thrombolysis in the setting of intermediate risk or submassive PE is still debated. The PEITHO trial evaluated the role of heparin alone versus heparin plus systemic thrombolysis (Meyer et al. 2014). Patients were randomly assigned to receive heparin plus tenecteplase or heparin plus placebo. The heparin plus tenecteplase group had a reduced likelihood for hemodynamic decompensation (1.6% vs. 5.0%; P = 0.002); however, there was no statistically significant difference in mortality at 7 days (1.2% vs. 1.8%; P = 0.42) which was the primary end-point. The heparin plus tenecteplase group was associated with a higher risk of major extracranial bleeding (6.3% vs. 1.2%; P  1.5 on CT imaging were more likely to fail systemic thrombolysis and convert to surgical embolectomy as definitive treatment (Aymard et al. 2013). Mechanical circulatory support in the form of venous-arterial extracorporeal membrane oxygenation (VA-ECMO) has been used to provide clinical stability or to allow for a bridge until definitive therapy can be performed in patients with sustained hemodynamic instability. VA ECMO can off-load the RV as deoxygenated blood is removed from the inferior vena cava via femoral vein cannulation site. This can cause the reduction of preload in the RV, decrease the pulmonary artery pressures, and bypass the obstruction within the pulmonary vasculature (Belohlavek et  al. 2010). Deoxygenated blood from the venous cannula is then passed through an external oxygenator and returned to the arterial system via the femoral artery into the aorta. Of note, it is important to closely monitor for pulmonary edema in the setting of VA ECMO (Baran 2017; Burkhoff et  al. 2015). As flows are increased in the circuit, LV afterload is increased and LV volume can also increase. This can create

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an increase in pulmonary capillary wedge pressure and the formation of pulmonary edema (Baran 2017; Burkhoff et al. 2015). Management of this requires some process to decompress the left ventricle which can include performing a septal puncture or using another mechanical device to off-load the LV. There have been several case series reporting the use of VA ECMO for massive PE.  In a retrospective review of 78 patients who received VA ECMO for acute massive PE, the mean duration of VA ECMO support was 4.47 ± 2.98 days (Yusuff et al. 2015). Forty-­ three patients had a cardiac arrest prior to initiation of VA ECMO and cardiac arrest significantly increased the risk of death with an odds ratio of 16.71 (Yusuff et al. 2015). The overall mortality was 29.9% for all patients who received VA ECMO with the leading cause of death being multi-organ dysfunction (Yusuff et al. 2015). The decision to attempt systemic anticoagulation, CDT, surgical embolectomy, or VA ECMO in the setting of high-risk PE can be challenging. Given the complexity of submassive and massive PE cases, a Pulmonary Embolism Response Team (PERT) was first created at Massachusetts General Hospital. The PERT is a multidisciplinary rapid response team complied of physicians in cardiovascular medicine and surgery, emergency medicine, hematology, pulmonary/critical care, and radiology. The adoption of the PERT approach has expanded to numerous institutions across the country and even worldwide.

Vasoactive Medications Supportive treatment of shock and hypoperfusion in massive PE is challenging, but is critical for maintaining organ function and, hopefully preventing the “death spiral.” Fluid resuscitation, although tempting in the setting of hypotension, is rarely beneficial and may, in fact be harmful (Ducas and Prewitt 1987). The already elevated right atrial pressure and RV volume overload seen in massive PE indicates that RV filling is limited and more fluid cannot increase cardiac output and more volume only worsens RV function by bulging the septum into the LV. Thus, avoiding volume challenges should be stressed. The selection of vasopressor medication to help support the blood pressure in a hemodynamically unstable patient with a PE can vary across institutions. The main goal of vasopressors is simply to achieve a high enough mean arterial pressure to maintain adequate coronary and systemic perfusion. Given that the right coronary fills throughout the cardiac cycle, when there is high right ventricular pressure, coronary perfusion pressure must be adequate or RV ischemia can result. Several pressors are available (Table  55.2). Norepinephrine acts on alpha-1 and beta-1 receptors. It acts on alpha-1 causing vasoconstriction leading to increases in mean systemic pressures. With high systemic pressures, there is better coronary perfusion to the RV which helps prevent or

Table 55.2  Vasopressor and inotropy mechanism of action for hemodynamically unstable PE Receptor of action Norepinephrine Epinephrine Vasopression Dopamine 10 μg/kg/min Dobutamine Milrinone

α1 ++ ++

β1 + ++

β2

D

V1

Other

+ +

+

++

+

++

++

++

++

++

++

+ Phosphodiesterase-3 inhibitor

Abbreviations: D dopaminergic receptor, V1 vasopressin receptor, + low effect, ++ moderate to high effect

55  Monitoring and Management of Acute Pulmonary Embolism

reduce myocardial ischemia (Ventetuolo and Klinger 2014). Norepinephrine also acts on beta-1 receptors creating inotropic support. Epinephrine acts on alpha-1, beta-1, and beta 2 receptors. Similar to norepinephrine it promotes vasoconstriction causing increases in systemic blood pressure, however has larger effects on beta-1 which can promote arrhythmia which can be detrimental to perfusion. By acting on beta-2 receptors it may produce a slight reduction in pulmonary vascular resistance (Ventetuolo and Klinger 2014). Dopamine has a dose-dependent effect on receptors. At doses less than 5  μg/kg/ min it acts mainly on dopaminergic receptors, from 5 to 10 μg/kg/min on beta-1 receptors and >10  μg/kg/min alpha 1 receptors. Dopamine shares similar risks to epinephrine at higher doses for arrhythmias (Ventetuolo and Klinger 2014). Vasopressin acts on V1 receptors and has been associated with pulmonary vasodilation via stimulation of endothelial nitric oxide at low doses. Inotropic agents can be used when additional support is necessary to maintain adequate perfusion. Prior to using inotropic agents, it is imperative that the mean systemic blood pressure is high enough to maintain perfusion as these agents can cause a drop in blood pressure which could precipitate RV failure if used in isolation. Dobutamine acts on beta-1 and beta-2 receptors. At doses of 5–10  μg/kg/min, dobutamine has been demonstrated to reduce PVR as it acts on beta-2 receptors (Ventetuolo and Klinger 2014). At higher doses, there is concern it can cause increased vasodilation and potentially lead to systemic hypotension (Ventetuolo and Klinger 2014). Milrinone is a phosphodiesterase-3 inhibitor and has been used in biventricular failure as it has been demonstrated to improve cardiac output while also reducing PVR (Ventetuolo and Klinger 2014). A relatively newer inotropic agent, levosimendan, improves cardiac contractility by increasing the sensitization of Troponin C to calcium (Ventetuolo and Klinger 2014). It has been demonstrated to act independently of cyclic-­ AMP, therefore may cause less ventricular arrhythmias as it does not use intracellular calcium. Levosimendan has vasodilating properties by a proposed mechanism of activating potas-

913

sium channels within the vascular smooth muscle cells. When studied in the setting of acute PE, levosimendan reduced PVR and increased RV contractility, and when compared to dobutamine in animal models levosimendan reduced RV afterload to a greater extent (Kerbaul et al. 2007; Kerbaul et al. 2006). At the current time, there is not a general consensus on what vasopressor or inotropic support is ideal for hypotension in the setting of PE; however, the goal should be to maintain mean arterial pressures (MAP) >65 to reduce RV ischemia. Selective pulmonary vasodilators such as inhaled nitric oxide (iNO) or inhaled epoprostenol can be used to counteract the vasoconstriction that can occur in acute PE. These agents are potentially ideal given their selectivity to the pulmonary vascular bed and their selectivity within the lung that may improve V/Q matching and oxygenation (Ventetuolo and Klinger 2014). Although numerous case reports describe the benefits of inhaled NO in acute PE, to date there are no randomized control trials (Kline et  al. 2017; Bhat et al. 2015).

Airway Management in PE Intubation should ideally be avoided in patients with hemodynamically compromising PEs. In a hypotensive patient, the transition from negative pressure to positive pressure ventilation can increase intrathoracic pressure and subsequently reduce RV preload. A further reduction in preload can compromise cardiac output which could further worsen a patient’s hypotension. If a patient does need intubation, assuring adequate mean arterial pressure with the use of vasopressors (discussed above) is key to prevent cardiac arrest. If a patient is intubated it is important to consider transpulmonary pressures, and lung volumes for these patients are at high risk for RV failure. Increased transpulmonary pressures can increase the load on the right ventricle by creating non-west zone III condition during lung inflations as discussed in Chap. 5 (Mitzner). When this occurs the downstream pressure for the RV is

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the alveolar pressure and not the left atrial ­pressure. Pulmonary vascular resistance changes with alterations in lung volumes secondary to compression of intra (arterioles, capillaries, and venules) and extra alveolar vessels (pulmonary arteries and veins) (Ventetuolo and Klinger 2014; Murray 1986) The ideal lung volume thus is likely around functional residual capacity. This is important to consider in ventilated patients, as FRC may not reflect lung-protective volumes in each patient.

Conclusion Submassive and massive PEs can lead to hemodynamic compromise, therefore early recognition and treatment is key. Diagnosis of RV dysfunction can be aided through troponin, BNP, and TTE. Management can range from anticoagulation, CDT, surgical embolectomy, and VA ECMO. Treatment should focus on the resolution of the clot while also maintaining mean arterial pressure and cardiac output with the utilization of vasopressors and inotropy. Given the challenging nature of many PE cases, PERT can be a valuable resource to determine the best course of action.

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916 Murray JF. The normal lung: the basis for diagnosis and treatment of pulmonary disease. 2nd ed. Philadelphia: Saunders; 1986. xi, 377 p. Penaloza A, Melot C, Motte S. Comparison of the Wells score with the simplified revised Geneva score for assessing pretest probability of pulmonary embolism. Thromb Res. 2011;127(2):81–4. PubMed PMID: 21094985. Reeves WC, Demers LM, Wood MA, Skarlatos S, Copenhaver G, Whitesell L, et al. The release of thromboxane A2 and prostacyclin following experimental acute pulmonary embolism. Prostaglandins Leukot Med. 1983;11(1):1–10. PubMed PMID: 6348800. Rosoff CB, Salzman EW, Gurewich V. Reduction of platelet serotonin and the response to pulmonary emboli. Surgery. 1971;70(1):12–9. PubMed PMID: 4326431. Scarvelis D, Anderson J, Davis L, Forgie M, Lee J, Petersson L, et  al. Hospital mortality due to pulmonary embolism and an evaluation of the usefulness of preventative interventions. Thromb Res. 2010;125(2):166–70. PubMed PMID: 19647292. Smulders YM. Pathophysiology and treatment of haemodynamic instability in acute pulmonary embolism: the pivotal role of pulmonary vasoconstriction. Cardiovasc Res. 2000;48(1):23–33. PubMed PMID: 11033105. Sofia M, Faraone S, Alifano M, Micco A, Albisinni R, Maniscalco M, et  al. Endothelin abnormalities in patients with pulmonary embolism. Chest. 1997;111(3):544–9. PubMed PMID: 9118685. Stein PD, Henry JW.  Prevalence of acute pulmonary embolism among patients in a general hospital and at autopsy. Chest. 1995;108(4):978–81. PubMed PMID: 7555172. Todd MH, Forrest JB, Cragg DB.  The effects of aspirin and methysergide on responses to clot-induced pulmonary embolism. Am Heart J. 1983;105(5):769–76. PubMed PMID: 6405602. Urban K, Kirley K, Stevermer JJ.  PURLs: it’s time to use an age-based approach to D-dimer. J Fam Pract. 2014;63(3):155–8. PubMed PMID: 24701602. Pubmed Central PMCID: 4042909. Utsonomiya T, Krausz MM, Levine L, Shepro D, Hechtman HB.  Thromboxane mediation of car-

J. McNeill and R. N. Channick diopulmonary effects of embolism. J Clin Invest. 1982;70(2):361–8. PubMed PMID: 6284801. Pubmed Central PMCID: 371244. Utsunomiya T, Krausz MM, Shepro D, Hechtman HB.  Prostaglandin control of plasma and platelet 5-hydroxytryptamine in normal and embolized animals. Am J Phys. 1981;241(5):H766–71. PubMed PMID: 7030086. Ventetuolo CE, Klinger JR.  Management of acute right ventricular failure in the intensive care unit. Ann Am Thorac Soc. 2014;11(5):811–22. PubMed PMID: 24828526. Pubmed Central PMCID: 4225807. Vlahakes GJ, Turley K, Hoffman JI.  The pathophysiology of failure in acute right ventricular hypertension: hemodynamic and biochemical correlations. Circulation. 1981;63(1):87–95. PubMed PMID: 7438411. Weidner WJ.  Effects of indomethacin on pulmonary hemodynamics and extravascular lung water in sheep after pulmonary microembolism. Prostaglandins Med. 1979;3(2):71–80. PubMed PMID: 552101. Yavuz S, Toktas F, Goncu T, Eris C, Gucu A, Ay D, et al. Surgical embolectomy for acute massive pulmonary embolism. Int J Clin Exp Med. 2014;7(12):5362–75. PubMed PMID: 25664045. Pubmed Central PMCID: 4307492. Yu T, Yuan M, Zhang Q, Shi H, Wang D. Evaluation of computed tomography obstruction index in guiding therapeutic decisions and monitoring percutanous catheter fragmentation in massive pulmonary embolism. J Biomed Res. 2011;25(6):431–7. PubMed PMID: 23554721. Pubmed Central PMCID: 3596723. Yusuff HO, Zochios V, Vuylsteke A.  Extracorporeal membrane oxygenation in acute massive pulmonary embolism: a systematic review. Perfusion. 2015;30(8):611–6. PubMed PMID: 25910837. Zarghouni M, Charles HW, Maldonado TS, Deipolyi AR.  Catheter-directed interventions for pulmonary embolism. Cardiovasc Diagn Ther. 2016;6(6):651–61. PubMed PMID: 28123985. Pubmed Central PMCID: 5220195.

Clinical Neurologic Issues in Cerebrovascular Monitoring

56

Thomas P. Bleck

The relationship between cerebral blood flow and the oxygen demands of the brain is frequently disrupted by disease. Focal disorders of blood flow occur acutely as a consequence of arterial disorders, most commonly ischemic stroke but also in the tissue adjacent to an intracerebral hematoma (primary, such as a hypertensive hemorrhage, or due to trauma or hemorrhage from a tumor). Cerebral venous occlusion also affects oxygen delivery, as venous stasis will prevent oxygenated blood from entering the affected tissue. Multifocal ischemia commonly occurs in the setting of cerebral vasospasm after aneurysmal subarachnoid hemorrhage, and may also be seen as a consequence of head trauma. Global cerebral ischemia is typically a consequence of cardiac arrest, but may also be seen in conditions such as drowning, hanging, or asphyxiation (physical or chemical). In addition to such primary injuries, any condition increasing intracranial pressure can impair cerebral perfusion. This relationship is discussed in detail in (Chaps. 11 and 23). In any setting, clinical evaluation of the patient often suffices to determine that global cerebral perfusion is adequate. If the patient is sufficiently

T. P. Bleck (*) Division of Stroke and Neurocritical Care, Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

conscious to make appropriate verbal responses, or to protect against noxious stimuli, perfusion at that time is adequate, and invasive pressure monitoring is not indicated for clinical management. However, since many causes of brain swelling will progress over hours, placement of an ICP monitor is often prudent based either on the likelihood of clinical worsening, or imaging findings that may presage such a decline. The choice of a monitoring device depends on many factors; parenchymal monitors are less invasive than external ventricular drains, but the latter allow removal of cerebrospinal fluid to reduce pressure as well as monitor it. Intracranial pressure monitoring allows the clinician to determine the cerebral perfusion pressure (CPP); CPP management is one of the cornerstones of acute brain trauma care for moderately or severely injured patients (Carney et al. 2017). Monitoring CPP requires an arterial line as well as an ICP monitor. The arterial line should be leveled at the level of the foramen of Monro for calculation of CPP, rather than at the phlebostatic axis, since when the head of the patient is elevated, the arterial pressure experienced by the brain will be reduced by the height of the column of blood above the heart (Thomas et  al. 2015). Practically, this is accomplished by keeping the transducer near the tragus of the ear, so that changes in head elevation are automatically applied to the arterial line.

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The optimal value of CPP is a subject of debate. In general, a CPP of 60 mmHg is the typical goal in adults, with age-dependent lower values in children. As discussed below, there are circumstances in which a higher CPP may be tolerated in order to improve cerebral oxygenation. Many therapeutic modalities are applied to improve CPP.  Raising the MAP with vasopressors is often required, and is usually the most rapidly effective. Lowering the ICP can be accomplished in several ways. If a mass lesion is responsible for elevating the ICP, its expeditious removal is usually an important component of treatment, but this may not be possible, or other therapeutic approaches may be required in order for the patient to survive long enough to get to the operating room. Table 56.1 presents a history of modern attempts to treat intracranial hypertension. Monitoring and management of CPP does not predict or prevent all problems with intracranial pressure; it is most useful when the cause of the ICP elevation is global. Herniation from one intracranial compartment to another occurs because of a pressure gradient, but even the higher pressure driving herniation may not exceed the commonly accepted upper limit of normal (e.g., 22  mmHg). Physical signs of herniation (e.g., pupillary or eye movement changes, or the development of asymmetric weakness or posturing) may also require therapies to lower ICP in order to decrease such gradients. Jugular venous oxygen measurements (of either saturation, which can be monitored continuously, or partial pressure, or content) were some of the earliest approaches to studying cereTable 56.1 Therapeutic approaches to intracranial hypertension 1918: Tentorial incision (Cushing) 1923: Osmotic diuretics (Fay) 1955: Hypothermia (Sedzimir) 1957: Hyperventilation (Furness) 1960: Ventricular drainage (Lundberg) 1961: Steroids (Gailich and French) 1971: Decompressive craniectomy (lateral: Ransohoff; bifrontal: Kjellberg) 1973: Barbiturates (Shapiro)

T. P. Bleck

bral oxygenation in trauma. The mixing of venous blood in the confluence of sinuses is rarely complete, however, and the values obtained when both jugular veins are studied may be quite disparate. Finding diminished oxygen in jugular venous blood is a reliable marker of inadequate delivery for demand, and thus indicates cerebral ischemia. A minority of patients, especially young patients, have had increased oxygen in their jugular veins; this was previously thought to indicate hyperemia, which might be safely treated with hyperventilation. However, this circumstance more likely indicates the inability to utilize the oxygen being delivered, either because of mitochondrial dysfunction or impaired diffusion (vide infra). This is not to say that true hyperemia never occurs, but recent measurements with better time resolution that the older studies often reveal that hyperemia may occur as a consequence of spikes in MAP, as the arterial pressure rise precedes the increase in CBF (Akbik et  al. 2016). The major factors controlling cerebral arterial caliber are the extracellular pH and the extravascular concentrations of potassium and nitric oxide; carbon dioxide itself plays a minor role, but is crucial because of its effect on pH. CO2 is freely diffusible across the blood-brain barrier, whereas buffers such as bicarbonate and phosphate are not. Thus, alterations in PaCO2 almost immediately change the cerebral extracellular pH; hyperventilation produces vasoconstriction, decreasing the caliber of arterioles (and possibly of venous structures), decreasing cerebral blood volume and thus ICP. This effect is very rapid, a drop in ICP occurring in less than 1 minute after hyperventilation begins. Once the increase in minute ventilation leads to a new PaCO2 steady state, it is common to see that the ICP slowly begins to rise. This is often interpreted as a failure of hyperventilation. However, the arteriolar response to the rise in pH does not appear to fatigue. Instead, three factors mitigate against a sustained effect on ICP. First, the choroid plexus begins to export bicarbonate when the CSF pH exceeds its normal value of 7.3 (Christensen et  al. 2018). (Another explanation based on the strong-ion difference to acid-base

56  Clinical Neurologic Issues in Cerebrovascular Monitoring

balance is that Na + is extruded which will lower the bicarbonate concentration  – see acid-base Chap. 41.) This compensation is complete within 6 hours in normal subjects, but appears to be slower in patients with brain injuries. Second, the cause of the ICP elevation is usually progressive, so the decreased blood volume will be counteracted by the pathologic process at work. Third, as the minute ventilation is further increased in subsequent attempts to control the ICP, the increases in tidal volume and respiratory rate will eventually result in increasing intrathoracic pressure to the point that jugular venous return is impaired, which itself will elevate the ICP. In the 1970s, investigators became concerned that excessive hyperventilation might produce cerebral ischemia. Mechanistically one might expect that the acidosis accompanying ischemia would counteract the vasoconstrictive effect, but it seemed prudent to examine whether hyperventilation might have an effect on outcome. Muizelaar et al. compared severely head injured patients who were randomized to PaCO2 partial pressures of 30–35 mmHg with those ventilated to PaCO2 partial pressures between 24 and 28 mmHg (Muizelaar et al. 1991). The value of 30–35 was chosen as the control because these patients often maintained themselves in that range when not controlled. Interim analyses at 3 and 6  months suggested that the more severely hyperventilated group had poorer outcomes as measured by the Glasgow Outcome Scale score, leading to premature termination of the trial. This difference was no longer present at 12  months, but this study is frequently invoked as an indication to avoid hyperventilation for ICP control. More recent work suggests that hyperventilation in the range of 30–35 mmHg does not adversely affect cerebral metabolism. Global insults such as head trauma can produce both generalized and localized abnormalities in blood flow. One technique to assess regional blood flow abnormalities is “cold” (nonradioactive) xenon CT scanning. A 1992 study of severely head-injured patients found that a substantial minority had diminished CBF, even in the absence of intracranial hematomata (Bouma et  al. 1992). This study found no patients with

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hyperemia; those with diffuse swelling had the worst CBF. Positron emission tomographic (PET) studies of hyperventilation using 15O-labeled water to measure CBF with high spatial resolution have yielded results with conflicting interpretations. The Cambridge neurocritical care group reported that hyperventilation increased the volume of hypoperfused tissue, although they noted that the response varied among patients and that no PaCO2 threshold for ischemia could be established (Coles et  al. 2002). They also noted that hyperemia was rare. The same group later showed that the oxygen extraction fraction (OEF) increased, in some regions to levels that the investigators considered critically high. Using similar techniques, the Washington University neurocritical care team obtained similar results, but considered that the increase in OEF represented successful compensation for diminished regional cerebral blood flow (Diringer et  al. 2002). This disagreement remains unresolved. One of the principles underlying CPP management is pressure autoregulation. Under normal conditions, CBF remains constant over a range of MAP extending from about 50 to 150 mmHg; this is accomplished by progressive constriction of arteriolar sphincters. However, pressure autoregulation may fail in pathologic situations. Studies in head trauma patients, using transcranial Doppler flow velocity measurements as a surrogate for actual CBF measurements, suggest that pressure autoregulation fails in about 55% of patients, resulting in CBF that increases passively as MAP increases through the normally autoregulated range (Lang et  al. 2003; Oertel et  al. 2002). A few studies using CT perfusion measurements suggest that this phenomenon may be less common, perhaps occurring in 24% (Peterson and Chesnut 2009). The slope of the increase in MAP appears to be steeper than the slope of CBF increase, indicating that raising the MAP can still increase the CPP, but not to the extent that would occur if pressure autoregulation was intact. Oxygen-derived free radicals are generated when previously underperfused areas experience a return of blood supply, especially when iron is

T. P. Bleck

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available to catalyze the Fenton reaction. Studies of free radical scavengers such as tirilazad mesylate, or substances to reduce free radical concentrations such as superoxide dismutase, have failed to improve outcome in brain injuries of various types. The largest such study, MRC CRASH, which used a dose of methylprednisolone that was a very effective free radical scavenger, actually had higher mortality in the active treatment group (Edwards et al. 2005). Chelating iron has similarly been ineffective. Improved strategies for dealing with oxygen-derived free radicals are in development (Ma et al. 2017; Frati et al. 2017). In the mid-1990s, deoxyglucose PET studies indicated the presence of hyperglycolysis in areas of severe injury, suggesting that mitochondria were unable to use the oxygen being delivered (Bergsneider et al. 1997). This finding correlated with microdialytic studies suggesting the same problem. Studies of mitochondria in resected tissue specimens indicated a defect in the function of mitochondrial transition pores, which could be partially corrected with cyclosporin (Yokobori et al. 2014). However, initial human studies with cyclosporin were discouraging, and research into its use in the treatment of this problem has stagnated (Mazzeo et al. 2009). More recent studies by the Cambridge neurocritical care group have underscored another potential cause of mitochondrial metabolic difficulty after trauma. Electronic microscopic analysis of tissue resected in the management of increased intracranial pressure suggests a substantial degree of impairment of oxygen diffusion from the capillaries to the mitochondria because of endothelial swelling, microvascular collapse, and perivascular edema (Menon et al. 2004). The finding that mitochondria may not be receiving adequate oxygen to supply the energy needs of the brain suggests that attempts to improve oxygen delivery may be useful. Tissue brain oxygen monitors, discussed in (Chap. 23), allow the clinician to introduce therapies to restore brain oxygen delivery to more normal levels. Early attempts to do so in head trauma (Spiotta et  al. 2010) and subarachnoid hemorrhage (Ramakrishna et al. 2008) appear promising. A phase 2 trial comparing conventional ICP/

Table 56.2 Techniques for improving brain oxygen delivery Goal Increase blood oxygen carrying capacity Increase oxygen dissolved in plasma Increase cerebral blood flow

Technique RBC transfusion

Increase FiO2 Increase PEEP Raise MAP with vasopressors Increase collateral blood flow by raising cardiac output with inotropes Allow modest vasodilation by slightly decreasing minute ventilation

CPP management to brain oxygen optimization showed that the techniques proposed for improving brain oxygen delivery were successful in accomplishing this goal safely (Okonkwo et  al. 2017). Although clinical outcomes were not the object of the study, both mortality and poor outcomes were lower in the brain oxygen group. Techniques used to improve brain oxygen are listed in Table  56.2. A phase 3 study has been funded to determine whether this strategy improves clinical outcomes. The future of cerebrovascular monitoring will depend in part on its inclusion as a component of multimodality monitoring. The most important additional techniques are surface and cortical electroencephalographic monitoring to detect sustained depolarizations, which appear to play an important role in secondary injury in both trauma and cerebrovascular disorders (Eriksen et  al. 2019). Originally described in 1944 by Leão as “spreading depression,” (Leão 1944) this phenomenon is capable of producing ischemia at sites removed from the original injury and helps to explain many potentially destructive phenomena such as delayed ischemic damage after subarachnoid hemorrhage.

References Akbik OS, Carlson AP, Krasberg M, Yonas H. The utility of cerebral blood flow assessment in TBI. Curr Neurol Neurosci Rep. 2016;16:72.

56  Clinical Neurologic Issues in Cerebrovascular Monitoring Bouma GJ, Muizelaar JP, Stringer WA, Choi SC, Fatouros P, Young HF. Ultra-early evaluation of regional cerebral blood flow in severely head-injured patients using xenon-enhanced computerized tomography. J Neurosurg. 1992;77:360–8. Carney N, Totten AM, O’Reilly C, et al. Guidelines for the management of severe traumatic brain injury, fourth edition. Neurosurgery. 2017;80:6–15. Christensen HL, Barbuskaite D, Rojek A, et al. The choroid plexus sodium-bicarbonate cotransporter NBCe2 regulates mouse cerebrospinal fluid pH.  J Physiol. 2018;596:4709–28. Coles JP, Minhas PS, Fryer TD, et al. Effect of hyperventilation on cerebral blood flow in traumatic head injury: clinical relevance and monitoring correlates. Crit Care Med. 2002;30:1950–9. Diringer MN, Videen TO, Yundt K, et al. Regional cerebrovascular and metabolic effects of hyperventilation after severe traumatic brain injury. J Neurosurg. 2002;96:103–8. Edwards P, Arango M, Balica L, et  al. Final results of MRC CRASH, a randomised placebo-controlled trial of intravenous corticosteroid in adults with head injury-­ outcomes at 6 months. Lancet. 2005;365:1957–9. Frati A, Cerretani D, Fiaschi AI, et  al. Diffuse axonal injury and oxidative stress: a comprehensive review. Int J Mol Sci. 2017;18:E2600. Lang EW, Lagopoulos J, Griffith J, et al. Cerebral vasomotor reactivity testing in head injury: the link between pressure and flow. J Neurol Neurosurg Psychiatry. 2003;74:1053–9. Ma MW, Wang J, Zhang Q, et  al. NADPH oxidase in brain injury and neurodegenerative disorders. Mol Neurodegener. 2017;12(1):7. Muizelaar JP, Marmarou A, Ward JD, et al. Adverse effects of prolonged hyperventilation in patients with severe head injury: a randomized clinical trial. J Neurosurg. 1991;75:731–9. Oertel M, Kelly DF, Lee JH, et al. Efficacy of hyperventilation, blood pressure elevation, and metabolic suppression therapy in controlling intracranial pressure after head injury. J Neurosurg. 2002;97:1045–53.

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Peterson E, Chesnut RM. Static autoregulation is intact in majority of patients with severe traumatic brain injury. J Trauma. 2009;67:944–9. Thomas E, Czosnyka M, Hutchinson P.  Calculation of cerebral perfusion pressure in the management of traumatic brain injury: joint position statement by the councils of the Neuroanaesthesia and critical Care Society of Great Britain and Ireland (NACCS) and the Society of British Neurological Surgeons (SBNS). Br J Anesthesia. 2015;115:457–88. Bergsneider M, Hovda DA, Shalmon E, et  al. Cerebral hyperglycolysis following severe traumatic brain injury in humans: a positron emission tomography study. J Neurosurg. 1997;86:241–51. Yokobori S, Mazzeo AT, Gajavelli S, Bullock MR.  Mitochondrial neuroprotection in traumatic brain injury: rationale and therapeutic strategies. CNS Neurol Disord Drug Targets. 2014;13:606–19. Mazzeo AT, Brophy GM, Gilman CB, et  al. Safety and tolerability of cyclosporin a in severe traumatic brain injury patients: results from a prospective randomized trial. J Neurotrauma. 2009;26:2195–206. Menon DK, Coles JP, Gupta AK, et al. Diffusion limited oxygen delivery following head injury. Crit Care Med. 2004;32:1384–90. Spiotta AM, Stiefel MF, Gracias VH, et  al. Brain tissue oxygen-directed management and outcome in patients with severe traumatic brain injury. J Neurosurg. 2010;113:571–80. Ramakrishna R, Stiefel M, Udoetuk J, et  al. Brain oxygen tension and outcome in patients with aneurysmal subarachnoid hemorrhage. J Neurosurg. 2008;109:1075–82. Okonkwo DO, Shutter LA, Moore C, et al. Brain Oxygen Optimization in Severe Traumatic Brain Injury Phase-II: A Phase II Randomized Trial. Crit Care Med. 2017;45:1907–14. Eriksen N, Rostrup E, Fabricius M, et  al. Early focal brain injury after subarachnoid hemorrhage correlates with spreading depolarizations. Neurology. 2019;92:e326–41. Leão AAP. Spreading depression of activity in the cerebral cortex. J Neurophysiol. 1944;7:359–90.

Delirium in the Critically Ill Patient

57

Alex K. Pearce, Jamie Labuzetta, Atul Malhotra, and Biren B. Kamdar

I ntroduction: ICU Delirium and its Consequences The Diagnostic and Statistical Manual of Mental disorders (DSM-5) defines delirium as “a disturbance of consciousness characterized by acute onset and fluctuating course of inattention accompanied by either a change in cognition or a perceptual disturbance, so that a patient’s ability to receive, process, store, and recall information is impaired” (American Psychiatric 2013). This disturbance is acute, typically developing over hours to days, and a stark deviation from the patient’s baseline. Moreover, this change in consciousness cannot be explained by another cause such as intoxication or an underlying medical condition (e.g., dementia).

A. K. Pearce (*) · B. B. Kamdar Division of Pulmonary, Critical Care, Sleep Medicine and Physiology, University of California San Diego, La Jolla, CA, USA e-mail: [email protected]; [email protected] J. Labuzetta Division of Neurocritical Care, Department of Neurosciences, University of California San Diego, La Jolla, CA, USA e-mail: [email protected] A. Malhotra UC San Diego, Department of Medicine, La Jolla, CA, USA e-mail: [email protected]

Delirium is common in the intensive care unit (ICU). Various studies have demonstrated that at least one-third of critically ill patients will develop delirium over the course of their ICU stay (Ely et  al. 2007; Ely et  al. 2004; van den Boogaard et  al. 2012; Salluh et  al. 2015). Incidence of delirium is even higher among mechanically ventilated patients (Ely et al. 2007; Ely et  al. 2004; van den Boogaard et  al. 2012; Salluh et  al. 2015). Delirium is associated with increased mortality and length of hospital admission (Ely et al. 2004; Ely et al. 2001a). A recent meta-analysis demonstrated that the development of delirium in the ICU was associated with a two-­ fold increased relative risk of in-hospital mortality (Salluh et al. 2015). An episode of delirium can have severe, lasting consequences extending beyond the index hospitalization. Patients experiencing ICU delirium have increased mortality up to 12  months following hospital discharge (Pisani et al. 2009). Delirium has been identified as an independent predictor of cognitive impairment 3 and 12  months following hospital discharge (Girard et  al. 2010a; Pandharipande et  al. 2013). Additionally, increased duration of delirium was associated with impairment in activities of daily living and motor sensory function (Brummel et al. 2014). To date, delirium has not been associated with an increased risk of mental health problems such as anxiety, depression, or post-­ traumatic stress (Wolters et al. 2016).

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Established tools have been validated for bedside detection of delirium in the ICU (Ely et al. 2001b). Identification of risk factors and evaluation of non-pharmacological and pharmacological management strategies are ongoing areas of investigation. Emphasis on identification, prevention, and treatment of delirium is vital in improving outcomes in critically ill patients. This chapter discusses the pathophysiology and risk factors for delirium in the ICU, along with management approaches.

Delirium Definition and Epidemiology Delirium is described as an acute disturbance of consciousness, characterized by fluctuating levels of inattention and disorganized thinking. While the clinical presentation of delirium is variable, it typically develops early during ICU admission, with the majority of afflicted patients developing delirium within the first 48 hours of their ICU stay (Peterson et  al. 2006). Clinical manifestations of delirium in the ICU are diverse and vary from patient to patient. Patients often experience impairments in memory and orientation, accompanied by difficulty with language and thought processes. Other frequently observed symptoms include sleep-wake cycle disruption, motor disturbances (hyperactive or hypoactive), mood changes, and hallucinations and/or delusions. Sleep-wake cycle disruption is often the earliest manifestation of delirium, characterized by insomnia, daytime sleepiness, and/or complete sleep-wake reversal. Inattention is also often present in the early stages of delirium. Patients can suffer from hallucinations, described as simple, visual, or somatic in comparison to more complex hallucinations associated with other major psychotic disorders (Meagher 2009). Delirium is categorized into three subtypes: (American Psychiatric 2013) hyperactive (agitated); (Ely et  al. 2007) hypoactive; and (Ely et al. 2004) mixed. Hypoactive delirium is characterized by lethargy, slowed speech, and psychomotor slowing (Liptzin and Levkoff 1992). Hypoactive delirium can be difficult to differenti-

ate from acute metabolic encephalopathy, and for this reason is often missed by clinicians (Pandharipande et al. 2007a). Hyperactive delirium is easier to recognize as patients are overtly agitated and often require pharmacologic or physical interventions. In critically ill patients, hypoactive and mixed delirium subtypes predominate, comprising 27–55% and 36–44% of delirium episodes in the ICU, respectively (Peterson et  al. 2006; Rood et al. 2019). Older patients (e.g., ≥65 years old) have a higher rate of hypoactive delirium as compared to younger patients (Peterson et al. 2006). As compared to hyperactive delirium, hypoactive and mixed delirium have been associated with higher mortality; however, further research is needed to evaluate this association (Rood et  al. 2019; Meagher et al. 2000).

Delirium Pathophysiology Though its mechanisms are complex and poorly understood, the high prevalence and adverse consequences of ICU delirium have motivated a growing body of research aimed at disentangling causal pathways. Several pathways have been proposed, including those involving neurotransmitter imbalance, inflammation, hypoxia, sleep disruption, and circadian rhythm disturbance. In critically ill patients exposed to a complex ICU environment, these pathways co-exist, rarely acting independently (Maldonado 2013). The theories underlying the pathogenesis of delirium provide a framework for studies focused on the identification, treatment, and prevention of delirium in the ICU. Neurotransmitter imbalance during acute illness, specifically acetylcholine (Ach) and dopamine, are linked to delirium. Acetylcholine plays an important role in attention and consciousness (Hshieh et al. 2008). Increased serum anticholinergic levels are often seen in patients with delirium (Flacker et al. 1998). The involvement of Ach is reinforced by the observation that anticholinergic medications are associated with delirium and hyperactivity (Han et  al. 2001; Meagher 2001). Despite these observations, delirium prevention trials evaluating the use of medications that oppose

57  Delirium in the Critically Ill Patient

anticholinergic activity, such as one involving the anticholinesterase inhibitor ­donepezil in the postoperative setting, have been largely unsuccessful (Liptzin et al. 2005). Dopamine excess has also been cited as a contributor to delirium development (Trzepacz 2000). The importance of the dopaminergic system is reflected clinically in the pharmacological approach to delirium management. Dopamine antagonism serves as a key target for delirium prevention, although most trials to date have been inconclusive (discussed in more detail in “Prevention and Treatment” below). Abnormalities in GABA and glutamate are also identified as participants in the delirium pathway. GABA levels vary based on different clinical scenarios (Maldonado 2013). For example, elevated GABA is thought to play a role in the development of hepatic encephalopathy (Maldonado 2013; Ahboucha and Butterworth 2004; Ahboucha et  al. 2004). Additionally, this mechanism has been linked with the observed association of benzodiazepine sedative infusions and incident delirium (Pandharipande et al. 2006). A neuro-inflammatory mechanism is also implicated in ICU delirium. A systemic inflammatory state is common in patients admitted to the ICU.  Elevation in inflammatory cytokines such as IL-8 is seen in patients with delirium (van den Boogaard et  al. 2011). Pro-inflammatory cytokines have been linked with decreased cholinergic activity (Eikelenboom et  al. 2002). Hypoxia, oxidative stress, and disruption in oxidative metabolism contribute to cerebral dysfunction (Seaman et al. 2006). Furthermore, the pro-inflammatory state experienced by many ICU patients can lead to disruption in the blood-­ brain barrier/endothelium allowing pathogens and cytokines to penetrate the brain, causing neuronal dysfunction leading to delirium (Slooter et  al. 2017). Hence, delirium is considered by some to be the central nervous system (CNS) manifestation of systemic inflammation (Maldonado 2008). Finally, sleep-wake and circadian rhythm disruption are implicated in the development of delirium (Jacobson et al. 2008). As discussed in “Definition and Epidemiology” above, sleep-­

925

wake disturbance is often an early sign of delirium. Melatonin is intricately tied to the sleep-wake regulation. Primarily synthesized in the pineal gland, melatonin release is suppressed by light and promotes a new cycle of melatonin synthesis (which peaks at night). Darkness then prompts melatonin release. Melatonin exerts considerable influence over the suprachiasmatic nucleus, which is responsible for maintaining circadian rhythms. Delirious patients often exhibit low levels of melatonin (Mo et  al. 2015). For example, irregular and low levels of melatonin are seen in the patients who develop delirium (Miyazaki et al. 2003). The irregular melatonin secretion alters the homeostatic circadian sleep-­wake rhythm (BaHammam 2006). The association between melatonin, sleepwake rhythm disturbance, and delirium is clear; however, a causal relationship has not been established. The relationship of melatonin, sleep-wake disturbance, and delirium represents intriguing areas of research.

Delirium Identification Recognition of delirium is a critical first step in management. Early studies showed that delirium is generally under-identified in real-world ICU settings (van den Boogaard et al. 2012). Several widely available delirium screening tools have been developed, and two, the Confusion Assessment Method-Intensive Care Unit (CAM-­ ICU) and Intensive Care Delirium Screening Checklist (ICDSC), are recommended in the Society of Critical Care Medicine Clinical Practice Guidelines (Ely et al. 2001a). The CAM was initially introduced for screening delirium in non-ICU hospitalized patients (Inouye et  al. 1990); a modified “ICU” version was subsequently developed for use in the critical care setting. The CAM-ICU is simple to perform, requiring 10 seconds) Briefly awakens with eye contact to voice (48 hours and traditionally is associated with elevated triglycerides, hepatomegaly, and refractory bradycardia (Kam and Cardone 2007). There is also rising interest in the use of ketamine for ICU sedation, although additional studies are needed. Currently, propofol and dexmedetomidine are preferred for sedation in the ICU; however, overall sedation strategy should be determined based on individual patient characteristics.

Conclusion Delirium presents a complex pathophysiologic phenomenon that frequently occurs in the intensive care environment and poses a challenge to clinicians and health care staff. It is associated with adverse patient outcomes both during the index hospitalization and months to years later. A multifaceted approach is required to identify and manage this common ICU phenomenon, including frequent identification of modifiable risk factors, optimization of the patient environment, sedation minimization, early mobilization/rehabilitation, and promotion of sleep-wake alignment.

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Obesity in Critically Ill Patients

58

Kathryn A. Hibbert and Atul Malhotra

Introduction With the rising prevalence of obesity (McTigue and Kuller 2008; McTigue et al. 2006), a nuanced understanding of the physiologic impact and clinical implications of obesity in the critically ill are integral to clinical practice (Malhotra and Hillman 2008). In the United States, recent data show that roughly one-third of the population have a normal BMI, one-third are overweight, and one-third are obese (BMI  >30  kg/m2) (McTigue and Kuller 2008; McTigue et al. 2006). In some areas of the country, the prevalence of obesity reaches >40%. In addition, morbid obesity (BMI >35–40 kg/m2) is on the rise, and these patients are increasingly prevalent in the intensive care setting. Across Europe, obesity prevalence varies by country but ranges from 9% to 30% and is also steadily increasing. These data emphasize the importance of obesity and related conditions in patient care. Even in a state of relative health, obesity has major effects on cardiopulmonary physiology, and some of these are K. A. Hibbert (*) Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA e-mail: [email protected] A. Malhotra UC San Diego, Department of Medicine, La Jolla, CA, USA e-mail: [email protected]

exacerbated by critical illness. Additionally, obese patients can be challenging to manage due to issues including line placement, transportation, drug dosing, and imaging. Of note, morbidly obese patients are often excluded from major clinical trials and therefore reliance on physiological principles is generally required to guide management.

Changes in Baseline Physiology Obese patients experience a number of changes in their baseline physiology (i.e., before critical illness) compared to lean controls (Owens et al. 2012). Baseline alterations in respiratory mechanics of obese patients include a decrease in total lung capacity (TLC), functional residual capacity (FRC), and vital capacity (VC), as well as increases in pleural pressure, and increased upper and lower airway resistance (Malhotra and Hillman 2008) (Fig. 58.1). The decreased TLC, FRC, and VC are due to an overall decrease in respiratory system compliance, which in turn is secondary to the increased weight of the chest wall and increased abdominal pressure due to obesity (Malbrain and Cheatham 2011; Malbrain et al. 2007). In studies that have carefully measured lung and chest wall compliance, measured compliance of the chest wall is  relatively normal, but the position of the pressure  volume curve is shifted to the right ­

© Springer Nature Switzerland AG 2021 S. Magder et al. (eds.), Cardiopulmonary Monitoring, https://doi.org/10.1007/978-3-030-73387-2_58

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K. A. Hibbert and A. Malhotra

936

VC TLC TLC

FRC

IRV

TV

ERV

RV

Fig. 58.1  Impact of obesity on lung volumes. In the setting of increased chest wall weight and increased pleural pressure, patients with obesity often have decreased tidal volume (TV), expiratory reserve volume (ERV), and residual volume (RV). These result in a lower vital capacity (VC), functional residual capacity (FRC), and total lung capacity (TLC)

TLC

Volume

Residual volume (RV)

Pressure

Fig. 58.2  Pressure volume curve of the respiratory system in obesity. Although the measured respiratory system compliance in patients with obesity is often low, the pressure volume curve is typically shifted to the right but has a similar compliance (slope)

(Behazin et al. 2010) (Fig.  58.2). In contrast, measured lung compliance is often low, likely due to atelectasis, and thus the measured value of

compliance varies with recruitment along the pressure volume curve (Owens et al. 2008). A key physiological parameter in obese patients is transpulmonary pressure, which is the distending pressure across the lung (i.e., airway opening pressure minus pleural pressure) and which is distinguished from trans-chest wall pressure (the difference between pleural and atmospheric pressures) (Mead et al. 1970). With the increased pleural pressure in obesity, transpulmonary pressure becomes less positive (or more negative during a spontaneous breath) and the lung parenchyma experiences less distending (and more collapsing) pressure. The increase in pleural pressure, and resultant decrease in transpulmonary pressure, varies with gravitational position. Obese patients therefore have considerable atelectasis in some regions (e.g., dependent on the lung), whereas other regions remain well aerated (e.g., non-dependent on the lung) (Schetz et al. 2019). Atelectasis in obesity results in both impaired gas exchange and decreased compliance (Pelosi et al. 1996, 1997, 1998). Some authors have questioned the occurrence of negative transpulmonary pressures in obesity, i.e. pleural pressure in excess of pressure measured at the airway opening (Bernard 2008). However, such pleural pressure elevations without complete lung collapse are commonly observed during forced exhalation, when expiratory muscle activity raises pleural pressure and airway opening pressure remains atmospheric (Loring et al. 2010). Other situations in which pleural pressure elevations are common are expiratory flow limitation and airway closure, in which high pleural pressure, which is the pressure outside of the major airways, leads to airway narrowing or collapse, thereby allowing alveolar pressures to exceed those at the airway opening. Negative transpulmonary pressures are therefore commonly seen clinically even if not directly measured, and as described above, the resultant atelectasis is an important consideration in obese patients, even in the absence of additional lung pathology. FEV1 and FVC are reduced in proportion to each other in obese patients, but small airways dysfunction has also been observed in obese patients, and in some, expiratory flow limitation (Dixon and Peters 2018; Dixon and Poynter 2016;

58  Obesity in Critically Ill Patients

Dixon and Suratt 2017; Shore 2008, 2013, 2017). This phenomenon can result in the development of intrinsic PEEP at rest. During exercise, it can cause air trapping and a dynamic increase in endexpiratory lung volume. Even in the absence of increased end-­ expiratory lung volume, alveolar pressure may be elevated at end-exhalation secondary to the increased intra-abdominal pressure observed in obesity. These changes, combined with the intrinsic mechanical loading of inspiratory muscles in obesity, increase the work and oxygen cost of breathing both at rest and during exercise (Pankow et al. 1998). A decrease in lung compliance and increase in airway resistance have been observed in sedated, paralyzed, and morbidly obese patients without underlying lung pathology (Pelosi et al. 1996; Suratt et al. 1984a; Cherniack 1958; Naimark and Cherniack 1960). As stated above, this decrease in compliance is likely due to atelectasis and a shift of the pressure-volume curve of the lung rather than alteration of the underlying parenchyma. Obese patients frequently have arterial hypoxemia and an elevated alveolar to arterial oxygen (A-a) gradient. These changes are hypothesized to be secondary to a combination of atelectasis with shunting and V/Q mismatch due to airway narrowing and variations in lung perfusion. Obesity has many important effects on non-­ pulmonary physiology, including vascular physiology and endocrine function, and it is accompanied by comorbidities that complicate critical illness. These issues are complex – for example, measured vascular pressures are higher in obese patients, which reflect both issues of accurate measurement and actual physiologic changes – and are reviewed extensively elsewhere in the literature (Malhotra and Hillman 2008; Pelosi et al. 1996).

Obesity and the Abdomen When considering chest wall compliance in obesity, the role of the abdomen is frequently under-­ appreciated, particularly in critically ill patients. Abdominal compartment syndrome is well recognized by trauma surgeons, but the recognition of its high prevalence in medical ICUs has been less well appreciated. Malbrain et al. performed

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an observational study in mixed ICUs across Europe and demonstrated a remarkably high prevalence of raised intra-abdominal pressure in unselected ICU patients (Malbrain et al. 2007, 2006a, b). Based on an elevated intra-abdominal pressure >20 mmHg, 8% of patients had evidence of abdominal compartment syndrome. Recognizing that normal intra-abdominal pressure is typically atmospheric, the authors reported that 58% of medical ICU patients had an intraabdominal pressure of >12 mmHg. In multivariate analysis, body mass index (BMI) was the only independent predictor of intra-abdominal pressure, emphasizing the critical importance of obesity in the ICU. Of note, obesity rates are much greater in the United States than in Europe, suggesting that the incidence may be even higher depending on the geographic setting and demographics. Raised intra-abdominal pressure has multiple consequences, particularly in the ICU. First, the elevated pressure can affect visceral organ function. For example, when IAP is sufficiently elevated, renal failure is thought to occur by compression of renal veins and the subsequent reduction in perfusion. Second, elevated intra-­ abdominal pressure can contribute to raised intra-­ thoracic pressure since some pressure is transmitted across the diaphragm. The extent to which IAP is transmitted to the thorax is variable and likely depends upon patient position, chronicity, and other individual chest wall mechanical characteristics and, as discussed above, can have important implications for transpulmonary pressure. Increased abdominal weight also increases diaphragmatic loading, which increases the work of breathing in spontaneously breathing patients (Cherniack and Guenter 1961). These effects are especially important in the supine position and should not be underestimated in the obese and critically ill patient.

 besity Control of Breathing/ O OSA/ OHS Obstructive sleep apnea (OSA) is a common condition with major neurocognitive and cardiovascular sequelae. Recent estimates suggest that at

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least 13% of men and 6% of women have clinically important OSA, with up to 1 billion people affected worldwide (Benjafield et al. 2019; Peppard et al. 2013; Heinzer et al. 2015). Obesity is a major risk factor for OSA, although up to 30% of OSA patients are not obese. OSA is characterized by intermittent hypoxemia and recurrent surges in catecholamines, which contribute to its clinical manifestations. OSA is now thought to be a complex disease with multiple underlying endotypes (Jordan et al. 2014). Anatomical compromise of the upper airway is a common underlying feature as measured by the critical closing pressure (Pcrit) of the airway (i.e., the pressure that must be overcome to keep the airway open) (Gold et al. 1996). A positive value for the Pcrit reflects a “floppy” airway that requires positive intraluminal pressure to restore patency (Strohl et al. 2012). On the other hand, a negative Pcrit value reflects a “sturdy” airway that requires subatmospheric pressure to promote collapse. Alterations in transmural pressure of the upper airway are particularly important for non-intubated patients, including post-extubation (Epstein 2002). In addition to anatomy, pharyngeal dilator muscle function is thought to be affected in some patients. This dysfunction in upper airway muscles can be further exacerbated with sedative medications including benzodiazepines and propofol (Malhotra et al. 2000, 2001; Genta et al. 2011; Eastwood et al. 2002). Lastly, instability in ventilatory control (elevated loop gain) also plays an important role in a subset of OSA patients (Khoo 2000a, b, 2001; Younes et al. 2001; Wellman et al. 2003, 2004, 2007). Unstable ventilatory control may promote patient/ventilator asynchrony, although data in this context are sparse (Beitler et al. 2016a; Meza et al. 1998). Obesity hypoventilation syndrome (OHS) occurs in a subset of patients with OSA (and rarely in the absence of OSA) and has several important implications in critical illness (Nowbar et al. 2004; Berger et al. 2001). OHS is defined by elevated PaCO2 in the context of obesity without major parenchymal lung disease and is thought to result from the combination of a low central ventilatory drive and abnormal respira-

K. A. Hibbert and A. Malhotra

tory mechanics (O’donnell et al. 1999). In aggregate, the data suggest that at least 10–20% of obese patients being evaluated for OSA have OHS, although the diagnosis is still proportionately infrequent and may be under-diagnosed (Mokhlesi 2010; Mokhlesi and Tulaimat 2007). Indeed, data suggest that roughly 30% of patients with a BMI of 40  kg/m2 will have evidence of OHS if appropriately evaluated (Mokhlesi et al. 2019). Second, several studies suggest that even though OHS is a chronic condition, OHS commonly comes to clinical fruition in the ICU (Esquinas and BaHammam 2013; Sequeira et al. 2017). That is, patients with respiratory infection may arrive with an unexplained elevation in bicarbonate or, in some cases, they are misdiagnosed as acute exacerbations of chronic obstructive pulmonary disease based on assumptions in the setting of an acute or chronic hypercapnia. Thus, an appropriate level of clinical suspicion is required for this diagnosis to optimize management of these patients. Third, considerable data have shown that a diagnosis of OHS changes management not only in the ICU but also during follow-up. Although newer strategies are available for non-invasive ventilation of these patients, including volume-assured pressure support modes, the bulk of the recent data suggests that nasal CPAP (continuous positive airway pressure) provides good long-term results (Masa ­et al. 2019). The impact of OSA and OHS on critical illness is complex and incompletely studied. Potential implications include management considerations for non-intubated patients who have vulnerable upper airways and the potential for abnormal breathing patterns including ventilator asynchrony and spontaneous breathing patterns. Chronic intermittent hypoxia and hypercapnia also have metabolic sequelae that affect the critically ill (Xue et al. 2017). These factors can contribute to cardiovascular risk and also may include a protective effect in some ICU patients through ischemic preconditioning (Sanchez-dela-Torre et al. 2018). Lastly, the clinician must understand these effects in order to return patients to their baseline physiology and a state of relative health.

58  Obesity in Critically Ill Patients

 besity as a Risk Factor for Critical O Illness In addition to the changes in respiratory mechanics, obese patients experience chronic alterations in circulating inflammatory mediators derived from adipose tissue (collectively known as adipocytokines) (Mantzoros et al. 2011). Obese patients have increased circulating levels of cytokines (including TNF-α and IL-6), increased chemokine production (including IL-8), and altered levels of hormones produced by adipocytes such as leptin and adiponectin. A causal link between this inflammatory milieu in obese patients and asthma or airway hyper-responsiveness has been more thoroughly explored than the potential link with other critical illnesses (Dixon and Peters 2018; Dixon and Poynter 2016; Shore 2010). Additionally, in the setting of the baseline changes in physiology described above, obese patients may be more likely to have acute respiratory failure and even to meet criteria for acute respiratory distress syndrome (ARDS) without true lung injury, given their propensity to atelectasis and subsequent hypoxemia (Pepper et al. 2014, 2016, 2017, 2019).

Airway Management Airway management is a key issue in critically ill obese patients. Even relatively healthy obese patients who undergo surgery have an increased risk of complications and respiratory failure perioperatively. There is ongoing discussion about the degree to which obesity predicts difficult endotracheal intubation. Other predictive tools such as Mallampati scale may outperform body mass index (BMI) as a prognostic tool (De Cassai et al. 2020; Moon et al. 2019). Obese patients often have anatomical changes to head and neck regions that can make intubation difficult. These include increased soft tissue mass in the neck, decreased airway diameter, and increased airway collapsibility (Shore 2017; Pankow et al. 1998). When sedation and paralytics are given, the critical closing pressure (Pcrit) of the upper airway increases as muscle tone decreases and also is

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affected by other factors such as airway structure. Thus, complete airway closure is common during intubation of obese patients following muscle relaxation (Suratt et al. 1984a; Cherniack 1958; Naimark and Cherniack 1960; Suratt et al. 1984b). Based on the known effects on respiratory mechanics, obesity results in reduced end-­ expiratory lung volume and the subsequent atelectasis, and ventilation/perfusion mismatch can yield rapid desaturation during intubation, despite pre-oxygenation. There is also an increased prevalence of gastroesophageal reflux disease (GERD) in obese patients that can complicate intubation by increasing risk of aspiration. Although many patients will do well with the standard practice of “rapid sequence intubation” (with use of short-acting neuromuscular blockade, short-acting sedatives, and standard technique of pre-oxygenation), if there are additional issues that may lead to difficult intubation, then it is reasonable to pursue an awake fiberoptic intubation (Brodsky et al. 2002). There are also additional considerations when extubating obese patients, especially those with OSA who are predisposed to airway collapse and who have increased risk of re-intubation. Residual effects of sedatives and muscle relaxants may be more pronounced in obese patients given their unique pharmacodynamics. In addition to decreased wakefulness drive to breathe as sedatives wear off, the presence of an endotracheal tube can blunt upper airway reflexes that normally help maintain airway patency (Benumof 2001; Brown et al. 2010). Non-­invasive positive pressure ventilation can assist in successful extubation and can help prevent reintubation in highrisk patients (de Larminat et al. 1995). Notably, use of non-invasive ventilation after the development of post-extubation respiratory failure has been shown to delay, but not prevent, reintubation and is also associated with high mortality (Nava et al. 2005; Esteban et al. 2004). Conversely, pre-emptive non-­invasive ventilation at the time of extubation of high-risk patients has been shown to shorten the duration of invasive mechanical ventilation without increasing the risk of reintubation, ICU length of stay, or mor-

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tality. These data suggest that non-­invasive ventilation may be useful in patients who are at high risk of extubation failure, though this strategy is not specific to obese patients. We recommend extubation to non-invasive ventilation in patients with known OSA and history of failed extubation, as well as patients in whom there is co-existing CO2 retention due to either obstructive airways disease or obesity hypoventilation syndrome. Although the role of tracheostomy in the ICU has been debated, it does represent definitive therapy for OSA.  Thus, we consider tracheostomy in our ICU patients with variable CNS status and complex cardiopulmonary disease, particularly with underlying OSA or control of breathing abnormalities.

 entilator Management in Obese V Patients When obesity is coincident with respiratory failure, there are specific issues for mechanical ventilation and management. With increased abdominal pressure and concomitant elevated pleural pressure, obese patients develop atelectasis and therefore have a more heterogeneous lung at baseline, with some areas of lung being well aerated and other areas being relatively collapsed. This heterogeneity may be compounded in the acute respiratory distress syndrome (ARDS), in which increased pleural pressure is combined with increased surface tension due to surfactant dysfunction. Obese patient with ARDS can thus experience considerable atelectasis with resultant gas exchange abnormalities and potential risk of additional lung injury. Atelectasis in ARDS predisposes to ventilator-associated lung injury (VALI) in multiple ways. Cyclic opening and closing of lung units can result in shear stress, the so-called atelectrauma (Slutsky and Drazen 2002; Slutsky and Ranieri 2013). There are also concentrations of stress at the intersection of open and closed alveoli (i.e., junctions between normal and abnormal lungs). On conventional ventilator settings, the effective pressures generated in these heterogeneous areas are estimated theoretically to exceed 100 cmH2O, which greatly

K. A. Hibbert and A. Malhotra

surpasses the generally accepted “safe” maximum transpulmonary pressure of 25 cmH2O (Mead et al. 1970). Lastly, atelectasis results in a small functional lung and so even a “low tidal volume” ventilation strategy may result in high regional strain, the so-called volutrauma (Beitler et al. 2016b). These mechanisms of lung injury have led to an “open lung” strategy of ventilation, in which attempts are made to create parenchymal homogeneity. Strategies to achieve this end have included recruitment maneuvers (e.g., applying sustained high airway pressures (40 cmH2O) for brief periods) and PEEP titration to optimize respiratory mechanics (Hubmayr and Malhotra 2014). This “open lung” strategy has traditionally been balanced with attempts to minimize airway pressures, which are also thought to contribute to ventilator-associated lung injury (the so-called barotrauma). The relevant distending pressure to the lung parenchyma is the transpulmonary pressure, and thus in theory reducing transpulmonary pressure may be a desirable target to reduce lung injury. In the setting of obesity with raised pleural pressure, high plateau airway pressures can be applied without lung overdistention since transpulmonary pressures 35 kg/m2) did not demonstrate a physiological benefit to PEEP titration by esophageal manometry compared to titration to optimal respiratory system compliance. In the absence of compelling data to suggest improved outcomes with any specific titration strategy, we recommend titration of PEEP based on local expertise and individual responses. Methods may include use of esophageal balloons, analysis of airway pressure-time curve profiles (stress index), and titration of PEEP to optimize tidal compliance, recognizing that the individual response to therapy may be an important consideration. Importantly, even though higher airway pressures can be tolerated in obesity, the strategy of low tidal volume ventilation is still paramount, and tidal volumes should be based on ideal body weight (Malhotra 2007). This recommendation is because as body weight increases, lung size does not increase concomitantly and, therefore, individuals of the same height and different weights should receive the same tidal volume (approximately 6  ml/kg ideal body weight (IBW)). It must be noted that this low tidal volume strategy is frequently accompanied by hypercapnia, which in obese patients may reflect both the acute illness and chronic hypoventilation. In addition to the titration of PEEP, prone positioning may be an important recruitment

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strategy in obese patients with lung injury. Proning allows the weight of mediastinal tissue to be supported by the sternum, and thus less lung tissue may be susceptible to collapsing forces (Scholten et al. 2017). These issues are especially relevant in obese patients. Prone positioning in the obese patient may offer the same putative benefits as in lean patients, including more homogeneous distribution of perfusion, recruitment of atelectatic lung in the non-dependent position, and improved oxygenation through reduction of shunt and improved ventilation/perfusion matching. Small case-controlled clinical studies have demonstrated the safety of proning in patients with obesity (BMI ≥35 kg/m2) and a greater improvement in oxygenation compared to leaner patients, perhaps reflecting a greater fraction of atelectatic lung that is recruitable. Intra-abdominal pressures may increase in the prone position, and that increased IAP may be transmitted across the diaphragm to the pleural space leading to atelectasis. This effect may depend on whether the abdomen is suspended (e.g., in a specialty bed) or is lain upon by the patient in the prone position. Additionally, the impact of obesity and fat distribution with resulting influences on position-induced changes in abdominal pressure has not yet been studied, and it may be a critical variable explaining some of the variance in clinical trials. Additionally, there may be logistic concerns for transitioning very obese patients between the supine and prone positions. However, there are no data to suggest reduced benefit of prone positioning in obese patients with ARDS. These important physiological considerations are also relevant to the liberation of patients from mechanical ventilation. A study of obese patients without lung injury and also of patients with ascites demonstrated that a reverse Trendelenburg position at 45 degrees facilitated liberation from the ventilator. This finding is presumably due to a reduction in trans-diaphragmatic pressure, decreased atelectasis, and improved gas exchange with the postural change. Such benefit may also be seen prior to ventilator liberation in patients with a large fraction of recruitable lung (Richard et al. 2006).

K. A. Hibbert and A. Malhotra

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Additional Considerations

Obesity and Outcomes

There are multiple additional considerations in the care of critically ill obese patients. Many critical care trials have excluded patients with a weight greater than 1 kg/cm of height (approximately BMI of 45 kg/m2), and even those studies without an exclusion for obese patients have had a relatively moderate mean BMI in their study population leading to perhaps limited ability to extrapolate to the morbidly obese population (Guerin et al. 2013). At many institutions, some therapies such as extra corporeal membrane oxygenation (ECMO) remain available only to patients below a threshold BMI (e.g., 40–45 kg/ m2), and so experience and data to guide use are limited. Special considerations are required for interventions such as central line placement in morbidly obese patients. Variations in fat distribution and underlying anatomy may make one site preferred over another (e.g., avoiding subclavian site if the clavicle is not palpable). The length of the central line is also important since anecdotes have shown that infusions can extravasate into the soft tissues even when the distal tip of the line is in the lumen of the vessel. In addition, the femoral site may be associated with risk of deep venous thrombosis, which may be a particular concern in obese patients. Appropriate prophylaxis is recommended in all ICU patients particularly among the obese. We typically recommend both anticoagulant therapy and sequential compression devices (SCDs) if there are no contraindications in morbidly obese patients, even though recent data have not supported the use of SCDs for this purpose in general ICU patients (Arabi ­et  al. 2019). Pharmacokinetics in obesity is also unique. There can be delayed clearance of lipophilic drugs, which can lead to extended sedation. Drugs that are dosed by weight may also be optimally dosed by ideal body weight (IBW) or by lean body weight (LBW), while others should be dosed by total body weight (TBW) based on volume of distribution (Bernard et al. 2001).

The clinical literature regarding obesity and outcomes in the ICU has produced somewhat mixed results and is in need of further study. In several cohort studies, obesity has been associated with improved outcomes, although the mechanism underlying this observation is unclear. Several potential theories have been proposed but no definitive answer has emerged. In theory, obese patients may be less susceptible to iatrogenic injury since they are subject to fewer procedures and less transport for radiology assessments. In addition, some have suggested a survivor effect in surgical intensive care units (i.e., the sickest patients with morbid obesity are unlikely to undergo elective surgery). Obesity effects on the chest wall may also have a protective role from the standpoint of overdistension since the elevated pleural pressure associated with obesity results in lower transpulmonary pressure compared to lean individuals. Patients with obesity may also be more likely to qualify as having moderate or severe ARDS due to underlying atelectasis and not true lung injury and, therefore, may have a higher chance of survival. Thus, further work is clearly needed in this area (Pepper ­et al. 2014, 2017, 2019).

Conclusion The many alterations in baseline physiology with obesity and the impact of obesity on critical illness are increasingly important as the prevalence of obesity continues to increase. A thorough understanding of the physiological considerations related to obese patients with lung injury is becoming essential for optimal patient care given the obesity pandemic and ongoing prevalence of ARDS.  An individualized approach to the care of these patients can be invaluable, since a “one-­ size-­ fits-all” approach may be problematic for some patients. We support further clinical trials using individual patient measurements and response to guide therapy rather than overly simplified algorithms that are likely to provide heterogeneous results.

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Part VIII Epilogue

59

The Future Sheldon Magder, Charles C. Hardin, Kathryn A. Hibbert, and Atul Malhotra

Speculations on the future must inevitably be a mixture of fears and hopes, anticipating new challenges but also adopting new ways of dealing with them. We will begin with the fears to get the depressing side out of the way before dealing with our positive hopes for the future. Populations are aging, and although many older individuals are increasingly functional, when ill they have little reserves. Aging also is associated with a progressive loss of immune capacity, in what is called immune senescence (Weyand and Goronzy 2016; Goronzy and Weyand 2013). Because of the many newer lifepreserving therapies, patients who would have died at an early stage from their disease are now living longer but the therapies prolonging their lives often increase their vulnerability to other diseases, compromise their immune systems, and decrease their overall functional status. These S. Magder (*) Royal Victoria Hospital (McGill University Health Centre), Departments of Critical Care and Physiology McGill University, Montreal, QC, Canada e-mail: [email protected] C. C. Hardin · K. A. Hibbert Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA e-mail: [email protected]; [email protected] A. Malhotra UC San Diego, Department of Medicine, La Jolla, CA, USA e-mail: [email protected]

therapies also often are extremely expensive and are putting an increasing burden on the already high costs of health care. As the world increasingly becomes a global village, the old challenges of viral illnesses, such as the COVID-19 pandemic, are likely to become more common. Yet, besides vaccines, we still have minimal direct therapies for most viral illnesses that compare to the direct actions of antibacterial agents. Our current management of patients who are seriously ill from a viral infection is limited to providing lifesustaining therapies while waiting for host defenses to deal with the invading organism. Therapies that effectively direct attack viruses thus will need to be a major goal for the future. We also can expect an ongoing escalation of the negative health effects of climate change, including its impact on infectious diseases, air quality-related lung disease, and potential negative health consequences from global warming. Worsening economic inequity will exacerbate these issues, as we have seen during the COVID-­19 pandemic in which the concentration of resources often has been away from the most heavily impacted communities (Danziger 2020). With modern critical care, even very frail individuals can be supported through acute illnesses, but considerable resources are frequently required to do so. This demand will further aggravate the already very large variations in health care distribution which is heavily affected by where one lives and socio-economic status. Because of

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impending limits on available support, it is likely that physicians will have to deal with the difficult task of balancing the needs of the patient in front of them with the impact on society as a whole. Labor issues also likely will become worse because the population is aging and there will be fewer individuals of working age to provide the needed care, especially long-term care for the elderly and incapacitated. These issues, while distinct, are closely related to the ever present need to judge the appropriateness of increasingly sophisticated and invasive therapies with the goals, values, and prognosis of the individual in a patient-centered approach to care. The availability of increasingly specialized interventions, such as Extra Corporal Membrane Oxygenation (ECMO), also will present new demands on the organization of care. We will need to consider what structures should be put into place to facilitate the transfer of patients from smaller facilities to centers that have the structures, experience, budget, and economy of scale to provide these more sophisticated therapies while still maintaining equitable access to these therapies. Indeed, as critical care matures as a field, it may be important to explore whether formal triage and transfer systems, similar to those in place for trauma centers, are required. On the other hand, there is a lot of hope for the future. Life expectancy around the world has steadily increased, and even more importantly, the aged are living more functionally. In the last two decades, there has been an important decrease in the mortality of patients presenting with sepsis despite aging and sicker patients. What is even more striking is that these improvements in sepsis outcomes have occurred despite the failure to show survival benefits in almost all major randomized trials of new therapeutic approaches in the critically ill. It can be said that in recent years, the major theme in critical care research has been the extreme rarity of positive trials. Importantly, many studies included innovative therapies that had strong basic science rational, but still failed to show clinical benefits. A major priority in the near term will be sorting out the reasons for these failures. Importantly, we must ask if failure was a result of inadequate understanding of the biology,

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the heterogeneity of patient populations, or was it simply because of the technical challenges involved in performing clinical trials in the ICU. In the meantime, it seems fair to say that improvements in outcomes of critically ill patients have largely come from a better understanding of the underlying pathophysiological processes and the course of diseases, as well as the widespread adoption of relatively simple supportive interventions. Two of the most significant advances likely have been the appreciation that early identification and treatment of patients in shock prevents a downhill spiral to death, and that less intervention is better than more! Surgical outcomes, too, have dramatically improved over the past decades. In the 1990s mortality in reports from studies that attempted to augment oxygen delivery with high-risk surgery was 20% (Boyd et al. 1993) but it was down to less than 2% in the control group 20  years later in a study by the same investigators (Pearse et  al. 2014). This change is likely because clinicians now have a better understanding of what matters and what does not. A remarkable example has been the shortening of hospital stay for colorectal surgery from 10 to 12 days to the current 2 days and in some cases, same day; and this has come with a marked reduction in surgical mortality (Gustafsson et al. 2019; Ljungqvist et al. 2017). The increasing use of less invasive approaches with laparoscopy and radiological guidance likely have had a major impact on outcomes, and the use of these approaches continues to expand. Greater use of simulation for education likely also has helped across critical care. This technology has allowed clinicians to gain more hands-on and technical experiences and to develop a systematic approach to management, even with a declining number of actual cases. Further refinement of basic clinical skills and more rational, physiologically based therapies likely will continue to improve outcomes even without elaborate new technological developments. The rapidly expanding capacity to collect, store, and analyze bedside data likely will provide additional insights into ways to improve patient outcomes (Seymour et  al. 2019a; Knaus and Marks 2019) but this, too, will come with its

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own set of challenges. Big data approaches already have been used to describe different patient phenotypes that are indicative of which patients are expected to respond to a specific therapy versus those who will not (Knaus and Marks 2019). Currently, this information is largely only available retrospectively (Seymour et  al. 2019b), but with improved computing capacities, and artificial intelligence techniques, this type of information likely will be available in real time and could provide prospective insights. This information still may not be useful in individual patients because data are collected with tight restrictions that are needed to allow population-­ based statistics. However, insights gained should allow us to further refine therapies by providing constant feedback about the patient’s potential outcome. It should also strengthen the use of Bayesian approaches to support decision-making (Browner and Newman 1987). Given the intense interest in machine learning and big data approaches, it seems safe to predict widespread availability of decision support of this nature in the future. However, it also is worth sounding a note of caution. The fields of phenotyping and data analysis will face a crucial fork in the road in the near future. Do we content ourselves with outcome associations and purely statistical knowledge, or do we use the insights available from data analysis to formulate and test the mechanistic hypothesis? we still contend that bedside medicine is about formulating a hypothesis about the cause of a patient’s condition and a hypothesis of how to treat it. With this construct, the data given to the clinician only can be used to further strengthen the probability of the hypotheses and support the continuation of the same clinical approach, or it could indicate inconsistencies which should trigger re-evaluation of the hypotheses and the potential need to develop new ones. A tremendous advance over the past decade has been the immediate access to medical information. Gone are the days when it was necessary to go to a library to find an article on a specific subject. The world literature is now available at the bedside to any physician who has a modern cell phone. It will be important to make sure that

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this essential information remains accessible to all. A down side to our access to so much information is that the amount of information can become overwhelming and indiscriminant. This is already an issue with patients and families who regularly come to us with advice from Dr. Internet! In the future it will be essential that the organization, prioritization, and evaluation of new evidence become more efficient. Individual journals will continue to provide a general format for special interests and detailed analysis but there likely will be the expansion of the “encyclopedic” approaches which already have developed links to the supporting evidence on their sites. These are necessary to allow clinicians to make critical evaluations for the benefit of the patient in front of them rather than just considering the “mean” population-based response. With easy access to summary information, it will be important to encourage medical professionals making decisions that have major clinical implications to go back to the original data and assess the strength behind the recommendations. There thus needs to be a component of the personalized physician and not just personalized medicine. This philosophy also will need to impact on medical education. Curriculums are moving more and more to management driven by guidelines and formulaic treatments and so is the evaluation of trainees. Less time is spent in the curriculum on developing the scientific skills, as well as statistical skills, to evaluate the evidence that guidelines are based upon. It will be important to make sure that physicians of the future obtain the capacity to deal with the rapid increase in medical knowledge. Increased understanding of the pathophysiologic pathways in critical illness has offered up many potential therapeutic targets. However, despite these insights into the basic biochemical pathways, there have been few therapeutic advancements. This likely is because of the complexity and redundancy of the pathophysiology of critical illness, and caution is warranted rather than enthusiastic early adoption. A common factor in many failed clinical trials in the critically ill is that a single therapeutic agent was thought to be able to reverse a complex and multifactorial process that has many redundant effectors such

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as septic shock. Classic examples of this simplistic reasoning were the targeting of single signaling proteins such as tumor necrosis factor (Abraham et al. 1995; Abraham et al. 1997) and other cytokines, use of antibodies to block endotoxin (McCloskey et al. 1994; Cross 1994), and inhibition of nitric oxide production (Lopez et al. 2004). While these mediators are key factors in the inflammatory process, none of them are solely responsible for the inflammatory cascade in critical illness, and each also is involved in normal physiological functions, including activation of anti-inflammatory processes. Many other potentially important targets likely will emerge, but in the future, we will need to avoid the temptation of targeting a single molecule and plan more coordinated or personalized approaches. Decoding the human genome had promised a great potential for individualizing care. Identification of genetic polymorphisms explains in part why some patients have worse outcomes with the same disease and the same therapy (Rautanen et al. 2015). Faster identification of an individual’s genetic code could therefore provide better patient-centered care. However, although decoding of the genetic code has given insights into many new biochemical pathways, to date, it has added very little to therapeutic outcomes in the critically ill. It is worth paraphrasing Edmund Burke: “Those who don’t know history are destined to repeat it” (Burke n.d.). Just as targeting single agents in the pathophysiologic pathways of critical illness failed to yield powerful therapeutic approaches, the promise of so-called personalized medicine based on more precise knowledge of a patient’s genetic profile has failed to deliver outcome improvements and likely will not provide a simple solution. Biological systems are complex. They are composed of pathways with multiple interactions that act across a spectrum that seeks to develop thermodynamic equilibrium and not with a dichotomous response. It will be difficult, therefore, to precisely titrate therapy in an individual patient based only on a genetic code. Furthermore, identifying the genetic code responsible for proteins production is just the first step. In addition to the transcriptional and translational controls that determine

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actual protein production, other variables usually increase or decrease protein activity so that it is very difficult to predict what will happen in an individual patient under all conditions. On the other hand, the use of a specific therapy in all patients should occur with constant assessment of the responses and adjustments as necessary in what we have called “responsive” management. This part of patient-centered care is essential. Another molecular approach being considered is the manipulation of the epigenetic processes that regulate protein expression (Browner and Newman 1987). This approach has been used with success for cancer treatments, but the abnormal processes in cancers are in specific cell types and persist over long periods of time. In contrast, in critically ill patients processes are more complex and acute. The pathological process effect multiple organs, and each one has its own varying expression profile. Furthermore, processes are constantly and rapidly changing with a time scale of minutes to hours. A patient with severe sepsis can go from being in refractory shock to being awake, stable, and recovering within 12 hours if the offending invasive agent is rapidly identified and treated. A better use of molecular tools may be to expand the phenotypic description of critically ill patients. Current monitoring is largely limited to heart rate, blood pressure, temperature, urine output, lactate and white blood cell count, and perhaps cardiac output. A rapid deeper biochemical profile obtained with modern molecular tools at the bedside could provide more detailed insights into underlying processes. The same way that a falling lactate, falling creatinine, and rising urine output indicate that a patient is responding to therapy, more specific biochemical markers could add more sensitive and rapid insights into underlying processes and allow an adaptive approach to therapy. For example, perhaps there could be a detector of the ongoing activity of nuclear factor-κB (NFκB), a central transcriptional regulator of many molecules in the inflammatory cascade, or ongoing monitoring of the redox state in blood, or perhaps even in tissues of critically ill patients. In this approach, these disturbed biochemical processes would be used as indicators

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of the course of disease and not as targets of therapy similar to the way that lactate currently is used, but hopefully with more precision and more insights into underlying processes. Readily available, and more detailed phenotyping and endotyping of critical illness, perhaps even at the organ level, also will allow more efficient and high yield clinical trials, either through predictive enrichment (including only those patients most likely to benefit from a therapy) or through a responsive trial design that adapts the interventions based on patient responses. Importantly, these tools will have to have a proven benefit compared to standard bedside evaluation. For example, one of the best indicators of a patient’s state is wakefulness. If a previously comatose patient is awake and talking, or at least interacting spontaneously and coherently, it likely does not matter what the value is of any other indicators! Bedside assessment likely will remain a cornerstone of patient evaluation and care. An important limitation on the utility of newer bio-­ markers will likely not be the assay of biomarkers but rather the knowledge that it is necessary to interpret the significance of the level of the biomarker in the appropriate context. Genetic and molecular techniques have great potential for the treatment of infectious agents, but here the greatest benefit likely will be derived from the genetics of the invading organism rather than the host’s genetics. Recent experience with the COVID-19 pandemic illustrates the potential of these techniques to rapidly identify the organism as well as to track its epidemiology by detailed genetic analysis. Rapid genetic identification of pathogenic organisms can allow rapid identification of potential resistance to current standard treatments. This approach would be a major breakthrough because we have learned that the faster and more precise the treatment of an invading organism, the better the outcome. Better genetic profiling of invading organisms potentially also can help us understand the transmission of resistant organisms and strengthen public health policies. In addition, the rapid definition of the genetics of novel pathogens is a prerequisite for the development of novel therapies, to say nothing of vaccines.

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An area in which there have been tremendous advances is in patient imaging technologies. Improvements in ultrasonography have allowed rapid bedside evaluation of patient’s interior structures. Tissue tracking approaches are enhancing evaluations of cardiac muscle function, and likely will provide new insights into other organ pathologies. Increasing use of Doppler signals allows non-invasive evaluation of regional blood flows. In the future, it is likely that these tools will be combined with challenges to the system that tests the limit of responses and the reserves in the system in the same way that exercise testing is regularly used to evaluate cardiac limitation, and glucose is infused to test insulin responses. The future likely will provide more portable and less expensive devices that can be kept at the bedside of critically ill patients and provide dynamic information as the disease course evolves. An example is electrical impedance tomography. Techniques are beginning to evolve that will allow real-time assessment of ventilation-perfusion matching in the lung. This potentially will be used to adjust ventilator settings and would allow the clinician to better take into account heart-lung interactions, which is a major theme of this book. In the future, we should expect newer technologies that will expand on computed tomography and nuclear magnetic resonance by providing bedside approaches to patient investigations and more rapid processing. Resolution will also likely continue to improve as technology advances. Lastly, metabolic monitoring and profiling is another area ripe for development. It has long been said that “death begins in radiology” so the ability to obtain quality cross-­ sectional imaging without the need to transport critically ill patients will be an unalloyed good. Mitochondrial dysfunction is considered to be a major component of multi-organ failure. Currently, evaluation of mitochondrial function only can be done in vitro or in genetically modified animals, but perhaps newer imaging techniques will evolve that will allow assessment in vivo. We would then be able to evaluate how therapies affect mitochondrial function. It is unlikely that mitochondria could be a simple target of a single therapy because their dysfunction

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is most likely organ-specific, but this information would lead to a greater understanding of disease processes and the consequences of our overall treatments. Membrane dysfunction is likely also a major component of critical illness. It leads to vascular leak and the loss of blood volume, which is one of the greatest challenges in managing patients with distributive shock. It also leads to the failure of organs that require maintenance of a strong transmembrane potential such as the heart, smooth and skeletal muscles, the brain, and perhaps even mitochondria. Potential agents to improve this function currently are being studied, mainly at the cellular level, but hopefully in the future agents will be developed that will allow moderation of these processes in patients (Gavard 2014). With the increasing availability of extracorporeal membrane oxygenation and CO2 removal, we have the ability to maintain life and provide time for organ recovery. A future concept may be to use these devices to “rest” injured tissues, i.e., heart and lungs, while they recover. For example, such advanced support could be important for avoiding ventilator-induced lung injury or for decompressing a distended left heart, thereby protecting the lungs from high vascular pressures. However, caution will be required because these aggressive approaches may lead to tissue injuries themselves by removing normal functions. In addition, before broadly applying such intensive therapy, better predictive tools will be necessary to identify patients who may benefit so that life is prolonged instead of just prolonging dying. To date, this hypothesis of organ rest has not been supported by available data. As we get better at improving early survival, we will need better approaches to the long road to recovery (Herridge et al. 2011). It is evident that patients with post-intensive care syndrome (PICS) are left with multi-organ dysfunction and functional impairment. They most often have long-term disabilities in muscle, joints, and sensory functions. They frequently suffer from depression, post-traumatic stress disorder, and chronic pain syndromes (Herridge et  al. 2011). There also are major disruptions in their social

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structures, and in many health care systems, the financial toxicity of critical illness is increasingly recognized (Cameron et al. 2016). We will need to learn what we can do at early stages to prevent, or at least reduce, these complications. Early mobilization and nutritional support have improved functional outcomes to a degree, but a very large burden of chronic disease during ICU recovery remains. This issue is especially problematic in patients over 50 years of age (Herridge et al. 2003). It will be a major challenge to find therapeutic interventions that alleviate the burden in these individuals who make up the increasing proportion of critically ill patients. Ultimately, one of the best ways to treat the critically ill is the prevention of the factors that endanger patients. Thus, a better understanding of risk factors, and better preventive approaches in those at risk, should be a major priority in future management. Important measures should include improved hospital design with greater use of single rooms, improved surfaces for better cleaning and prevention of contamination, and advancements in hospital ventilations systems. As technologies advance, and our potential to sustain life improves, it will be important to evaluate how effectively new life-prolonging therapies provide a meaningful chance of recovery and an acceptable quality of life. Failure to consider this, the indiscriminant use of aggressive new technologies, and an increasing number of potential patient candidates, could overwhelm our health care systems without providing tangible benefits to patients. In summary, in some ways, the future is here. Outcomes of critically ill patients, and those undergoing complex surgical procedures, have improved dramatically. Further improvements will continue to require close and continuous attention to each patient’s clinical course, but more importantly, there needs to be an ongoing evolution of our understanding of the underlying physiological and pathophysiological processes. In addition, better bedside management will continue to require a skilled and well-trained workforce, which currently often is challenged by fragmented management and over-reliance on new technologies. The values obtained with new

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devices only can be as good as the questions asked of them by the treating team. When evaluating new technologies, it is worthwhile considering the words of the late Neil Postman, who was an expert in the social consequences of technologies (Postman 1992). What is the problem that this new technology solves? Whose problem is it? What new problems do we create by solving this problem? Which people and institutions will be most impacted by a technological solution? To end, we had some fun speculating on primary predictions and will finish with our list of favorites: 1. Technology: real time, non-invasive readout of intravascular volume and cardiac output. 2. Predictive analytics: use of big data to predict individual outcomes by using deep learning with iterative algorithms that improve with experience. 3. Smart pharmacology: Some drugs can help and some may hurt subgroups of patients; use of genomics and physiology may help us move past the one-size-fits-all approach by indicating who is most likely to respond. 4. Real-time lung imaging: This could help titrate PEEP and peak transpulmonary pressure in order to minimize atelectasis and over-distension. 5. Immunomodulation: better understanding of immune system dynamics may lead to more targeted approaches to immune modulation, for example, suppression of the response at some stage and stimulating the response at another time during the course of the illness. Humoral, T cell and neutrophil function are all different, some patients need more, some need less, and in some circumstances perhaps patients at risk should be vaccinated with endotoxin prior to sepsis. 6. Anti-infection: bedside, rapid genetic characterization of invading organisms will provide more rapid identification of the appropriate therapeutic agent. 7. Development of active antiviral agents that have comparable therapeutic effects as antibacterial and antifungal agents.

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8. Development of drugs that stabilize vascular permeability and prevent the marked increase in fluid loss in sepsis. 9. Prevention and recovery  – Better understanding of the mechanism of post-intensive care syndrome will lead to changes in care delivery and therapies to prevent the long-­ term sequelae of critical illness. 10. Understanding of the microbiome: a better understanding of the role of the microbiome will lead to both smarter use of antibiotics to avoid ablation of the host flora, but also will direct therapeutic manipulation of the microbiome itself. 11. Targeted therapies for common comorbidities; much intensive care unit mortality is related to underlying conditions but rapidly developing, targeted molecular therapies for common medical illness (heart failure, cancer) will lead to greater patient resilience in the face of critical illness. 12. Monitoring: as the use of single rooms with isolation capacity increase, alarms hopefully will finally be removed from around the patient’s head and moved to a control panel outside the patient’s room. Remote facilities will be established so that ventilators, pumps, and other controls can be managed remotely outside the room especially when patients are isolated for infection control. Maybe we will be right, maybe we will be wrong, or maybe the future will outshine the best of any predictions that we came up with and all our current thoughts will just seem naïve!

References Abraham E, Wunderink R, Silverman H, Perl TM, Nasraway S, Levy H, et  al. Efficacy and safety of monoclonal antibody to human tumor necrosis factor alpha in patients with sepsis syndrome. A randomized, controlled, double-blind, multicenter clinical trial. TNF-alpha MAb Sepsis study group. J Am Med Assoc. 1995;273(12):934–41. Abraham E, Glauser MP, Butler T, Garbino J, Gelmont D, Laterre PF, et  al. p55 tumor necrosis factor receptor fusion protein in the treatment of patients with severe sepsis and septic shock. A randomized controlled

958 multicenter trial. Ro 45-2081 study group. JAMA. 1997;277(19):1531–8. Boyd O, Grounds RM, Bennett ED. A randomized clinical trial of the effect of deliberate perioperative increase of oxygen delivery on mortality in high-risk surgical patients. J Am Med Assoc. 1993;270(22):2699–707. Browner WS, Newman TB.  Are all significant P values created equal? The analogy between diagnostic tests and clinical research. JAMA. 1987;257(18):2459–63. Burke E.  Edmun Burke quotes quote-­coyote.com. (Available from: https://www.quote-­coyote.com/ quotes/authors/b/edmund-­burke/). Cameron JI, Chu LM, Matte A, Tomlinson G, Chan L, Thomas C, et  al. One-year outcomes in caregivers of critically ill patients. N Engl J Med. 2016;374(19):1831–41. Cross AS.  Antiendotoxin antibodies: a dead end? Ann Intern Med. 1994;121(1):58–60. Danziger J. Ángel Armengol de la Hoz M, li W, Komorowski M, Deliberato RO, rush BNM, et  al. temporal trends in critical care outcomes in U.S. minority-­ serving hospitals. Am J Respir Crit Care Med. 2020;201(6):681–7. Gavard J. Endothelial permeability and VE-cadherin. Cell Adhes Migr. 2014;8(2):158–64. Goronzy JJ, Weyand CM. Understanding immunosenescence to improve responses to vaccines. Nat Immunol. 2013;14(5):428–36. Gustafsson UO, Scott MJ, Hubner M, Nygren J, Demartines N, Francis N, et al. Guidelines for perioperative Care in Elective Colorectal Surgery: enhanced recovery after surgery (ERAS(®)) society recommendations: 2018. World J Surg. 2019;43(3):659–95. Herridge MS, Cheung AM, Tansey CM, Matte-Martyn A, Diaz-Granados N, Al-Saidi F, et al. One-year outcomes in survivors of the acute respiratory distress syndrome. N Engl J Med. 2003;348(8):683–93. Herridge MS, Tansey CM, Matté A, Tomlinson G, Diaz-­ Granados N, Cooper A, et al. Functional disability 5 years after acute respiratory distress syndrome. N Engl J Med. 2011;364(14):1293–304.

S. Magder et al. Knaus WA, Marks RD.  New phenotypes for Sepsis: the promise and problem of applying machine learning and artificial intelligence in clinical research. JAMA. 2019;321(20):1981–2. Ljungqvist O, Scott M, Fearon KC.  Enhanced recovery after surgery: a review. JAMA Surg. 2017;152(3):292–8. Lopez A, Lorente JA, Steingrub J, Bakker J, McLuckie A, Willatts S, et al. Multiple-center, randomized, placebo-­ controlled, double-blind study of the nitric oxide synthase inhibitor 546C88: effect on survival in patients with septic shock. Crit Care Med. 2004;32(1):21–30. McCloskey RV, Straube RC, Sanders C, Smith SM, Smith CR. Treatment of septic shock with human monoclonal antibody HA-1A.  A randomized, double-blind, placebo-controlled trial. CHESS trial study group. Ann Intern Med. 1994;121(1):1–5. Pearse RM, Harrison DA, MacDonald N, Gillies MA, Blunt M, Ackland G, et al. Effect of a perioperative, cardiac output-guided hemodynamic therapy algorithm on outcomes following major gastrointestinal surgery: a randomized clinical trial and systematic review. JAMA. 2014;311(21):2181–90. Postman N. Technopoloy: the surrender of culture to technology. New York: Vintage books; 1992. Rautanen A, Mills TC, Gordon AC, Hutton P, Steffens M, Nuamah R, et  al. Genome-wide association study of survival from sepsis due to pneumonia: an observational cohort study. Lancet Respir Med. 2015;3(1):53–60. Seymour CW, Kennedy JN, Wang S, Chang C-CH, Elliott CF, Xu Z, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for Sepsis. JAMA. 2019a;321(20):2003–17. Seymour CW, Kennedy JN, Wang S, Chang CH, Elliott CF, Xu Z, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for Sepsis. JAMA. 2019b;321(20):2003–17. Weyand CM, Goronzy JJ. Aging of the immune system. mechanisms and therapeutic targets. Ann Am Thorac Soc. 2016;13(Suppl 5):S422–s8.

Index

A Abdominal pressure (Pab), 554 ABiomed AB5000, 807 Absolute esophageal pressure, 690, 691 Acid-base disorders, 654 base excess, 660, 661 chloride effect, 662 evaluation of, 661–662 protein effect, 663 water effect, 662 Action potential duration (APD) immediate change, 94, 95 steady-state, 95 transient change, 95 Acute cor pulmonale, 685 Acute Myocardial Infarction in Switzerland (AMIS), 761 Acute obstructive disease airflow limitation, 708 heart-lung interaction intra-pulmonary vessels, 710–711 pleural pressure, 709–710 mechanical ventilation, 707 normal respiratory mechanics, 707–708 spontaneously breathing patient, 711–712 therapeutic strategy, 712–713 ventilated patient, 712 Acute pulmonary embolism (PE) categorization of, 908 CTA, 909 incidence, 905 laboratory testing, 908 management, 909 airway management, 913, 914 anticoagulation and thrombolytics, 910–912 vasoactive medications, 912, 913 mortality, 905 pathophysiology, 905, 906 right ventricle, 906–908 TTE, 909, 910 vasoconstrictive mediators, 906 Wells criteria and revised Geneva score, 908 Acute respiratory distress syndrome (ARDS), 172, 200, 375, 486, 509, 590, 685, 699 biological modulation, 736

definition, 729 hyperinflammation, 731 pathophysiology of exudative phase, 729, 730 fibrotic phase, 730 proliferative phase, 730 VILI biotrauma and inflammation, 734, 735 etiology and pathophysiology, 732, 733 histologic alterations, 731 historical perspective, 732 inflammation and fibrosis, 735 mechanical and biological pathways, 735, 736 mechanotransduction, 733, 734 Aging, 951 Airway opening pressure (AOP), 689 Airway pressure (ΔPaw), 486 Airway pressures and electromyographic (EMG) signals, 545 Airway resistance, 250 Alternating electric current (AC), 585 Alveolar-arterial difference (AaDO2), 193, 194 Alveolar overdistension and collapse (ODCL), 591–592 Alveolar pressure, 254 Alveolar syndrome, 502–503 Ambient hypoxia, 644 Amplitude of oscillations, 276 Aortic stenosis, 628 Apnea, 214, 215 Arginine vasopressin, 752 Arterial blood pressure critical closing pressure, 117, 118 elastic energy, 108, 109 gravitational energy, 111, 112 impedance, 120 kinetic energy, 109–111 mammals, 112, 113 measurement, 120, 121 physical principles, 107, 108 pulse pressure, 119, 120 regional distribution of flow, 113–117 Arterial cannulation, monitoring of BP, 273 Arterial doppler waveform, 388, 389 Arterial wall properties, 124–126

© Springer Nature Switzerland AG 2021 S. Magder et al. (eds.), Cardiopulmonary Monitoring, https://doi.org/10.1007/978-3-030-73387-2

959

Index

960 Arterial wave reflections, 129–131 Asynchronies, mechanical ventilation, 715, 716 ATHOS-3 trial, 754 Auscultatory method, 275 B Bainbridge reflex, 99, 100 Barbiturates, 160 Barotrauma, 172 Base excess (BE), 660, 661 Bedside management, 956 Beginning cardiogenic shock, 763 Benzodiazepines, 159 Bernoulli effect, 475, 476 Big data approaches, 953 Bioimpedance, 585, 586 Bisphosphoglycerate, 639 Blood chemistry analysis, 664 24-hour blood pressure assessment, 276 Blood pressure measurement, 278, 279 innovative sensor technology developments, 278 intermittent non-invasive, 273, 274 invasive arterial, 273–275 nanocomposites, 278 non-invasive continuous arterial, 276–278 Body plethysmography, 477, 478 Bohr and Haldane effects, 638–639 Bohr’s and Enghoff’s formulas, 607 Bohr’s formula, 605 Brain monitoring, 337 Bronchial C-fibers, 213 C Cabamylation, 648 Calcium-clock, 90, 91 Campbell diagram, 549 Capnography, 613 Capsaicin, 229 Carbamylated hemoglobin (CarHb), 647, 648 Carbon dioxide (CO2), 601 Carbon monoxide (CO), 646 Cardiac output, 283 analytic problem, 292, 293 bathtub concept, 11, 12 capacitance, 9 closed circuit, 7 compliance, 10, 11 compliant region, 10–12 continuous thermodilution technique, 293 echocardiographic techniques, 299–301 errors, 286 esophageal doppler, 298, 299 fick method, 287 foreign gas method, 295, 296 Guyton’s analysis, 13, 14 Guyton’s, graphical approach, 14, 17 capacitance, 16, 17 cardiac function, 14–18

Starling’s cardiac function curve, 13 stressed volume, 16 vascular capacitance, 16 venous return curve, 14–18 x-intercept, 14 hydraulic and electrical models, 12, 13 indicator dilution methods, 288, 289 injectate, 290, 291 intrathoracic volume, 303, 304 Krogh’s two compartment model, 18, 19 LiDCO Plus®, 297 measurement method, 309–311 multicellular organisms, 7 normalization, 284 pathophyiologic factors, 292 physiologic factors, 291, 292 PiCCO system, 297 potential problems, 294 pressure-flow relationship, 10 pressure-volume relationship, 8–10 procedures, 313–318 pulse contour analysis, 296 rebreathing method, 294 reliability, 293 respiration, 287 single breath technique, 295 sources of variability, 290 specific quality criteria, 311, 313 stressed volume, 7 thermodilution technique, 289, 290 transoesophageal echocardiography, 302, 303 transthoracic echocardiography, 302 Vigileo™, 297 Cardiac-related signal (CRS), 589 Cardiogenic (hydrostatic) pulmonary edema, 508 Cardiogenic shock with acute coronary syndromes, 760–762 without acute coronary syndromes, 762–766 cardiac and return functions, 774 CathPCI registry, 759 clinical consequences, 775–778 congestive component, 766, 767 definition, 759, 760 depression of Emax, 774 diastolic filling pressure, 774 distributive component, 767 GUSTO-IIb, 766 Guyton’s plot, 774, 775 in-hospital mortality, 759 left anterior descending (LAD) occlusions, 766 LV passive filling curve, 774 mechanical support devices Centrimag®, 793 ECMO, 809, 811, 812 general principles, 796–803 IABP, 793–797 Impella®, 793, 803–806 left ventricular support, 794 para-corporal devices, 793 physiological approaches, 793, 809

Index surgically implantable devices, 807, 808 TandemHeart® p-VAD, 793, 808–810, 813 monitoring, 777, 778 morbidity and mortality, 766, 780 multi-organ dysfunction and injury, 766 Orbi score, 763 pharmacological approaches dobutamine, 783, 784 dopamine, 784 Epi versus NE, 782, 783 epinephrine (Epi), 781 milrinone, 784, 785 norepinephrine (NE), 781, 782 sodium bicarbonate (NaHCO3), 785 vasopressin, 785 physiology and pathophysiology, 768–777 progressive deterioration, 766 pulmonary artery catheter diagnostic value of, 779 management value of, 779, 780 right heart support, 815, 816 right ventricular failure, 812–815 SHOCK-IABP 2 trial, 759 treatment recommendations, 780 venous return, 774–776 ventilation, 785, 786 Cardiopulmonary diseases, 601 Cardiopulmonary interaction chest wall and lung mechanics, 628 diffuse lung disease, 624–625 gas exchange, 625–627 heart disease, 628 imaging evaluation, 619 pulmonary functional assessment, 620 pulmonary hypertension, 623, 624 pulmonary perfusion, 623 techniques for mapping DCE MRI, 620, 621 fluorine gas MRI, 623 hyperpolarized gases, 622–623 oxygen enhanced MRI, 622 proton imaging, 622 Cardiopulmonary monitoring, 235, 443, 457, 458 alveolar collapse, 239, 240 capillary refill, 450–452 crackles, 238 limitations, 457 optical methods, 452–454 perfusion indexes, 454–456 perfusion shock, 444 peripheral perfusion, 445 pressure volume behavior, 237 skin blood flow regulation, 443, 444 skin mottling, 449, 450 skin temperature gradients, 446, 449 subjective assessment, 445, 446 surface tension, 236 surfactant activity, 239 surfactant composition, 238

961 surfactant function, 240, 241 surfactant secretion, 239 CardShock, 760 Center of ventilation (CoV), 592 CentriMag®, 807 Cerebral hemodynamics carbon dioxide and hyperventilation, 156 cerebral blood flow and circulation, 153, 154 cerebral metabolic rate of oxygen, 155 cerebral oxygenation and CO2 reactivity, 155, 156 CPP, 154, 155 Monro-Kelli hypothesis, 153 neuromuscular junction blockers, 160 osmotherapy hypertonic saline, 158 mannitol, 157, 158 pressure autoregulation, 154 sedation barbiturates, 160 benzodiazepines, 159 dexmedetomidine, 160 ischemia, 158 opioids, 159, 160 propofol, 159 Cerebral microdialysis (CMD), 349, 350 Cerebral perfusion pressure (CPP), 154 Cerebral venous occlusion, 917 Cerebrovascular monitoring clinical evaluation, 917 deoxyglucose PET studies, 920 electronic microscopic analysis of tissue, 920 Glasgow Outcome Scale score, 919 ICP elevation, 919 intracranial pressure monitoring, 917 jugular venous oxygen measurements, 918 monitoring and management, 918 oxygen-derived free radicals, 919 PaCO2 steady state, 918 phase 2 trial, 920 positron emission tomographic (PET) studies, 919 pressure autoregulation, 919 spreading depression, 920 surface and cortical electroencephalographic monitoring, 920 therapeutic modalities, 918 tissue brain oxygen monitors, 920 vasoconstrictive effect, 919 Characteristic impedance, 127 Charge-coupled device (CCD), 437 Chemoreceptors monitor, 208 Chest radiography (CXR), 507 Chloride, 662, 673 Chronic hypercapnia, 216, 217 Chronic obstructive pulmonary disease (COPD), 627 Classic cardiogenic shock, 763 Classical Starling principle, 73, 74 Clearsight system, 277 ClearSight system, 277, 278 CNAP system, 277 CO2 kinetics, 604

962 Colloids, 678–679 Colorectal surgery, 952 Compound muscle action potential (CMAP), 558, 559 Computed tomography angiography (CTA), 909 Connective tissue disease-associated ILDs, 510 Control of breathing arterial PO2, PCO2, and pH arterial chemoreceptors, 210, 211 central chemoreceptors, 209, 210 chemoreflex, mechanoreflex, and negative feedback, 207, 208 clinical implications apnea, 214, 215 chronic hypercapnia, 216, 217 dyspnea, 215, 216 patient/ventilator dyssynchrony, 215 CNS-processed signal targeting effector respiratory muscles, 208, 209 definition, 205 limitations, 206 lungs and airways autonomic nervous system, 213, 214 bronchial C-fibers, 213 juxtacapillary receptors, 213 lungs and lower airways, 212 nose and upper airways, 211, 212 pulmonary stretch receptors, 212 respiratory rhythm generation, 206 Cranio-caudal movement, 571 Curtain sign, 498, 508 Cystic fibrosis (CF), 627 D Delirium bedside detection, 923 definition and epidemiology, 924 Diagnostic and Statistical Manual of Mental disorders (DSM-5) definition, 923 identification, 925, 926 incidence, 923 nonpharmacological interventions, 927, 928 PADIS guidelines and ABCDEF bundle, 927 pathophysiology, 924, 925 pharmacological interventions, 929–931 analgesia and sedation management, 930, 931 drugs, 929 risk factors, 923, 926, 927 Deteriorating/doom, 763 Dexmedetomidine, 160 Dialysis disequilibrium syndrome, 143 Diaphragm echodensity, 528 electrical resistance of, 274 excursion, 524, 529 speckle tracking, 528 thickening fraction, 525 thickness and tidal thickening fraction, 528 ultrasonography, 566–571 ultrasound

Index clinical and research applications, 529–530 diaphragm excursion, 527 diaphragm strain, 527 echodensity, 528 functional anatomy of, 521–522 influence of effort and lung volume, 526 mechanics of, 522–523 motion, 524 reference range for, 525 relation to inspiratory pressure, 527 relation to inspiratory volume, 525–527 shortening, 525–527 speckle tracking, 528 structure and function, 523–527 thickness and tidal thickening fraction, 523, 528 Diaphragmatic motion, 524 Diastolic compliance, right heart function, 24, 25, 28, 39, 40 Diffuse lung disease, 624–625 Dilutional effect, 658 Dipalmitoylphosphatidylcholine(DPPC), 236 Dopamine, 925 Doppler measurements, 363 Doppler ultrasound clinical application, 391–394, 396–398 principles, 386, 388–391 Duty cycle, 220 Dynamic contrast enhanced (DCE) MRI, 620, 621 Dyspnea, 215, 216 E Echocardiography cardiogenic shock, 367, 368 classical hemodynamical parameters, 363, 365 clinical conditions, 365, 366 education, 370 pitfalls and limitations, 369, 370 transthoracic technique, 371 TTE imaging, 359–362 Effective arterial elastance (Ea), 32 Effective lung volume (ELV), 611 Efficacy and Safety of Recombinant Human Activated Protein C for Severe Sepsis (PROWESS), 823 Elastance, 769 Elastic energy, 108, 109 Elastic recoil, 8, 12 Electrical impedance, 585 Electrical impedance tomography (EIT), 587, 691, 692, 955 bioimpedance, 585, 586 clinical application, 593–594 measurements and image reconstruction, 586–589 perfusion monitoring, 594–596 Sheffield backprojection, 588 spatial distribution of ventilation, 590–592 temporal distribution of ventilation, 592–593 ventilation monitoring, 590–594 voltage measurements, 587 waveform analysis, 589

Index Electromyograms (EMG), 543 Embedding oscillometric techniques, 274 Encyclopedic approaches, 953 Endocardial endothelial cells (EEC), 23 Endothelial glycocalyx, 72 Endotracheal suctioning, 594 Endotracheal tubes, 594 End-systolic elastance relationship (ESPVR), 772 End-systolic left ventricular pressure-volume (Es-Lv), 26 End-systolic pressure-volume line (Es-Rv), 26 End-systolic pressure volume relationship (ESPVR), 51–53 Epipharynx, 211 Epithelial-endothelial fluid reabsorption intestinal mucosa, 79 in kidney, 78, 79 lymph nodes, 79 Equal pressure point, 475 Esophageal balloon, 485 Esophageal Doppler, 298 Esophageal pressure (ΔPes), 486, 490, 539 auto-triggering, 490 clinical applications of, 486 delayed cycling, 490 diaphragmatic muscle contractions, 489 double triggering, 490 estimate of pleural pressure, 485 ineffective efforts, 489 occlusion test, 485 patient’s effort, 488–489 patient-ventilator interaction, 489–490 positioning of, 485 premature cycling, 490 supine position, 486 transpulmonary pressure, 486–488 upright and prone positions, 486 Esophageal pressure monitoring, 488, 489 European Life Support Organization (ELSO) registry, 763 Expiratory reflex, 212 Extra corporal membrane oxygenation (ECMO), 952 Extracellular fluid, 586 F Fick principle, 285 sources of error, 286 Figge equation, 660 Finger cuff technology, see Volume clamp method Fluid filled system, 273 Fluid filtration blood loss and saline infusion, 80, 81 endothelial glycocalyx components, 82 optical methods, 82, 83 whole body glycocalyx volume, 83 epithelial-endothelial fluid reabsorption intestinal mucosa, 79 in kidney, 78, 79 lymph nodes, 79

963 fluid exchange, 81 interstitial pressure, 80 lung fluid balance, 81 microvascular pressures, 79–81 microvessel pressure and colloid osmotic pressure, 76, 78 plasma proteins, 81 plasma volume, 71 revised Starling principle, 72 classical Starling principle, 73, 74 endothelial glycocalyx, 72 filtration rate, 74–77 fluid exchange, 75 sub-glycocalyx space, 72, 73 Fluid physiology body water and electrolytes, 139, 140 compartments, 139 extracellular fluid dynamics, 144, 145 hyper-oncotic solutions, 148, 149 hypertonic sodium chloride solutions, 147, 148 iso-oncotic colloids, 148 movement and distribution, 145, 146 normal saline, 146, 147 osmoles albumin molecules, 143 analbuminemia, 144 definition, 140 diffusive permeability and refection coefficient, 142, 143 Gibbs-Donnan relationship, 143 glucose, 142 membrane, 141 osmotic pressure, 140, 141, 143 plasma volume, 144 positive elements, 141, 142 water movement, 141 principles, 137 pure water and dextrose, 146 role of water, 137, 138 sodium bicarbonate solution, 149 volume and generation of blood flow, 138, 139 Fluid-responsiveness, 366, 369 fluid challenge, 406, 407 heart-lung interactions, 414 passive leg raising test, 411–413 pulse pressure variation, 407 respiratory occlusion tests, 413, 414 static indices, 405 stroke volume variation, 408, 410 vena cava, 410, 411 Fluorine gas MRI, 623 Focal disorders, 917 Focal lung diseases, 510–512 Force balance, 171 Forced oscillation technique, 480 Force-velocity relation, 522 Fourier transform analysis, 128 Frank-Starling curve, 613 Frequency-domain analysis, 561–562

Index

964 Fuid responsiveness, 414 Functional EIT (fEIT) images, 589 Functional residual capacity (FRC), 474, 534 G Gambelgrams, 657 Gamblegrams, 656 Gas exchange, 625–627 Gastric pressure (Pga), 539 Gastric-to-esophageal pressure, 555–556 Gatorade®, 777 Genetic and molecular techniques, 955 Gibbs-Donnan effect, 146 Global cerebral ischemia, 917 Global inhomogeneity index (GI index), 592 Global ventilation, 590 Glucose, 673 Glycocalyx-cleft model, 77 Glycocalyx-junction model, 77 Gravitational energy, 111, 112 Guillain-Barre’ syndrome, 534 Guyton’s analysis, 13, 14, 100 H Heart disease, 628 Heart lung interactions, 243, 244, 267 active expiration, 264 clinical implications, 244 Guyton’s graphical, 246–248 HJR, 264 inverse pulsus paradoxus, 263 lung inflation, 255 mueller maneuver, 265, 266 Ppl, 249, 251–254 pulsus paradoxus, 260, 262 respiratory variations, 256–260 transpulmonary pressure, 255–257 valsalva, 267 Heart-lung mechanical coupling, 709 Heart rate action potential, 87 Bainbridge reflex, 99, 100 beta-blockers and ejection fraction, 98, 99 diastolic limitation, 101 during exercise, 96–98 Guyton analysis, 100 intrinsic heart rate, 95, 96 periodicity, 87 rhythmicity APD, 94, 95 SAN (see Sinoatrial node (SAN)) stroke return, 87 supply-demand of, 101, 102 tachycardia and hypovolemia, 99 time constants and volume constraints, 87–89 Hemodynamic measurements, 319 CVP, 329–332 frequency response, 328, 329 measuring pressures, 321–324

Ppao, 333, 334 pulmonary artery pressure, 334, 335 transmural pressure, 324–327 understanding pressure measurements, 320, 321 volume vs. pressure measurements, 319, 320 Hemoglobin-mediated interactions, 641 Hemoglobin respiratory function adaptation of erythrocytes, 641–643 adaptation to life, 643 anaerobic origin of hemoproteins, 636 clinical alterations of, 645 clinical physiology and pathophysiology acylation, 647 ambient hypoxia, 644 carboxyhemoglobinemia, 646–647 CarHb, 647, 648 deamination, 647 hemoglobinopathy, 644–646 human fetal hemoglobin, 646 increased metabolic demand, 643 methemoglobinemia, 647 post-translational modification, 647–648 unstable hemoglobins, 646 coupling of O2 and CO2 exchange, 639 erythrocytes, 638–641 oxygen carrier, 636–638 Henderson-Hasselbalch equation, 654 Hepatojugular reflux, 264 Heat moisture exchanger (HME), 603 Hooke’s assessment, 9 Hospital design, 956 Hydrogen (H+) ion acidemia or alkalemia, 653 albumin, 659–660 CO2, 658, 659 empiric equations, 654 Henderson-Hasselbalch equation, 654 serum electrolytes, 653 strong ions, 655–658 water, 654, 655 Hydrostatic pressure, 140, 143, 144 Hydrostatic zero reference point, 275 Hypercapnia, 196 Hyperinflation, 695 Hyperpolarisation-activated cyclic nucleotide-gated (HCN), 89 Hypertonic saline, 158 Hyperventilation, 156 Hypovolemia, 99 Hypoxic pulmonary vasoconstriction (HPV), 196 I IABP Shock II registry, 763 IABP Shock II risk score, 764 Idiopathic pulmonary fibrosis, 510 Impedance ratio, 590 Impella®, 777, 793 Indicator-based signal (IBS), 589 Inferior vena cava (IVC), 392 Innovative non-invasive technologies, 278

Index Input impedance, 127–129 Inspiratory and expiratory intercostal muscle, 208 Intensive care unit (ICU), 2 Intensive therapy, 956 Intermittent non-invasive arterial blood pressure measurement, 275, 276 Interstitial lung diseases (ILDs), 510 Interstitial pressure, 80 Interstitial space, 138, 139, 142–144 Interstitial syndrome, 499–503 Intestinal mucosa, 79 Intra-aortic balloon pump (IABP), 793–797 Intracardiac nervous system (ICNS), 92 Intracellular spaces, 142, 143, 148 Intracranial monitoring, 337 cerebral oxygenation, 347, 348 cerebral perfusion pressure, 345, 346 clinical application, 342–345 electrophysiology, 351 history, 339 indications, 345 modes of measurement, 341 pressure, 338 techniques, 342 waveform analysis, 339–341 Intramuscular electrodes, 560 Intra-thoracic bioimpedance, 585 Intratidal gas distribution (ITV), 593 Intravenous fluids assessment of hydration, 671 blood flow, 670–671 colloids, 678–679 crystalloid solutions, 676–678 fluid infusion, 669 intravenous solutions, 674 maintenance and daily needs, 672 chloride, 673 glucose, 673 potassium, 673 sodium, 672, 673 water, 672 managing hydration status, 679–680 Na+ balance and vascular volume, 671 for resuscitation, 669, 674–676 types of, 676 Intrinsic positive end-expiratory pressure (PEEPi), 550 Invasive arterial blood pressure measurement (arterial catheter), 273–275 Ischemia, 155 Iso-oncotic colloids, 148 J Jugular venous bulb oximetry (JVBO), 348 Juxtacapillary receptors, 213 K Kigali modification, 509 Kinetic energy, 109–111 Krogh’s two compartment model, 18, 19 Kussmaul’s sign, 265

965 L Labor issues, 952 Larynx, 212 Laser Doppler flowmetry (LDF), 436 Laser Doppler perfusion imaging, 436 Left heart function cardiac muscle characteristics, 49, 50 contractility, 55 ESPVR, 51–53 maximum elastance (Emax), 53, 54 myocardial oxygen consumption, 57 pressure-volume relationships, 51, 52, 57, 58 tension-length relationships, 50, 51 time-varying elastance, 53, 54 ventricular energetics, 55, 56 Lempel-Ziv index, 572 Length-tension relation, 522 Life expectancy, 952 Life-sustaining therapies, 951 Lorazepam, 159 Lower inflection point, 686, 687 Low-frequency probes (5-1 MHz), 502 Lung barotrauma and volutrauma, 172 collapse and overdistension, 593 edema, 184 inflation, 68, 69, 167, 169, 170 injury, 227 mechanical ventilation, 167 prestress and shear modulus, 170 stress and strain non-uniform inflation, 172 pressure and volume, 168, 169 transmission, 170, 171 viscoelasticity, respiratory rate and mechanical power, 172–174 Lung elastance (EL), 479 Lung resistance (RL), 479 Lung ultrasound (LUS) assessment alveolar syndrome, 502–503 anterior and lateral chest, 499 ARDS, 509, 510 cardiogenic (hydrostatic) pulmonary edema, 508 clinical application of, 513 diagnosis and monitoring, 503, 512–515 of alveolar syndrome, 502 of interstitial syndrome, 501 of pneumothorax, 506 focal lung diseases, 510–512 ILDs, 510 interstitial syndrome, 499–502 lung ultrasound scanning protocols, 514 normal lung ultrasound, 498–500 physics of, 493–495 pleural effusion, 507–508 pneumothorax, 503–507 training, 515 ultrasound systems, 495 LUS-Berlin criteria, 509 Lymph nodes, 79

Index

966 M Magnetic stimulation, 540–541 Main-stream capnographs, 602 Mannitol, 157, 158 Manufacturer and User Facility Device Experience (MAUDE) database, 816 Maximal diaphragm thickening fraction, 528 Maximal expiratory airway pressure (PEmax), 535 Maximal inspiratory pressure (PImax), 535 Maximal transdiaphragmatic pressure (Pdimax), 533 Maximum elastance (Emax), 51, 53, 54 Mean airways-alveolar interface, 605 Mean circulatory filling pressure (MCFP), 12 Mean systemic filling pressure (MSFP), 12, 25, 670 Measurement precision, 310, 311 Measurement trueness, 309 Measurement uncertainty, 311 Mechanoreceptors monitor, 208 Melatonin, 925 Membrane-clock, 89, 90 Membrane dysfunction, 956 Mesenchymal stromal cells (MSCs), 736 Methemoglobinemia, 647 Microcirculation, 429 capillaroscopy, 432 function, 438, 439 handh-held vital microscopes, 432, 433, 435, 436 laser-based techniques, 436 LDPI, 436, 437 LSCI, 437, 438 monitoring techniques, 430, 431 tissue oxygenation, 430 Microvascular pressure, 79, 80 Midazolam, 159 Mild hypoxemia, 198 Mitochondria, 156 Mitochondrial dysfunction, 955 Mitral annular plane systolic excursion (MAPSE), 369 Mobile biomonitoring, 278 Mobile health monitoring, 278 Mueller-expulsive maneuver, 265, 266, 538–539 Multifocal ischemia, 917 Multiple inert gas elimination technique (MIGET), 194, 195 Myocardial oxygen consumption (MVO2), 56, 57, 101 Myogenic peptides, 92, 93 Myosin, 49 N Nanocomposites, 278 Near-infrared spectroscopy (NIRS), 439, 453 Nephrotic syndrome, 145 Neural breathing modulates, 558 Neurally adjusted ventilator assist (NAVA), 565 Neurocritical care, 337, 339, 348 Neuromuscular coupling, 564 Neuromuscular junction blockers, 160 Neuroventilatory coupling, 564, 565 Newton’s laws of motion, 476

Newton-Raphson algorithm, 589 Non-invasive continuous arterial blood pressure measurement, 276–278 Nuclear factor-κB (NFκB), 954 O Obesity and abdomen, 937 additional considerations, 942 airway management, 939, 940 baseline physiology changes, 935–937 control of breathing/OSA/OHS, 937, 938 and outcomes, 942 physiologic impact and clinical implications, 935 prevalence, 935 risk factor, 939 ventilator management, 940, 941 Observatoire Regional Breton sur l’infarctus registry, 763 Occlusion test, 485 Ohm’s law, 61 Oncotic pressure, 143 “Open lung” strategy, 689 Opioids, 159, 160 Oscillometry, 275, 482 Osmoles albumin molecules, 143 analbuminemia, 144 definition, 140 diffusive permeability and refection coefficient, 142, 143 Gibbs-Donnan relationship, 143 glucose, 142 membrane, 141 osmotic pressure, 140, 141, 143 plasma volume, 144 positive elements, 141, 142 water movement, 141 Osmotherapy hypertonic saline, 158 mannitol, 157, 158 Oxygen delivery critically ill, 467 DO2, 465–467 equation, 461, 462 fluid administration, 462 mechanical ventilation, 463 optimizing arterial oxygen content, 463, 464 tissues, 464 vasoactive medications, 463 Oxygen electrodes, 438 Oxygen enhanced MRI, 622 Oxygen extraction fraction (OEF), 155 P Packed red blood cells, 463, 466 Palpatory method, 275 PaO2/FIO2 ratio (“P/F ratio”), 195 Partial pressure of carbon dioxide (PaCO2), 702

Index Passive leg raising test, 366, 369, 411 Patient self-inflicted lung injury (P-SILI), 488 Patient’s work of breathing (WOB), 488 PCO2 gap, 420–422 clinical practice, 422, 423 errors, 425, 426 oxygen-derived variables, 424, 425 SvO2, 425 Pendelluft, 594 Peripheral perfusion indexes, 447–448 Personalized medicine, 954 Pharynx, 211 Phospholipid, 235, 236 PiCCO system, 297 PICO® device, 778 Plethysmography, 482 Pleural effusion, 507–508 Pleural line abnormalities, 510 Pleural pressure (Ppl), 247, 485 Pneumonia, 510, 511 Pneumothorax detection, 594 Point-of-care ultrasound (POCUS), 299, 386 Poiseuille’s law, 10, 61, 108 Portal doppler waveform, 390, 391 Positive end-expiratory pressure (PEEP), 173, 463, 464, 466, 486, 549, 552, 553, 700 airway closure, 688–690 airway P-V curve, 689 alveolar recruitment, 685 auto-PEEP, 185–187 baby lung, 685 “braking” effect, 184 cardiopulmonary effects, 184, 185 clinical applications, 185 definition, 177 dynamic hyperinflation, 185–187 EIT, 691, 692 esophageal pressure manometry absolute esophageal pressure, 690, 691 chest wall to respiratory system, 691 estimating transpulmonary pressure, 690 EXPRESS study, 688 hemodynamic monitoring, 185 left ventricular afterload, 182 lung edema, 184 lung recruitability CT scan images, 692–694 global integrative clinical approach, 694–695 lung volume and simplified method, 694 pressure-volume curves, 694 rationale, 692 mean airway pressure and hemodynamics, 180 myocardial contractility and compliance, 181, 183, 184 PEEP-FiO2 table, 687, 688 recruitable lung regions, 685 regional heterogeneity, 179, 180 respiratory system compliance, 686–687 right ventricular afterload, 182, 183 spontaneous vs. passive inflation, 184, 185

967 transmural pressure, 177–179, 182 venous return and Starling curves, 180, 181 ventilation/perfusion abnormalities, 200 Post-intensive care syndrome (PICS), 956 Potassium, 673 Pressure–time index (PTI), 220 Pressure-time product (PTP), 551 Pressure volume, 236 Pressure-volume-area (PVA), 56 Primary lung cancer, 512 Primary percutaneous coronary intervention (PPCI), 761 Principal component analysis (PCA), 595 Prone position (PP), 197, 200 ARDS, 699, 700 distribution of alveolar size, 701 hemodynamics, 700 lung, 700–702 maneuver, 702–704 monitoring, 704 Swimmer’s position, 703 Prone position maneuver, 702–704 Propofol, 159 Protein effect, 663 Protocol based care for early sepsis (ProCESS), 822 Proton imaging, 622 Pulmonary artery occlusion pressure (Ppao), 325, 335 Pulmonary artery pressure (Ppa), 64, 65, 365, 370 Pulmonary capillary wedge pressure (PCWP), 67 Pulmonary contusion, 511 Pulmonary embolism, 511, 624 Pulmonary functional imaging, 619 Pulmonary hypertension (PH), 623, 624 airway function, 876 apical views, 891 biomarkers, 894–896 cardiac magnetic resonance imaging, 893, 894 cardiopulmonary hemodynamics definition, 873 hemodynamic characterization, 873 normal pulmonary circulation, 872–873 pre-capillary PH, 873 pulmonary vascular resistance, 873 cardiopulmonary monitoring adverse effects, 879 HRQoL, 880–881 morbidity and mortality, 878 risk assessment and stratification, 879 symptomatic/functional classification, 879, 880 clinical practice, 889 clinical presentation and severity, 871 comprehensive assessment, 872 definition, 871 diagnosis, 886–887 diagnostic classification, 872 echocardiography, 889–893 ERS/ESC guidelines, 898 exercise capacity, 882 6-minute walk test (6MWT), 882–883 cardiopulmonary exercise test (CPET), 883–887

968 Pulmonary hypertension (PH) (cont.) natriuretic peptides (NPs), 896, 897 normal RV function, 877 PA elastance, 875 parasternal views, 890 PA stiffness, 875 physical examination, 881–882 practical vs. complete assessment, 897 preliminary c-index analysis, 898 pulmonary arterial vasoconstrictor reactivity, 875 pulmonary artery elastance, 874 pulmonary artery impedance, 875 pulmonary compliance/volumes, 876 pulmonary diffusing capacity (DLco), 876 pulmonary gas exchange, 876 pulmonary hemodynamics, 875, 889 pulmonary vascular compliance, 874, 875 pulmonary vascular physiology, 875, 876 PVR calculation, 874 PVR-compliance relationship, 875 right heart catheterization (RHC), 887, 888 risk assessment, 872 RV failure, 877, 878 RV-PA coupling Versus decoupling, 877, 878 severity/prognosis assessment, 887 subcostal view, 891, 892 ventricular interdependence, 878 Pulmonary perfusion, 609, 623, 624 Pulmonary sarcoidosis, 510 Pulmonary stability, 171 Pulmonary stretch receptors, 212 Pulmonary vascular resistance (PVR) compliance, 65–67 definition, 61, 62 interpretation, 67, 68 lung inflation, 68, 69 starling resistors, 63–65 vascular distensibility, 62, 63 Pulmonary wedge pressure (PWP), 67 Pulsatile haemodynamics, 124–126 and central aortic pressure, 132–134 and ventricular arterial coupling, 134, 135 Pulsatility index of arterial pressure (PIPressure), 388 Pulse pressure respiratory variation (PPV), 379, 381 Pulsed wave Doppler (PWD), 376 Pulsus paradoxus, 712 Q Quantitative pulmonary perfusion, 623 R Reconstruction algorithm for EIT (GREIT), 589 Regional pressure–volume (P/V) curves, 591 Regional ventilation delay (RVD), 592–593 Regional ventilation delay index (RVDI), 592–593 Relative workload, 97 Remifentanil, 160 Resistance and reactance, 480 Resistive loading, 221 Respiratory muscle function monitoring

Index abdominal-pleural pressure ratio, 557 airway and expiratory pressures, 536 clinical assessment, 533, 534 electromyography clinical applications of, 565–566 esophageal electrodes, 559, 560 frequency-domain analysis, 561–562 intramuscular electrodes, 560 left panel, 557 neuromuscular coupling, 564 neuroventilatory coupling, 564, 565 right lower panel, 558 right upper panel, 558 time-domain analysis, 562–564 esophageal and gastric pressure tracings gastric-to-esophageal pressure, 555–556 gastric-to-transdiaphragmatic pressure, 556–557 pleural pressure-abdominal pressure diagram, 553–555 imaging chest x-ray and fluoroscopy, 566 computed tomography, 566 diaphragm ultrasonography, 566–571 magnetic resonance imaging, 566 muscle fiber vibration assessment surface mechanomyography, 572–573 surface phonomyography, 572 output (see Respiratory muscle pressure output) pulmonary function testing, 534 transdiaphragmatic twitch pressure, 544 Respiratory muscle pressure output effort pressure-time product, 550–551 tension-time index, 551, 552 work of breathing, 548–550 strength airway pressures, 535–537 airway twitch pressure, 544 cough Pga, 540 electrical and magnetic stimulation, 540–544 evoked maneuvers, 547–548 magnetic stimulation, 540–541 Mueller-expulsive maneuver, 538–539 phrenic nerve stimulation, 540–547 pressure relaxation rate, 539 sniff Pdi values, 539 transdiaphragmatic pressure, 537–539 twitch interpolation technique, 545–547 voluntary maneuvers, 540 Respiratory muscles anatomy, 219, 220 blood flow animal models, 228 cardiogenic shock, 226–228 diaphragm, 221, 222 E coli, 226 fatigue, 223, 224 mechanical ventilation, 224–226 O2 consumption, 222, 223 PCO2, 224, 225 principles, 221–223 pulmonary edema, 227, 228

Index respiratory rate, 222 sepsis, 226, 227 septic shock, 226 efferent fibres, 228–230 energetics and mechanics, 220, 221 Respiratory quotient, 420, 424, 425 Respiratory system elastance (Ers), 479 Respiratory system resistance (Rrs), 479 Respiratory volumes, flows and pressures body plethysmography, 477, 478 clinical applications, 482 input-output relationships, 474 kinked/blocked airway, 474 onset of pulmonary edema, 474 oscillometry and impedance, 480–482 resistance and elastance, 478–480 spirometry Bernoulli effect, 475, 476 equal pressure point, 475 expiratory flow limitation, 474 FEV1 and FVC, 476 forced expiration, 475 intra-airway pressure, 475 low value of FEV1, 477 Poiseuille flow, 475 transmit transpulmonary pressure, 474 wave speed, 476 sudden bronchospasm, 474 x-ray computed tomography, 474 Responsive management, 954 Responsive therapy, 2 Revised Starling principle, 72 vs. classical Starling principle, 73, 74 endothelial glycocalyx, 72 filtration rate, 74–77 fluid exchange, 75 sub-glycocalyx space, 72, 73 Richmond agitation-sedation scale (RASS), 926 Right heart function acute processes, 21 aerobic function, 40, 41 origins of, 21, 22 right ventricular and left ventricular afterload, 37 clinical and physiological significance, 37 compartment model, 35 consistent response, 40 coronary blood flow, 41–43 diastolic pressure, 36–38, 43 diastolic-volume interaction, 35 dysfunction, 34, 35 EEC, 23 electrophysiological differences, 23 embryological development, 22 failure, 34, 35 limitation, 34 Mueller maneuver, 36 pharmacological differences, 23, 24 pressure load vs. volume load, 33, 34 pressure-volume loops, 28–33 principles, 24–28 properties, 23

969 pulmonary arterial compliance, 32, 33 pulmonary hypertension, 40 P-V plots, 37, 38 septal shift, 36 shape and load differences, 24–26 systolic pressure, 39 tetralogy of Fallot, 38, 39 volume effects, 35, 36 S Sagawa’s concept, 769, 770 SAVE score, 763, 765 Sepsis, 226, 227, 952, 954 animal data, 827–830 antibiotics, 821 capillary permeability, 853 cardiovascular responses to major drugs, 854 clinical manifestations, 834 clinical presentation, 822–823 coronary flow and myocardial ischemia, 832, 833 definition, 822 dobutamine, 861, 862 epidemiology, 823 epinephrine (Epi), 860, 861 examples, 821 experimental studies, 849 heart-lung component, 863–866 human data, 823–826 intracellular calcium regulation, 839, 840 intracellular signaling pathways, 833 intravascular pressures, 853 management, 821 mechanical requirements, 829–832 milrinone, 862 mitochondria, 838 mortality, 821 myocardial depression, 852 nitric oxide and superoxide, 836–838 norepinephrine, 854–859 O2 delivery management, 851 pathogen-associated molecular patterns (PAMPS), 833 permeable barriers, 840–842 phenylephrine, 859, 860 potassium ATP channel, 835, 836 quick SOFA score (eSOFA), 822 role of volume, 851–853, 855, 856 SOFA score, 822 standard initial management, 851 survival, 821 symptoms and signs, 821 systemic inflammatory response (SIRS), 821 therapeutic options, 842 transcription factor-3 (ATF3), 833 treatments, 849–851 tumour necrosis factor-α (TNFα), 833 vascular collapse and multi-organ failure, 842 vascular dysfunction, 834, 835 vascular failure, 834 vascular leak, 852 vasopressin, 860 volume infusions, 852

Index

970 Shear-wave elastography, 570 Side-stream capnographs, 601 Single-compartment model, 478, 480, 481 Single-photon emission computed tomography (SPECT), 596 Sinoatrial node (SAN) calcium-clock, 90, 91 definition, 88 extrinsic control, 89 central nervous system, 90, 91 circulating factors, 91, 92 function, 89 intrinsic control, 89 ICNS, 92 myogenic peptides, 92, 93 tissue stretch, 93 membrane-clock, 89, 90 Skeletal muscle structure, 558 Sodium, 672, 673 Spine sign, 508 Starling resistors, 63–65 Starling’s cardiac function curve, 13, 14 Step-wise acid-base analysis, 661 Sternomastoid activity, 534 Stressed volume, 9 Strong ion difference (SID), 654 Strong ion gap (SIG), 660 Strong ions, 654 Subtracting fEIT images, 590 Surface mechanomyography, 572–573 Surface phonomyography, 572 Swallowing reflex, 211 Swimmer’s position, 703 Systemic vascular resistance (SVR), 118 Systolic overshoot, 275 T Tachycardia, 99 TandemHeart® p-VAD, 808–810, 813 Tension-time index, 551–553 Tension time index of the diaphragm (TTdi), 220 Terlipressin, 752 Thoracic EIT, 585 Thoracocardiography, 304 Tidal impedance variation (TIV), 590 Time and volume-based capnography, 602, 603 Time-based and volume based capnography, 603 Time-domain analysis, 562–564 Time-varying elastance, 10, 27 Tissue hypoxia, 419, 422, 424 Tissue tracking approaches, 955 Tissue stretch, 93 Total lung capacity (TLC), 476, 534 Transdiaphragmatic twitch pressure (Pditw), 538, 539, 541, 542, 546, 569, 572 Transesophageal echocardiography (TEE), 359, 375–377 clinical applications, 379–383 monitoring cardiac flows, 377, 378 2-D TEE, 378, 379

Transmural pressure, 325 Transpulmonary pressure, 235, 237, 253, 254, 479 Transthoracic echocardiography (TTE), 377, 909, 910 Transthoracic ultrasound, 523 Tricuspid annular plane systolic excursion (TAPSE), 362 Troponin/tropomyosin, 49 U Ultrasonography, 955 Ultrasound systems, 495 Unstable hemoglobins, 646 Unstressed volume, 9 V Valsalva, 266, 267 Van’t Hoff equation, 140 Vascular compliance, 65–67 Vascular distensibility, 62, 63 Vascular impedance arterial resistance, 127 arterial wave reflections, 129–131 changes of waveforms, 131, 132 characteristic impedance, 127 frequency domain assessments, 127 history, 126 implications, 126 input impedance, 127–129 time domain assessments, 127 Vascular smooth muscle, 670 Vascular unloading technology, see Volume clamp method Vasoactive intestinal polypeptide (VIP), 92 Vasopressin vs. Norepinephrine Infusion in Patients with Septic Shock (VASST), 823 Vasopressors adrenergic vasopressor agents, 752 adverse effects, 751 angiotensin II, 753 autoregulation threshold, 751 on left ventricular function, 754, 755 non-adrenergic vasopressor agents, 752 outcomes, 753, 754 in right heart failure (RHF), 755, 756 vasopressin derivatives, 752 Venous Doppler waveform, 390 Venous return (VR), 709 Veno-venous extracorporeal membrane oxygenation (VV-ECMO) advantage, 741 assisted mechanical ventilation, 747, 748 description, 741 principles of gas exchange, 742, 743 technological limitations, 741 total rest vs. open lung ventilatory strategy, 745 ventilation parameters, 744 ventilator management, 741 ventilatory strategy during early phase, 743, 745–747

Index Ventilation/perfusion abnormalities aging, 198 ARDS, 200 clinical assessment, 195, 196 gas exchange AaDO2, 193, 194 alveolar gas approach, 191 alveolar gas concentrations and Va/Q ratio, 190 Bohr dead space, 193 Bohr effect, 191 mass balance, 190, 191 oxygen concentration, 191 shunt, 192 gravity and posture, 196–199 healthy young subjects, 196 lung disease, 190, 199 MIGET, 194, 195 obesity, 198 positive airway pressure, 200 pulmonary gas exchange, 189 tidal volume, ventilation mode, and cardiac output, 200, 201 VA/Q ratio, 189, 190 Ventilation-perfusion matching, 955 Ventilation-related signal (VRS), 589 Ventilator induced lung injury (VILI), 486 biotrauma and inflammation, 734, 735 etiology and pathophysiology, 732, 733 histologic alterations, 731 historical perspective, 732 inflammation and fibrosis, 735 mechanical and biological pathways, 735, 736 mechanotransduction, 733, 734 Ventilator-patient dyssynchrony assessment, 722–724 clinical consequences, 726 entrainment phenomenon, reverse triggering, 721, 724, 725 insufficient assistance (high respiratory drive) double trigger and breath stacking, 720, 723 flow starvation, 718–721 premature/short cycling, 719, 720, 722 management, 724–726 over-assistance (low respiratory drive)

971 delayed cycling, 717–719 ineffective triggering (IE), 716–718 Venturi effect, 475 Vigileo™-FloTrac system, 297 Viscoelastic model, 480 Volume clamp method, 276, 277 Volumetric capnography, 609, 612, 614 definition, 601 hemodynamic monitoring capnodynamic method, 611 capnography, 613 capnotracking method, 610, 611 fluid responsiveness, 612 Frank-Starling curve, 613 NICO monitor, 610 preload assessment, 612–613 preload-dependency assessment, 613 quantitative monitoring, 610 thermodilution, 611 main-stream capnographs, 602 respiratory monitoring monitoring gas exchange, 608–609 respiratory monitoring monitoring ventilation, 604–608 side-stream capnographs, 601 time and volume-based capnography, 602 Voluntary activation index, 547 Volutrauma, 172 W Water, 654, 655, 663, 664, 672 Wave reflection, 129, 131 Wave speed, 476 Weak acids, 660 Windkessel model, 123, 124 Work of breathing, 548–550 World Symposium on Pulmonary Hypertension (WSPH), 872 Z Zeroing, 335