ERS Handbook of Respiratory Sleep Medicine [2 ed.] 1849841640, 9781849841634, 9781849841641, 9781849841658

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handbook Respiratory Sleep Medicine

ISBN 978-1-84984-163-4

9 781849 841634

handbook Respiratory Sleep Medicine

Sleep medicine is a multidisciplinary field, with patients referred to specialising physicians from all areas of medicine. The new edition of the ERS Handbook of Respiratory Sleep Medicine is truly reflective of this diversity, covering everything from neurobiology to digital health. Broad in scope but easy to use, the book is broken down into 17 sections, including diagnosis and management, neuromuscular disorders, hypoventilation syndromes, nonrespiratory sleep disorders, and paediatrics. The Editors have brought together expert authors to create a book that focuses on practical aspects, with tips and advice based on clinical practice and the latest guidelines. This book will be invaluable to experienced sleep specialists, trainees and nurses alike.

2nd Edition

Editors Maria R. Bonsignore, Winfried Randerath, Sophia E. Schiza and Anita K. Simonds

handbook Respiratory Sleep Medicine 2nd Edition Editors Maria R. Bonsignore, Winfried Randerath, Sophia E. Schiza and Anita K. Simonds

PUBLISHED BY THE EUROPEAN RESPIRATORY SOCIETY CHIEF EDITORS Maria R. Bonsignore, Winfried Randerath, Sophia E. Schiza and Anita K. Simonds

ERS STAFF Rachel Gozzard, Caroline Ashford-Bentley, Alice Bartlett, Matt Broadhead, Neil Bullen, Clarissa Charles, Jonathan Hansen, Claire Marchant, Catherine Pumphrey and Kay Sharpe © 2023 European Respiratory Society Design by Claire Marchant and Clarissa Charles, ERS Typeset by Nova Techset Private Limited, India Indexed by Merrall-Ross International, UK Printed by Page Bros Group Ltd, Norwich, UK All material is copyright to the European Respiratory Society. It may not be reproduced in any way including electronically without the express permission of the society. CONTACT, PERMISSIONS AND SALES REQUESTS: European Respiratory Society, 442 Glossop Road, Sheffield, S10 2PX, UK Tel: +44 114 2672860 Fax: +44 114 2665064 e-mail: [email protected]

Print ISBN: 978-1-84984-163-4 Online ISBN: 978-1-84984-164-1 ePub ISBN: 978-1-84984-165-8

Table of contents Contributors ix List of abbreviations xiv Preface xv 1 – Neurobiology and physiology of sleep and breathing

1

Ivana Rosenzweig, Silvia V. Conde and Emilia C. Monteiro 2 – Classification, definition and epidemiology of sleep disordered breathing 2.1 Definitions of sleep disordered breathing 15 Johan Verbraecken 2.2 More specific grading of sleep disordered breathing 23 Steven Vits, Frederik Massie and Johan Verbraecken 2.3 Evaluation of obstructive sleep apnoea severity 32 Dirk Pevernagie, Sophia E. Schiza and Winfried Randerath 2.4 Epidemiology of obstructive sleep apnoea, central sleep apnoea and hypoventilation syndromes Johan Verbraecken

38

3 – Pathophysiology of sleep disordered breathing 3.1 The pathophysiological concept of upper airway obstruction, the arousal threshold, muscle responsiveness and respiratory drive Ludovico Messineo, Luigi Taranto-Montemurro and Elisa Perger

44

3.2 Pathophysiology of central sleep apnoea 55 Winfried Randerath 3.3 Pathophysiology of hypoventilation 65 Annabel H. Nickol

4 – Clinical aspects and consequences of sleep disordered breathing 4.1 Obstructive sleep apnoea 74 Sophia E. Schiza, Izolde Bouloukaki and Athanasia Pataka 4.2 Central sleep apnoea 84 Dimitrios Papadopoulos, Bertien Buyse and Dries Testelmans 5 – Clinical assessment 5.1 Sleep history 90 Silke Ryan 5.2 Questionnaires in respiratory sleep medicine 95 Sarah Cullivan, Barry Kennedy and Brian D. Kent 5.3 Clinical examination 101 Louise Byrne, Brian D. Kent and Barry Kennedy 5.4 Comorbidities 106 Silke Ryan 5.5 Identification of high-risk patients 112 Walter T. McNicholas 6 – Monitoring sleep and wakefulness 6.1 Methods of different sleep tests 117 Renata L. Riha 6.2 Limitations of oximetry and respiratory polygraphy in comparison with hospital-based PSG studies Renata L. Riha

133

6.3 Nocturnal capnography 136 Francesco Fanfulla 6.4 Assessment of excessive daytime sleepiness 141 Francesco Fanfulla 7 – Other diagnostic aspects of obstructive sleep apnoea and central sleep apnoea 7.1 Diagnostic algorithms based on an individualised patient approach Sophia E. Schiza, Winfried Randerath and Özen K. Basoglu

147

7.2 Screening with limited sleep tests to increase pre-test probability Sophia E. Schiza, Winfried Randerath and Marta Drummond

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8 – Management of obstructive sleep apnoea 8.1 Non-continuous positive airway pressure therapies 155 Johan Verbraecken, Olivier Vanderveken, Marie Marklund, Marijke Dieltjens and Joerg Steier 8.2 Indications for continuous positive airway pressure therapy 164 Dries Testelmans and Özen K. Basoglu 8.3 Differences between fixed-level CPAP, variable (automatic) 169 CPAP and BPAP Dries Testelmans, Alexandros Kalkanis and Bertien Buyse 8.4 Side-effects associated with continuous positive airway pressure Bertien Buyse, Alexandros Kalkanis and Dries Testelmans

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8.5 Adherence to continuous positive airway pressure treatment 181 Alexandros Kalkanis, Bertien Buyse and Dries Testelmans 8.6 Monitoring positive airway pressure therapy 186 Bertien Buyse, Alexandros Kalkanis and Dries Testelmans 8.7 Evaluation of positive airway pressure efficacy 192 Gisèle Maury and Dries Testelmans 9 – Management of central sleep apnoea 9.1 Prognostic impact of central sleep apnoea 198 Winfried Randerath 9.2 Central sleep apnoea in chronic heart failure Winfried Randerath

202

9.3 Continuous positive airway pressure or adaptive servo ventilation in patients with opioid-induced sleep disordered breathing Shahrokh Javaheri

210

9.4 Treatment of central sleep apnoea with oxygen, drugs and phrenic nerve stimulation Shahrokh Javaheri and Robin Germany

215

9.5 Treatment-emergent CSA, idiopathic CSA, high-altitude 222 periodic breathing and CSA in non-cardiac medical neurological conditions Shahrokh Javaheri, Timothy I. Morgenthaler, Winfried Randerath and Bernardo Selim 10 – Sleep disordered breathing in patients with other disorders 10.1 Asthma, chronic obstructive pulmonary disease and interstitial lung diseases Maria R. Bonsignore, Walter T. McNicholas, Izolde Bouloukaki and Sophia E. Schiza

230

10.2 Diabetes and metabolic syndrome 242 Marie Bruyneel and Maria R. Bonsignore 10.3 Hypothyroidism and acromegaly 248 Marie Bruyneel and Sonia Deweerdt 11 – Obesity hypoventilation syndrome Victor R. Ramírez Molina, Jean-Louis Pépin and Juan F. Masa Jiménez

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12 – Neuromuscular and chest wall disorders 12.1 Disorders that cause respiratory failure 263 Imran J. Meurling and Joerg Steier 12.2 Assessment of respiratory muscle weakness 269 Neeraj M. Shah, Georgios Kaltsakas and Joerg Steier 12.3 Symptoms and signs of hypoventilation 273 Anita K. Simonds 12.4 Peri-operative assessment and management of pregnancy 278 Anita K. Simonds 13 – Treatment of hypoventilation syndromes 13.1 Noninvasive ventilation 282 Marieke L. Duiverman, Renzo Boersma and Peter J. Wijkstra 13.2 Indications for tracheostomy 293 Anita K. Simonds 13.3 Cough augmentation techniques 296 Tiina Andersen and Michel Toussaint

13.4 Indications for additional oxygen treatment 302 Anita K. Simonds 13.5 Palliation and advance directives in end-stage disease 305 Anita K. Simonds 14 – Non-respiratory sleep disorders 14.1 Identifying which patients to refer for further investigation Luigi Ferini-Strambi, Francesca Casoni and Maria Paola Mogavero

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14.2 Insomnia 316 Luigi Ferini-Strambi, Marco Sforza and Andrea Galbiati 14.3 Restless legs syndrome 321 Ambra Stefani and Ulf Kallweit 14.4 Narcolepsy and idiopathic hypersomnia 325 Ulf Kallweit and Ambra Stefani 14.5 Parasomnia and associated conditions 328 Anna Heidbreder 14.6 Circadian disorders 332 Anna Heidbreder 15 – Medico-legal and organisational aspects 15.1 The medico-legal and socioeconomic impact of respiratory sleep disorders Maria R. Bonsignore, Francesco Fanfulla and Sergio Garbarino

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15.2 Organisational aspects in sleep clinics 342 Maria R. Bonsignore, Marta Amata and Giuseppe Adamo 16 – Digital health in respiratory sleep disorders 16.1 Emerging technologies to monitor sleep and circadian rhythms 348 Renaud Tamisier, Sébastien Baillieul and Jean-Louis Pépin 16.2 New digital diagnostic tools for respiratory sleep disorders Renaud Tamisier, Maelle Guellerin and Jean-Louis Pépin

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16.3 Digital health innovations for optimisation and follow-up of therapy Renaud Tamisier, Sébastien Baillieul and Jean-Louis Pépin

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16.4 Big data and artificial intelligence: opportunities and challenges Renaud Tamisier, Sébastien Bailly and Jean-Louis Pépin

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16.5 Patient empowerment/participation in care of respiratory sleep disorders Piet-Heijn van Mechelen

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17 – Paediatric respiratory sleep medicine 17.1 Development of breathing and sleep, and pathophysiology of apnoea in the first years of life Refika Ersu and Ha Trang

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17.2 Sleep disordered breathing in children 376 Refika Ersu and Ha Trang 17.3 Comorbid respiratory disorders in children 382 Stijn Verhulst and Brigitte Fauroux 17.4 Clinical assessment and diagnostic techniques Maria Pia Villa and Stijn Verhulst

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17.5 Management of sleep-related respiratory disorders in children 396 Athanasios G. Kaditis, Maria Pia Villa, Anita K. Simonds, Stijn Verhulst and Brigitte Fauroux

Contributors Chief editors Maria R. Bonsignore PROMISE Department, University of Palermo, Palermo, and Pulmonary Division, Villa Sofia-Cervello Hospital, Palermo, and Institute of Biomedical Research and Innovation (IRIB), National Research COuncil (CNR), Palermo, Italy [email protected] Winfried Randerath Bethanien Hospital, Clinic of Pneumology and Allergology, Center for Sleep Medicine and Respiratory Care, Institute of Pneumology at the University of Cologne, Solingen, Germany [email protected] Giuseppe Adamo PROMISE Department, University of Palermo, and Pulmonary Division, Villa Sofia-Cervello Hospital, Palermo, Italy [email protected] Marta Amata PROMISE Department, University of Palermo, and Pulmonary Division, Villa Sofia-Cervello Hospital, Palermo, Italy [email protected] Tiina Andersen Norwegian Advisory Unit on Long-term Mechanical Ventilation, Thoracic Department, Haukland University Hospital, and Department of Physiotherapy, Haukeland University Hospital, Bergen, and Faculty of Health and Social Sciences, Western Norway University of Applied Science, Bergen, Norway [email protected] Sébastien Baillieul University Grenoble Alpes, Grenoble, and Service Hospitalo-Universitaire Pneumologie et Physiologie, Grenoble, France [email protected]

Sophia E. Schiza Sleep Disorders Center, Department of Respiratory Medicine, School of Medicine, University of Crete, Crete, Greece [email protected] Anita K. Simonds Sleep and Ventilation Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, UK [email protected]

Sébastien Bailly University Grenoble Alpes, Grenoble, and Service Hospitalo-Universitaire Pneumologie et Physiologie, Grenoble, France [email protected] Özen K. Basoglu Department of Chest Diseases, Ege University, Faculty of Medicine, Izmir, Turkey [email protected], ozenbasoglu@ yahoo.com Renzo Boersma Department of Pulmonary Diseases/ Home Mechanical Ventilation, University of Groningen, University Medical Center Groningen, Groningen, and Groningen Research Institute of Asthma and COPD, University of Groningen, University Medical Center Groningen, The Netherlands [email protected] Izolde Bouloukaki Sleep Disorders Center, Department of Respiratory Medicine, School of Medicine, University of Crete, Crete, Greece [email protected]

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Marie Bruyneel Department of Pneumology, CHU Saint-Pierre and CHU Brugmann, and Université Libre de Bruxelles, Brussels, Belgium [email protected] Bertien Buyse Department of Respiratory Diseases, UZ Leuven, Leuven, Belgium [email protected] Louise Byrne Department of Respiratory Medicine, St James’ Hospital, Dublin, Ireland [email protected] Francesca Casoni Sleep Disorders Center, Vita-Salute San Raffaele University, Milan, Italy [email protected] Silvia V. Conde Universidade Nova de Lisboa Faculdade de Ciencias Medicas, Lisbon, Portugal [email protected] Sarah Cullivan Department of Respiratory Medicine, St James’ Hospital, Dublin, Ireland [email protected] Sonia Deweerdt Department of Pneumology, UZ Brussel, and Vrije Universiteit Brussel, Brussels, Belgium [email protected] Marijke Dieltjens Department of ENT, Antwerp University Hospital and University of Antwerp, Antwerp, Belgium [email protected] Marta Drummond Faculty of Medicine, University of Porto, and Centro de Responsabilida de Integrada de Sono e VNI, Centro Hospitalar Universitário de São João, Porto, Portugal [email protected]

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Marieke L. Duiverman Department of Pulmonary Diseases/ Home Mechanical Ventilation, University of Groningen, University Medical Center Groningen, Groningen, and Groningen Research Institute of Asthma and COPD, University of Groningen, University Medical Center Groningen, The Netherlands [email protected] Refika Ersu Division of Pediatric Respirology, Children’s Hospital of Eastern Ontario, University of Ottawa, Ottawa, ON, Canada [email protected] Francesco Fanfulla Respiratory Function and Sleep Unit, Scientific Institutes of Pavia and Montescano IRCCS, Istituti Clinici Scientifici Maugeri, Pavia, Italy [email protected] Brigitte Fauroux Pediatric Noninvasive Ventilation and Sleep Unit, AP-HP, Hôpital Necker-Enfants malades, Paris, France [email protected] Luigi Ferini-Strambi Department of Clinical Neurosciences, Neurology – Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Milan, and “Vita-Salute” San Raffaele University, Milan, Italy [email protected] Andrea Galbiati Department of Clinical Neurosciences, Neurology – Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Milan, and “Vita- Salute” San Raffaele University, Milan, Italy [email protected] Sergio Garbarino Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal/Child Sciences (DINOGMI), University of Genoa, Genoa, Italy [email protected] Robin Germany Division of Cardiovascular Diseases, University of Oklahoma, Oklahoma City, OK, USA [email protected]

Maelle Guellerin University Grenoble Alpes, Grenoble, and Service Hospitalo-Universitaire Pneumologie et Physiologie, Grenoble, France [email protected] Anna Heidbreder Department of Neurology, Medical University Innsbruck, Innsbruck, Austria [email protected] Shahrokh Javaheri Montgomery Sleep Laboratory, Bethesda North Hospital, Cincinnati, OH, and University of Cincinnati, College of Medicine, Cincinnati, OH, USA [email protected] Athanasios Kaditis Division of Pediatric Pulmonology, Sleep Disorders Laboratory, First Department of Pediatrics, University of Athens School of Medicine and Agia Sofia Children’s Hospital, Athens, Greece [email protected] Alexandros Kalkanis Department of Respiratory Diseases, UZ Leuven, Leuven, Belgium [email protected] Ulf Kallweit Clinic of Sleep and Neuroimmunology, Institute of Immunology, and Center for Biomedical Education and Research (ZBAF), University Witten/Herdecke, Witten, Germany [email protected] Georgios Kaltsakas Lane Fox Unit, Sleep Disorders Centre, Guy’s & St Thomas’ NHS Foundation Trust, and Centre for Human and Applied Physiological Sciences (CHAPS), School of Basic and Medical Biosciences, Faculty of Life Sciences & Medicine, King’s College London, London, UK [email protected] Barry Kennedy Department of Respiratory Medicine, St James’ Hospital, Dublin, Ireland [email protected]

Brian D. Kent Department of Respiratory Medicine, St James’ Hospital, Dublin, and School of Medicine, Trinity College Dublin, Dublin, Ireland [email protected] Marie Marklund Department of Orthodontics, Faculty of Medicine, Umeå University, Umeå, Sweden [email protected] Juan F. Masa Jiménez CIBER of Respiratory Diseases (CIBERES), Madrid, and Pneumology Service, San Pedro de Alcántara Hospital, Cáceres, Spain [email protected] Frederik Massie ResMed Science Center, Leuven, Belgium, and Department of Engineering, Natural Interaction Lab, University of Oxford, Oxford, UK [email protected] Gisèle Maury Department of Respiratory Diseases, Université Catholique de Louvain, Yvoir, Belgium [email protected]. be Walter T. McNicholas School of Medicine, University College Dublin, and Department of Respiratory and Sleep Medicine, St. Vincent’s Hospital Group, Dublin, Ireland [email protected] Ludovico Messineo Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA [email protected] Imran Johan Meurling Sleep Disorders Centre, Guy’s & St Thomas’ NHS Foundation Trust, London, UK [email protected] Maria Paola Mogavero Sleep Disorders Center, Vita-Salute San Raffaele University, Milan, Italy [email protected] xi

Emilia C. Monteiro CEDOC, Chronic Diseases Research Center, NOVA Medical School/ Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal [email protected] Timothy I. Morgenthaler Center for Sleep Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Mayo Clinic, Rochester, MN, USA [email protected] Annabel H. Nickol Oxford Centre for Respiratory Medicine, Oxford University Hospital NHS Foundation Trust, Oxford, UK [email protected] Dimitrios Papadopoulos Department of Respiratory Diseases, UZ Leuven, Leuven, Belgium [email protected] Athanasia Pataka Respiratory Failure Unit G Papanikolaou Hospital Thessaloniki, Aristotle University of Thessaloniki, Thessaloniki, Greece [email protected] Jean-Louis Pépin University Grenoble Alpes, Grenoble, France [email protected] Elisa Perger Istituto Auxologico Italiano, IRCCS, Sleep Disorders Center & Department of Cardiovascular, Neural and Metabolic Sciences, San Luca Hospital, and Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy [email protected] Dirk Pevernagie Department of Respiratory Medicine, Ghent University Hospital, and Department of Internal Medicine and Paediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium [email protected]

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Victor R. Ramírez Molina Hospital H+ Querétaro, Querétaro, Mexico [email protected] Renata L. Riha Department of Sleep Medicine, Royal Infirmary of Edinburgh, and University of Edinburgh, Edinburgh, UK [email protected] Ivana Rosenzweig Sleep and Brain Plasticity Centre, Neuroimaging, IoPPN, King’s College London, London, UK [email protected] Silke Ryan School of Medicine, University College Dublin, and Pulmonary and Sleep Disorders Unit, St Vincent’s University Hospital, Dublin, Ireland [email protected] Bernardo Selim Respiratory Care Unit, Division of Pulmonary, Critical Care, and Sleep Medicine, Mayo Clinic, Rochester, MN, USA [email protected] Marco Sforza Department of Clinical Neurosciences, Neurology – Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Milan, and “Vita- Salute” San Raffaele University, Milan, Italy [email protected] Neeraj M. Shah Lane Fox Unit, Sleep Disorders Centre, Guy’s & St Thomas’ NHS Foundation Trust, and Centre for Human and Applied Physiological Sciences (CHAPS), School of Basic and Medical Biosciences, Faculty of Life Sciences & Medicine, King’s College London, London, UK [email protected] Ambra Stefani Department of Neurology, Neurological Clinical Research Institute, Massachusetts General Hospital, Boston, MA, USA, and Department of Neurology, Sleep Disorders Clinic, Medical University of Innsbruck, Innsbruck, Austria [email protected], ambra. [email protected]

Joerg Steier Lane Fox Unit, Sleep Disorders Centre, Guy’s & St Thomas’ NHS Foundation Trust, and Centre for Human and Applied Physiological Sciences (CHAPS), School of Basic and Medical Biosciences, Faculty of Life Sciences & Medicine, King’s College London, London, UK [email protected] Renaud Tamisier University Grenoble Alpes, Grenoble, and Service Hospitalo-Universitaire Pneumologie et Physiologie, Grenoble, France [email protected] Hui-Leng Tan Department of Pediatric Respiratory Medicine, Royal Brompton Hospital, London, UK [email protected] Luigi Taranto-Montemurro Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA [email protected] Dries Testelmans Department of Respiratory Diseases, UZ Leuven, Leuven, Belgium [email protected] Michel Toussaint Centre de Référence Neuromusculaire, Department of Neurology, Cliniques Universitaires de Bruxelles, Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium [email protected] Ha Trang University of Paris; AP-HP, University Hospital Robert Debré; Paediatric Sleep Centre, Centre of reference for CCHS, Paris, France [email protected]

Piet-Heijn van Mechelen Stichting Apneu Research, Bentveld, the Netherlands [email protected] Olivier Vanderveken Multidisciplinary Sleep Disorders Centre, Antwerp University Hospital and University of Antwerp, Antwerp, Belgium [email protected] Johan Verbraecken Multidisciplinary Sleep Disorders Centre, Antwerp University Hospital and University of Antwerp, Antwerp, Belgium [email protected] Stijn Verhulst Department of Pediatrics, Antwerp University Hospital and Lab of Experimental Medicine and Pediatrics, University of Antwerp, Antwerp, Belgium [email protected] Maria Pia Villa Department of Pediatrics, Sleep Disease Centre, University of Rome La Sapienza-Sant’Andrea Hospital, Rome, Italy [email protected] Steven Vits Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, and ResMed Science Center, Leuven, Belgium [email protected] Peter J. Wijkstra Department of Pulmonary Diseases/ Home Mechanical Ventilation, University of Groningen, University Medical Center Groningen, Groningen, and Groningen Research Institute of Asthma and COPD, University of Groningen, University Medical Center Groningen, The Netherlands [email protected]

Conflicts of interest Disclosures for all authors are given at https://doi.org/10.1183/9781849841641.coi xiii

List of abbreviations AHI apnoea–hypopnoea index ASV adaptive servo ventilation BMI body mass index BP blood pressure BPAP bilevel positive airway pressure CHF congestive heart failure CPAP continuous positive airway pressure CSA central sleep apnoea CSR Cheyne–Stokes respiration CVD cardiovascular disease COPD chronic obstructive pulmonary disease DBP diastolic BP EDS excessive daytime sleepiness EEG electroencephalography EMG electromyography ENT ear, nose and throat EOG electrooculography EPAP expiratory positive airway pressure ESS Epworth Sleepiness Scale FVC forced vital capacity FRC functional residual capacity FEV1 forced expiratory volume in 1 s

xiv

HF heart failure ICSD International Classification of Sleep Disorders IPAP inspiratory positive airway pressure MRI magnetic resonance imaging MSLT Multiple Sleep Latency Test NIV noninvasive ventilation NREM non-rapid eye movement OHS obesity hypoventilation syndrome OSA obstructive sleep apnoea OSAS OSA syndrome OSLER Oxford Sleep Resistance Test PaCO2 arterial carbon dioxide tension PaO2 arterial oxygen tension PAP positive airway pressure PSG polysomnography PtcCO2 transcutaneous carbon dioxide tension REM rapid eye movement SAHS sleep apnoea–hypopnoea syndrome SaO2 arterial oxygen saturation SDB sleep disordered breathing SBP systolic blood pressure V'E minute ventilation

Preface Respiratory sleep medicine is a rapidly evolving discipline in pneumology. Since the first edition of the ERS Handbook of Respiratory Sleep Medicine, we have seen significant progress in the pathophysiological understanding of the various endotypes of obstructive sleep apnoea, described distinct phenotypes based on symptoms and comorbidities, and gained insights into the limitations and potential of biomarkers. This helps us to reformulate a pure mechanistic understanding of the disease. Moreover, we have proceeded from a generic definition of the disease based on the apnoea–hypopnoea index to its replacement by outcome-oriented or patient-related biomarkers. Similarly, we are currently discovering important information about the different phenotypes of central sleep apnoea and its optimal, personalised treatment. Finally, large randomised controlled studies have produced unexpected results, that underline the urgent need for a change in study design and use of refined statistical analysis based on large number of patients. Therefore, an update of the ERS Handbook of Respiratory Sleep Medicine is clearly necessary. Sleep medicine is a true multidisciplinary field. Sleep physicians are referred and treat patients from all specialties of medicine. Therefore, we are grateful for the contributions from colleagues, not only from pulmonary medicine, but also from neurology, paediatrics, psychiatry and ENT, among others. We have worked to ensure the book provides a valuable update, not only for experienced sleep specialists, but also for trainees, nurses and allied healthcare professionals. Our aim is to focus on practical aspects, tips and advice based on clinical practice and up-to-date guidelines. We are really grateful to everyone who contributed to this edition.

Maria R. Bonsignore, Winfried Randerath, Sophia E. Schiza and Anita K. Simonds Chief editors

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Neurobiology and physiology of sleep and breathing Ivana Rosenzweig, Silvia V. Conde and Emilia C. Monteiro

Sleep has been defined as a recurring, reversible neurobehavioural state of psychomotor arrest with increased arousal threshold. This neurobehavioural state is facilitated by relative perceptual disengagement and unresponsiveness to the environment. Sleep involves sets of complex interactions in the central nervous system and all other body systems that are still far from being fully understood. As humans, we spend up to third of our lives in this universal neurobehavioural state that has been observed in all species of animals and which is commonly accompanied by postural recumbence, behavioural quiescence and closed eyes. The neural regulation of the sleep–wake cycle During sleep, the brain continues to be active in complex series of stages that repeat itself in a characteristic pattern. Healthy human sleep comprises two states, REM and NREM sleep, which alternate cyclically across a sleep episode. The timing and quality of sleep are determined by intricate interplay of ultradian, homeostatic and circadian factors. Circadian and homeostatic signals are integrated in diencephalic brain structures. Circadian sleep rhythm is among several intrinsic body rhythms modulated by the hypothalamus. Its rhythmicity is based on an interlocking positive–negative feedback mechanism that controls gene transcription in the suprachiasmatic nucleus (SCN) of Key points • Glutamatergic neurons in the parabrachial nucleus provide main ascending arousal influence from the brainstem. • Cycles of NREM and REM sleep alternate throughout the night in a predictable manner. • HRV varies with gender, age, previous hypoxic exposures and sustained CO2 tension, and can be modified if hypoxia is sustained or intermittent. • Reductions in BP and heart rate occur during NREM sleep phase (dipping phenomenon). • Peaks in BP and heart rate variability are characteristic of REM and transitions from NREM sleep, and associated to high cardiovascular morbidity in the early morning. ERS Handbook: Respiratory Sleep Medicine

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the hypothalamus. The physiological mechanism of the circadian rhythm is probably initiated by light striking particular cells in the retina of the eye. These cells then secrete a hormone that causes the SCN to signal the pineal body to stop secreting melatonin. The SCN sets the body’s ‘clock’ to ∼24.2 h; light exposure and schedule clues entrain this to the 24-h cycle. Circadian rhythm disruption can have severe health implications in multiple organ systems. Recently, the presence of secondary or peripheral oscillators has been demonstrated throughout the body. Whilst they act independently, they are all nonetheless synchronised with the SCN, as well as other external cues, such as temperature and timing of meals. The best method of measuring the circadian rhythm includes monitoring the core body temperature and salivary or plasma melatonin levels. Previous models of brain circuitry controlling homeostatic wake–sleep focused on monoaminergic and cholinergic arousal systems. However, recent evidence suggests that these may play a modulatory role, and that the backbone of the wake–sleep regulatory system depends upon glutamate and γ-aminobutyric acid (GABA) fast neurotransmitters (figure 1). The brain transitions from a slow-wave state to REM sleep (figure 2), the brain state with a faster, low voltage EEG and loss of muscle tone (atonia), associated with REMs. The REM sleep is likely generated by a population of glutamatergic neurons in the region just ventral to the locus coeruleus, in the region often referred to as the subcoeruleus region. The ventrally based neurons from this region instigate motor atonia due to activation of inhibitory interneurons in the medulla and the spinal cord. REM sleep is associated with EEG desynchronisation, the source of which is yet unknown. However, all the nearby regions that project to the forebrain, namely the parabrachial nucleus, pedunculopontine and laterodorsal tegmental nucleus, also contain REM-active neurons. Similarly, it is not known which circuitry underlies the activation of eye movements but some studies suggest that they may be due to short projections to the paramedian pontine reticular formation. A main control over the REM generator is through inhibitory, mostly GABAergic neurons in the nearby ventrolateral periaqueductal grey matter, at the level where the cerebral aqueduct begins to open into the fourth ventricle. Normal sleep architecture Sleep has a unique structure with a cyclical pattern composed of different sleep stages and transitions between them. Sleep architecture is traditionally represented by a graph called a hypnogram (figure 3). Sleep architecture and stages can be evaluated by PSG, which evaluates brain activity using specific scalp EEG channels and eye movements with EOG electrodes. Other EMG electrodes, and respiratory and cardiac monitoring are also required. In 1968, Rechtschaffen, Kales and a committee of experts established the rules for the scoring of sleep in normal human adults, on which the current American Academy of Sleep Medicine (AASM) scoring is based. The sleep scoring assesses data seen in sequential 30-s images (epochs) of PSG. The different sleep stages of NREM–REM cycling are associated with diverse physiological changes. In adults, sleep is most often initiated through NREM sleep and is marked by synchronisation of EEG activity. NREM sleep has increased parasympathetic tone with slow heart rate, low BP and decreased respiratory rate. Body temperature is lowest during NREM sleep. During REM sleep, autonomic instability with bursts of sympathetic activity causes irregular heart rate and transient increases in BP. Similarly,

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PFZ (GABA)

Cerebellum Brainstem

Figure 1.  a) A schematic presentation of the fast neurotransmitter systems that have the largest role in promoting wakefulness. The monoaminergic, cholinergic and peptidergic neurons in the brainstem and hypothalamus, which were prominent in earlier models, are here shown in brown. They play a modulatory role and lesions in these locations have little effect on wake–sleep amounts. The backbone of the arousal system is shown here in red: this is the glutamatergic input from the parabrachial nucleus (PB) and pedunculopontine tegmental nucleus (PPT) to the basal forebrain, and the GABAergic and cholinergic neurons in the basal forebrain (BF) that diffusely innervate the cerebral cortex. Lesions at these sites result in loss of consciousness, whereas lesions of supramammillary (SUM) glutamatergic or dopaminergic (DA) neurons in the ventral periaqueductal grey matter (vPAG) near the dorsal raphe nucleus commonly cause ∼20% loss of wake time. Additionally, two populations of GABAergic neurons in the lateral hypothalamus (LH), shown in purple, may also promote wakefulness by inhibiting sleep promoting neurons in the thalamus and preoptic area. b) A schematic presentation of the fast neurotransmitter systems that contribute to sleep promotion (purple). Ventrolateral preoptic (VLPO) and median preoptic (MnPO) GABAergic neurons send axons to most components of the arousal system (shown in red, orange and green), and are thought to inhibit them in a coordinated fashion. Parafacial zone (PFZ) GABAergic neurons in the medulla have a pro-hypnotic effect by inhibiting the parabrachial glutamatergic arousal neurons. Melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus contain both GABA and glutamate (Glu), and may be able to release them at different terminal sites, including neurons in the brainstem that control REM sleep. 5HT: serotonin; ACh: acetylcholine; Hist: histamine; LC: locus coeruleus; LDT: laterodorsal tegmental nucleus; NA: noradrenaline; ORX: orexin; TMN: tuberomammillary nucleus. Reproduced and modified from Saper et al. (2017) with permission from the publisher.

during REM, respiratory rate increases, but the ventilatory drive responding to hypoxia and hypercapnia decreases. A healthy nocturnal pattern of sleep (figure 3) commonly includes several consistent features. It starts with NREM N1 and progresses through deeper NREM stages (N2 and N3), before the first episode of REM sleep occurs approximately 80–100 min later. After that, NREM and REM sleep cycle with a period of ∼90 min. The 90-min NREM–REM cycle is repeated approximately three to six times during the night, with N3 sleep stages being more concentrated in the early NREM cycles, while REM sleep episodes lengthen through the night. The preferential occurrence of NREM sleep (e.g. slow-wave sleep (SWS)) early in the night is coincidental with sleep homeostasis, while the predominance of REM sleep later in the night is thought to be associated with the circadian rhythm of core body temperature. The transition from wake to sleep can be difficult to determine as there are typically brief periods of drowsiness with transient bursts of wakefulness before sleep consolidation. ERS Handbook: Respiratory Sleep Medicine

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a)

b)

Glycine and GABA

C1–C8 T1–T12 L1–L5 S1–S5

GABA 5HT Noradrenaline ACh Glycine Glu and GABA Motor pools Groups of respiratory neurons Inspiratory neurons Inspiratory projections Excitation Inhibition

C1–C8 T1–T12 L1–L5 S1–S5

c) VRG

XII

Hypoglossal (XII)

DRG

PBC

Tongue

Figure 2. The main neuronal groups and their role in generating the brain state of REM sleep. a) Traditionally, interactions of cholinergic and aminergic cell clusters has been used to explain the defining features of REM sleep: 1) ascending cortical activation and 2) descending spinal motor inhibition. b) Recent advances suggest that REM sleep is due to the interaction of glutamatergic and GABAergic cell clusters; for more details, see the main text. The inhibition of spinal motor activity in REM sleep is mediated by descending projections to the medial and ventral horn of the spinal cord, and increased release of the inhibitory amino acids glycine (predominantly) and GABA onto spinal motoneurons. c) The mechanism of upper airway motor suppression in REM sleep appears different. For the hypoglossal motor pool, for example, which innervates the musculature of the tongue via cranial nerve XII, a cholinergic mechanism mediates the strong motor inhibition of REM sleep. This inhibition counteracts the inspiratory drive to motor pool that originates from the ventral respiratory group via the pre-Bötzinger complex (PBC) and premotoneurons in the lateral reticular formation (the latter two indicated as inspiratory neurons, in blue). The hypoglossal motor pool also receives tonic state-dependent drive from the reticular formation (in grey). C: cervical vertebra; T: thoracic vertebra; L: lumbar vertebra; S: sacral vertebra; 5HT: serotonin; ACh: acetylcholine; Glu: glutamate. Reproduced and modified from Horner et al. (2016) with permission from the publisher.

The molecular mechanisms that control sleep rhythms are highly phylogenetically conserved. The characteristic EEG of NREM sleep comprises prominent oscillatory thalamocortical rhythms that include the delta and spindle rhythms, as well as a recently described slow (12 h per night may be required for preschoolers. This equates to ∼12 h per night for primary school children in order to achieve optimal cognitive functioning and development. Paediatric daytime somnolence is known to be associated with slower improvement in verbal comprehension. Notably, daytime naps have been shown to help consolidate learning in preschool children and the memory loss associated with nap deprivation in this age group was not reversed with an overnight sleep. This may be of particular relevance for children with a learning delay. Adults need around 7–8 h sleep per night. Ageing negatively affects total sleep quantity, sleep efficiency and SWS. The incidence of waking after sleep onset tends to increase with ageing process. Ageing also affects sleep architecture so that time spent in SWS and REM sleep diminishes, whilst the time spent in stages N1 and N2 of NREM sleep increases. Thus, normal ageing appears to be associated with reduced ability to initiate and maintain sleep. Physiological later age-dependent changes in sleep include changes in sleep architecture, increased sleep fragmentation and increased susceptibility to certain sleep disorders, such as OSA, insomnia and REM behaviour disorder. Moreover, PSG findings, such as significant reductions in SWS associated with ageing, are linked to poorer episodic memory scores. In addition, spectral power in the frequency range of sleep spindles is also reduced in middle-aged and older adults, with maximal reductions observed over frontal regions. However, the largest age-related impairments appear in the final sleep cycles of the night, with significant reduction of faster frequency spindles. Conversely, the duration of REM sleep remains relatively constant throughout adulthood. ERS Handbook: Respiratory Sleep Medicine

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In summary, sleep beyond the fifth decade of age is associated with several wellcharacterised changes in sleep architecture that include advanced sleep timing (i.e. earlier bedtimes and rise times), longer sleep-onset latency, shorter overall sleep duration, increased sleep fragmentation, more fragile sleep (i.e. greater likelihood of being woken by external sensory stimuli), reduced SWS, increased time spent in lighter N1 and N2 sleep, shorter and fewer NREM–REM cycles, and finally, an increased time spent awake throughout the night. The increased number of arousals per night may be consequent to the decline in neural systems that regulate sleep or an age-related change in arousal thresholds to external stimuli. Of note is, however, that whilst older adults experience more awakening during sleep, they do not seem to report extensive middle insomnia. In addition, there are some remarkable sex differences. For instance, majority of sleep ageing changes have been reported as more prominent in men. Nonetheless, this changes in women in the perimenopausal or postmenopausal period, who then appear to show similar decline in quality and quantity of sleep. Sex differences in sleep Sleep is known to be modulated by sex hormones, although the exact mechanisms underlying this interplay remain to be fully mapped out. For instance, sleep is known to be affected by the ovarian hormones in women across the adult lifespan. Development of sleep disorder following menopause contributes to accelerated cognitive decline and dementia in older women. Disturbed sleep architecture during perimenopausal changes is associated with the presence of vasomotor symptoms (hot flushes) and lower sleep efficiency. Higher cortisol levels or greater cortisol reactivity has been suggested as one mechanism that links hot flashes, sleep and depressive or anxiety symptoms to decrements in cognitive performance. Sleep disturbances and insomnia are reported by 40–60% of perimenopausal women. Moreover, the perimenopausal transition is linked with increased frequency of self-reported problems, such as falling and staying asleep, and reduced total sleep time. (Peri)menopausal sleep disruption can exacerbate other pre-existing sleep disorders, including restless leg syndrome and circadian disorders, as well as lead to increased prevalence of new ones, such as OSA. OSA prevalence increases partly due to weight gain, and probably also due to hormone changes and other mechanisms. Similarly, higher risk of insomnia, and depressive and anxiety disorders are reported. Of note is that postmenopausal women who receive timely hormone replacement therapy have a reduced latency to fall asleep, and fewer night-time awakenings and less wakefulness. In addition, the timing of oestrogen exposure in relation to the menopausal transition and age are increasingly seen as clinically important. In the case of cognitive outcomes, some evidence supports the ‘critical window hypothesis’ that suggests that exposure early in the menopausal transition or postmenopausal period may confer cognitive benefit, with exposure later in the menopausal transition having no, or even detrimental effects. Sleep complaints are also known to increase during other periods of large fluctuations of ovarian hormones, including during puberty, pregnancy and the menopausal transition. Moreover, there is also evidence for sleep changes across the menstrual cycle. Poorest quality of sleep is traditionally reported during the mid-to-late luteal phase. This phase is associated with increased reports of nighttime awakenings and arousals, and with decreased SWS. In addition, sleep spindles have been reported as more frequent and longer in duration, and to occur in higher EEG spectral frequency during the luteal, than the follicular, phase. Interestingly,

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differences in objective sleep measures have been demonstrated in women taking oral contraceptives, with increased N2 and REM, and reduced SWS, relative to naturally cycling women. Moreover, women and men appear to differ in the effect of sleep on the hypoxic ventilatory response (HVR). It is known that the ventilatory response to hypoxia falls during sleep in healthy adults. In men, the HVR has been shown to be lower during NREM sleep than during wakefulness. However, in women, the responses were shown to be similar in wakefulness and NREM sleep. Previous investigations have demonstrated that progesterone may be a ventilatory stimulant and, correspondingly, women have been shown to have higher ventilatory responses in the luteal, than in the follicular, menstrual phase. Taken together, past studies suggest that women, with relatively higher resting ventilation, have lower responses to hypoxia and hypercapnia. The major sex/gender difference appears in the levels of ventilatory response during wakefulness, which is much higher in men than in women. However, the ventilatory responses in NREM sleep, to hypocapnic hypoxia and to the posthypoxic ventilatory decline, appear similar in both sexes/genders. During REM sleep, both sexes have been reported to have lower HVRs than in NREM sleep. Control of breathing during sleep and wakefulness Problems with breathing are present in most common sleep disorders and are associated with a wide range of poor health outcomes. Continuous breathing movements only become apparent around the 11th week of gestation. They are initially present during periods of low-amplitude electrocortical activity, eye movement and no neck EMG activity, corresponding to the REM sleep state. During NREM sleep, a potent inhibition (from the lateral pons) leads to a complete cessation of breathing movements. At birth, the newborn establishes gas exchange through the lungs. Thereafter, during the first year of life, respiratory control and the chemical drives are subjected to significant maturation during which irregularities of the breathing pattern become less frequent. The brainstem respiratory network includes: • Respiratory neurons that generate respiratory rhythm and drive the expression of rhythmic activity in other components of the respiratory network. • Respiratory motor pools that activate the primary and secondary muscles of breathing. • Chemosensors that detect alterations in blood gases and elicit a physiological response. The primary respiratory muscles are those that generate airflow, such as the diaphragm. Conversely, the secondary accessory muscles, such as the pharyngeal muscles, significantly modulate airflow passage. They can also support the act of breathing, for example, the intercostal muscles, which contribute to the maintenance of lung volume. The level of respiratory-related and tonic activities varies for different muscle groups, with some muscles expressing mainly tonic activity and others (e.g. intercostals) expressing both tonic and respiratory activity. Tonic activity is commonly suppressed in sleep. The main components of the respiratory network (figure 2c) are traditionally divided into the ventral (VRG) and dorsal respiratory groups (DRG). The VRG group includes Bötzinger expiratory complex neurons, pre-Bötzinger inspiratory complex neurons, ERS Handbook: Respiratory Sleep Medicine

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rostral VRG (predominantly inspiratory) neurons and caudal VRG (predominantly expiratory) neurons. The DRG consists of primarily inspiratory neurons that, together with the nucleus of the solitary tract, receive projections and afferents important to the reflex control of breathing. These afferents include the carotid and aortic chemoreceptors and baroreceptors, and lung vagal afferents. Importantly, in the case of hypoventilation during sleep, the physiological response includes both an attempted chemoreceptor-mediated increase in ventilation and arousal from sleep. In summary, the brain functions as a gain-setting device for breathing by altering the neurochemistry of the key elements of respiratory control, including the respiratory neurons, the motoneurons and the sites involved in the reflex modulation of breathing. Moreover, the respiratory network is moderated by the same state-dependent arousal and sleep systems shown in figures 1 and 2. During sleep, there is an increase in the GABAergic inhibitory system tone and a corresponding decrease in excitatory influences from the brain arousal systems. The overall result may be decreased brain arousability, including alteration of drives to the respiratory network. These statedependent changes affect the musculature, especially that of the upper airways, potentially leading to OSAs and hypopnoeas in susceptible individuals (figure 2c). The automatic rhythm of breathing is generated by the intrinsic pacemaker cell membrane properties and the sum effects of their interconnectivity within the respiratory network. Essential to expression of this rhythmicity is a sufficient level of underlying tonic excitation, which is regulated by the wakefulness-dependent neural systems (figure 1) as well as the peripheral and central chemoreceptors (figures 4 and 5). Thus, the brain arousal systems provide a major source of such

5HT Noradrenaline Retrotrapezoid nucleus Nucleus tractus solitarius

Cell activity

Awake

NREM sleep Inspired CO2

VRG (rostral region)

Figure 4.  The key brain regions of interest of chemoception/responsivity to changes in CO­2 and protons (H+). The retrotrapezoid nucleus located near the ventral surface of the medulla is a key region that is intrinsically sensitive to alterations in CO2/H+. Dendrites from retrotrapezoid neurons are in contact with the cerebrospinal fluid at the medullary surface and are activated by increased CO2 (decreased H+). The graph demonstrates the response of a brainstem serotonergic neuron to increases in inspired CO2. Of note is: 1) baseline activity (i.e. at zero inspired CO2) is higher in wakefulness than in sleep; 2) the neuronal activity at any given inspired CO2 is greater in wakefulness than in sleep; 3) the slope (gain) of response is also greater in wakefulness than in sleep. These are three main features that comprise the overall respiratory responses to CO2, as measured by changes in ventilation. 5HT: serotonin. Reproduced and modified from Horner et al. (2016) with permission from the publisher.

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‘behavioural’ excitation to modulate breathing volitionally or nonvolitionally. During the transition to NREM sleep, such behavioural influences on respiratory network activity are reduced and as a result, the respiratory system becomes dependent upon feedback regulation in NREM sleep to sustain sufficient activity. In NREM sleep, tonic activity of the peripheral and central chemoreceptors maintains effective breathing. However, any pathological process that may affect feedback chemoreceptor control will thus cause severe respiratory disturbance. Conversely, in REM sleep, the heightened state of brain arousal (figure 2) restores adequate behavioural drives to the respiratory network. Under physiological conditions, the levels of oxygen (O2) and carbon dioxide (CO2) in the blood remain remarkably consistent under disparate physiological states, ranging from SWS to exercise. The stable modus operandi is maintained through the concerted actions of central and peripheral chemosensing mechanisms, of which chemoreception of CO2 (figure 4) is, in humans, more sensitive than that of O2 (figure 5). It has been shown that relatively small (∼1.3 kPa (∼10 mmHg)) increases in PaCO2 from normal circulating levels (6.0 kPa (45 mmHg)) are sufficient to promote a marked change in V′E. However, a much greater PaO2 decrease (2.7–5.3 kPa (20–40 mmHg)) from physiological levels is required to markedly change basal V′E. Sensitivity of the peripheral chemoreceptors to oxygen increases dramatically only when the O2 tension of the blood flowing through the carotid body (CB) falls from between 10.7 kPa (80 mmHg) and 13.3 kPa (100 mmHg) to 8.0 kPa (60 mmHg). Ventilatory response to hypercapnia and hypoxia The ventilatory responses to hypoxia and hypercapnia are fundamental to the homeostatic regulation of arterial blood gases. These ventilatory responses are reduced in NREM sleep, compared to wakefulness, and are further reduced in REM sleep. Mammals respond to hypercapnia and hypoxia by increasing V′E to maintain a neutral balance of acid and base (figure 4). Hypercapnia is the main driver of the ventilatory response and is likely mediated by the retrotrapezoid nucleus, located on the ventral medullary surface with afferents to the respiratory rhythm generator (figure 4). The consequent hypercapnic ventilatory response (HCVR) is expressed as the change Stimulus

Transduction

Outputs

Glomus cell output CSN output Neurotransmitter Action potential release frequency

Systemic output Ventilation (HVR)

60

SaO2 (%) 100

VE (L·min–1)

50

PO2 Blood

Chemoreceptor cell

CSN

Respiratory sytem

0 20 100 PaO2 (mmHg)

Figure 5.  Ventilatory response to hypoxaemia. Decreased PaO2 is sensed by the carotid body (CB), the major peripheral chemoreceptor involved in hypoxic response. In the CB, hypoxia activates the release of neurotransmitters from CB glomus cells that increase the activity of the CSN, which is integrated in the brainstem to produce the HVR. The HVR aims to restore normal blood O2 levels. Also note the inverse exponential relationship between hypoxaemia and HVR, with the HVR being higher at lower PaO2. PO2: oxygen tension. ERS Handbook: Respiratory Sleep Medicine

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in V′E per change in CO2 at the end of exhalation. In contrast, the HVR arises from peripheral chemoreceptors that sense PaO2 and are located mainly at the bifurcation of the common carotid arteries, the so-called CB (figure 5). It is expressed as the change in V′E per change in SaO2. There is inter-individual variation in these ventilatory responses, which may be genetic. The threshold level of hypercapnia to provoke arousal from sleep is similar, on average, between NREM and REM sleep in humans, whereas the level of asphyxial hypoxia (e.g. hypoxia of the type experienced in OSA) often elicits arousal at lower SaO2 in REM than NREM sleep. However, isocapnic hypoxia is generally a weak stimulus of arousal from sleep and the threshold is similar between NREM and REM sleep. Moreover, the normal decrease in V′E from wakefulness to sleep commonly results in minimal changes in SaO2, predominantly because the starting PaO2 is on the flat portion of the oxyhaemoglobin dissociation curve. However, if an individual is initially hypoxaemic, therefore initially positioned on the steep portion of the dissociation curve, the effects can be significant. This can happen in individuals at altitude, or with depressed ventilation or impairments in breathing for any reason. The core physiological principle is that any state that lowers PaO2 in wakefulness will similarly predispose to worsening hypoxaemia in sleep, especially in REM sleep. The arterial chemoreceptors, aortic bodies and, in particular, the CB are activators of regulatory mechanisms that act to minimise hypoxia and to prevent its deleterious effects. The CBs are polymodal chemoreceptors, that sense PaO2, PaCO2 and pH, among other stimuli. They are responsible for the greatest part of the hyperventilation observed during hypoxaemia and they contribute to the hyperventilation that accompanies respiratory or metabolic acidosis. The remaining respiratory drive is due to aortic bodies in the case of hypoxaemia and to central chemoreceptors in the case of acidosis. The chemosensory unit of the CB are the glomus cells that, in response to hypoxia, hypercapnia and acidosis, release neurotransmitters that modify (increasing or inhibiting) the frequency of carotid sinus nerve (CSN) sensory fibres. Central CSN projections terminate in the brainstem, where the firing frequency is integrated by the respiratory central control system, generating a compensatory ventilatory response (figure 5). This response is defined as HVR. The HVR is characterised by an increase in V′E that allows the body to restore normal blood O2 levels. The HVR depends on the intensity of hypoxia and on its duration. The isocapnic HVR is divided into two phases: • a first phase (0–5 min) of immediate ventilation increase (acute HVR (AHVR)) • a second phase (5–20 min) of slow decline (hypoxic ventilatory decline) The CSN PaO2 threshold to hypoxia corresponds to 9.3–10.0 kPa (70–75 mmHg) and below this, the slope between PaO2 and CSN discharges changes abruptly from linear to exponential until 1.3–2.0 kPa (10–15 mmHg). At very low PaO2 (45 mmHg) while asleep or levels that are disproportionately increased in relation to those during wakefulness (American Sleep Disorders Association, 1999). An elevated PaCO2 when waking is very suggestive, as is an elevated bicarbonate (HCO3−) in the blood gas analysis (without any other evident explanation, such as use of diuretics). To find these patients, a series of daytime tests is useful: FVC 27 mmol·L−1. This group includes six subcategories, which will be discussed below (ICSD-3). Among these categories, OHS is the most common clinical presentation of this syndrome. When brainstem anomalies are ruled out (preferably via MRI), the term “idiopathic central hypoventilation syndrome” is used. Finally, sleep-related hypoxaemia disorder, and isolated symptoms and normal variants constitute two smaller categories, which will not be further discussed here. The definition of OSA OSA can be considered if A and B or C satisfy the criteria (ICSD-3): A. The presence of one or more of the following: 1. The patient complains of sleepiness, non-restorative sleep, fatigue or insomnia symptoms. ERS Handbook: Respiratory Sleep Medicine

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2. The patient wakes with breath-holding, gasping or choking. 3. The bed partner or other observer reports habitual snoring, breathing interruptions or both during the patient’s sleep. 4. The patient has been diagnosed with hypertension, a mood disorder, cognitive dysfunction, coronary artery disease, stroke, CHF, atrial fibrillation (AF) or type 2 diabetes mellitus. B. PSG or out-of-centre sleep testing (OCST) demonstrates: 1. Five or more predominantly obstructive respiratory events (obstructive and mixed apnoeas, hypopnoeas or respiratory effort-related arousals (RERAs)) per hour of sleep during PSG or per hour of monitoring (OCST). OR C. PSG or OCST demonstrates: 1. ≥15 predominantly obstructive respiratory events (apnoeas, hypopnoeas or RERAs) per hour of sleep during a PSG or per hour of monitoring (OCST). Obstructive breathing events may include not only apnoeas or hypopnoeas but also RERAs. A RERA is a series of breaths characterised by increasing respiratory effort, leading to microarousal from sleep, but not meeting the criteria for an apnoea or hypopnoea. These events present with a pattern of increasingly negative oesophageal pressures, ending with an abrupt change in pressure to a less negative level and a microarousal. Oesophageal pressure has been proposed as the recommended sensor (American Sleep Disorders Association, 1999); the suggested feasible alternative is a flattening of the flow curve via nasal pressure, together with induction plethysmography. In daily practice, nasal pressure is the method of choice for the majority of sleep laboratories. These events last ≥10 s. The definition of CSA Diagnosis of CSA can also be made using criteria recommended by the ICSD-3. CSA with CSR CSR is a form of unstable breathing in which the same mechanisms apply as in primary CSA, alone or in combination with central apnoea. A periodic pattern of waxing and waning ventilation with episodes of hyperventilation, alternating with central apnoeas and hypopnoeas, is defined as CSR. CSR can be considered if A or B+C+D satisfy the criteria (ICSD-3): A. The presence of one or more of the following: 1. Sleepiness. 2. Difficulty initiating or maintaining sleep, frequent awakenings or non-restorative sleep. 3. Awakening short of breath. 4. Snoring. 5. Witnessed apnoeas. B. The presence of AF/flutter, CHF or a neurological disorder. C. PSG (during diagnostic or PAP titration) shows all of the following: 1. Five or more central apnoeas and/or central hypopnoeas per hour of sleep. 2. The total number of central apnoeas and/or central hypopnoeas is >50% of the total number of apnoeas and hypopnoeas. 3. The pattern of ventilation meets criteria for CSR. D. The disorder is not explained more clearly by another current sleep disorder, medication use (e.g. opioids) or substance use disorder.

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The pattern of ventilation meets the criteria for CSR (crescendo–decrescendo of ≥10 min, with at least three cycles, a minimum sleep registration time of 2 h and a cycle length of ≥40 s) (Berry et al., 2012). CSR occurs in patients with (acute or chronic) HF and after stroke. It is characterised by an unstable breathing pattern with a relatively long cycle time of 40–90 s, with an observable arousal at the peak of the hyperpnoea phase. In CSR, oxygen desaturations are characteristically long and recover slowly. An underlying cardiopulmonary disease is present in ∼70% of patients with CSR. CSA due to a medical disorder without CSR This subcategory is used for CSA that is attributed to a medical or neurological condition (and does not have the pattern of CSR). The majority of the patients in this category have brainstem anomalies. Criteria A–C must be met (ICSD-3): A. The presence of one or more of the following: 1. Sleepiness. 2. Difficulty initiating or maintaining sleep, frequent awakenings or non-restorative sleep. 3. Awakening short of breath. 4. Snoring. 5. Witnessed apnoeas. B. PSG shows all of the following: 1. Five or more central apnoeas and/or central hypopnoeas per hour of sleep. 2. The number of central apnoeas and/or central hypopnoeas is >50% of the total number of apnoeas and hypopnoeas. 3. Absence of CSR. C. The disorders occurs as a consequence of a medical or neurological disorder but is not due to medication use or substance abuse. CSA due to high-altitude periodic breathing This condition occurs typically at >2500 m high. Criteria A–D must be met (ICSD-3): A. Recent ascent to a high altitude B. The presence of one or more of the following: 1. Sleepiness. 2. Difficulty initiating or maintaining sleep, frequent awakenings or non-restorative sleep. 3. Awakening short of breath or morning headache. 4. Witnessed apnoeas. C. The symptoms are clinically attributable to high-altitude periodic breathing, or PSG, if performed, demonstrates recurrent central apnoeas or hypopnoeas primarily during NREM sleep at a frequency of ≥5 events·h−1. D. The disorder is not better explained by another current sleep disorder, medical or neurological disorder, medication use (e.g. opioids), or substance use disorders. CSA due to medication or a substance CSA is found with acute as well as chronic opioid intake, depending on the dosage. Criteria A–E must be met (ICSD-3): A. The patient is taking an opioid or other respiratory depressant. B. The presence of one or more of the following: 1. Sleepiness. ERS Handbook: Respiratory Sleep Medicine

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2. Difficulty initiating or maintaining sleep, frequent awakenings or non-restorative sleep. 3. Awakening short of breath. 4. Snoring. 5. Witnessed apnoeas. C. PSG (diagnostic or on PAP) shows all of the following: 1. Five or more central apnoeas and/or central hypopnoeas per hour of sleep (PSG). 2. The number of central apnoeas and/or central hypopnoeas is >50% of the total number of apnoeas and hypopnoeas. 3. Absence of CSR. D. The disorder occurs as a consequence of an opioid or other respiratory depressant. E. The disorder is not better explained by another current sleep disorder. Primary CSA In the majority of patients with CSA, no underlying disease has been ascertained. This variant is called primary CSA. Patients do not display any typical respiratory pattern during sleep (CSR), and central apnoeas can either be isolated or occur in cycles of ∼40 s. There are arousals at the end of the apnoea, which fragment sleep. The main symptoms are daytime sleepiness or even sleeplessness. Criteria A–D must be met (ICSD-3): A. The presence of at least one of the following: 1. Sleepiness. 2. Difficulty initiating or maintaining sleep, frequent awakenings or non-restorative sleep. 3. Awakening short of breath. 4. Snoring. 5. Witnessed apnoeas. B. PSG demonstrated all of the following: 1. Five or more central apnoeas and/or central hypopnoeas per hour of sleep (PSG). 2. The number of central apnoeas and/or central hypopnoeas is >50% of the total number of apnoeas and hypopnoeas. 3. Absence of CSR. C. There is no evidence of daytime or nocturnal hypoventilation. D. The disorder is not explained more clearly by another current sleep disorder, medical or neurological disorder, medication use or substance use disorder. Primary CSA of infancy For further information on this topic, we refer the interested reader to chapter 17 on paediatric respiratory sleep medicine. Primary CSA of prematurity For further information on this topic, we refer the interested reader to chapter 17 on paediatric respiratory sleep medicine. Treatment-emergent CSA Treatment-emergent CSA (TECSA) should be used in patients with predominantly obstructive events during the diagnostic set-up, who exhibit central apnoeas or hypopnoeas when applying CPAP/PAP as a treatment for OSA. CSA associated with other identifiable aetiologies, such as CSR or substance-induced CSA, cannot be classified as treatment-emergent.

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Criteria A–C must be met (ICSD-3): A. Diagnostic PSG shows five or more predominantly obstructive respiratory events (obstructive or mixed apnoeas, hypopnoeas or RERAs) per hour of sleep. B. PSG during use of PAP without a back-up rate shows significant resolution of obstructive events and emergence or persistence of central apnoea or central hypopnoea with both of the following: 1. Central AHI ≥5 events·h−1. 2. Number of central apnoeas and central hypopnoeas is ≥50% of total number of apnoeas and hypopnoeas. C. The CSA is not better explained by another CSA disorder (e.g. CSA with CSR or CSA due to a medication or substance). Definition of hypoventilation syndromes OHS OHS is probably the most common clinical presentation of hypoventilation. Criteria A–C must be met (ICSD-3): A. The presence of hypoventilation during wakefulness (PaCO2 >6.0 kPa (>45 mmHg)), as measured by PaCO2, end-tidal carbon dioxide tension or PtcCO2. B. The presence of obesity (BMI >30 kg·m−2; >95th percentile for age and sex for children). C. Hypoventilation is not primarily due to lung parenchymal or airway disease, pulmonary vascular pathology, chest wall disorder (other than mass loading from obesity), medication use, a neurological disorder, muscle weakness, or a known congenital or idiopathic central alveolar hypoventilation syndrome. A new classification of patients with OHS has been proposed by the European Respiratory Society (ERS) (Randerath et al., 2017). Congenital central hypoventilation syndrome Nocturnal hypoventilation is characteristic of congenital central hypoventilation syndrome, often with severe hypercapnia (PaCO2 >8.0 kPa (>60 mmHg)) and prolonged oxygen desaturation, immediately after falling asleep. There is often a certain degree of hypoventilation when awake. Primary cardiac or pulmonary diseases must be ruled out, as well as neuromuscular diseases, brainstem abnormalities (by neuroimaging) and congenital metabolic disorders. The cause of congenital central hypoventilation syndrome is a mutation in the PHOX2B gene (mostly the novo), which is present in 100% of cases. Criteria A and B must be met (ICSD-3): A. Sleep-related hypoventilation is present. B. Mutation of the PHOX2B gene is present. Late-onset central hypoventilation with hypothalamic dysfunction For the specific criteria, we refer the interested reader to the ICSD-3, as this is a very rare condition. Idiopathic central alveolar hypoventilation Criteria A and B must be met (ICSD-3): A. Sleep-related hypoventilation is present. ERS Handbook: Respiratory Sleep Medicine

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B. Hypoventilation is not primarily due to lung parenchymal or airway disease, pulmonary vascular pathology, a chest wall disorder, medication use, a neurological disorder, muscle weakness, or obesity of congenital hypoventilation syndromes. Sleep-related hypoventilation due to medication or a substance Criteria A–C must be met (ICSD-3): A. Sleep-related hypoventilation is present. B. A medication or substance known to inhibit respiration and/or ventilator drive is believed to be the primary cause of sleep-related hypoventilation. C. Hypoventilation is not primarily due to lung parenchymal or airway disease, pulmonary vascular pathology, chest wall disorder, neurological disorder, muscle weakness, OHS, or a known congenital central alveolar hypoventilation syndrome. Hypoventilation may be present during wakefulness but is not required for the diagnosis. Sleep-related hypoventilation due to a medical disorder Criteria A–C must be met (ICSD-3): A. Sleep-related hypoventilation is present. B. A lung parenchymal or airway disease, pulmonary vascular pathology, chest wall disorder, neurological disorder or muscle weakness is believed to be the primary cause of hypoventilation. C. Hypoventilation is not primarily due to OHS, medication use or a known congenital central alveolar hypoventilation syndrome. Hypoventilation may be present during wakefulness but is not required for the diagnosis. Further reading • American Academy of Sleep Medicine (2014). International Classification of Sleep Disorders. 3rd Edn. Darien, American Academy of Sleep Medicine. • American Sleep Disorders Association (1999). Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. Sleep; 22: 667–689. • Berry RB, et al. (2012). Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for scoring of sleep and associated events. J Clin Sleep Med; 8: 597–618. • Iber C, et al. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. Darien, American Academy of Sleep Medicine. • Randerath W, et al. (2017). Definition, discrimination, diagnosis and treatment of central breathing disturbances during sleep. Eur Respir J; 49: 1600959. • Verbraecken J, et al. (2009). Upper airway mechanics. Respiration; 78: 121–133.

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More specific grading of sleep disordered breathing Steven Vits, Frederik Massie and Johan Verbraecken

The AHI calculates the frequency of apnoea–hypopnoea events but does not take into account the amount of hypoxaemia provoked by obstructive respiratory events, the length of events, or other pathophysiological consequences. Moreover, in patients with rather low AHIs, a therapeutic dilemma can occur. These patients are often susceptible to being part of different SDB severity categories, depending on the hypopnoea scoring criteria used to score their PSGs. There is now growing evidence that characterising OSA by a frequency-oriented metric is not sufficient as a singular assessment parameter for classifying and rating the severity of OSA. The Baveno classification is a valuable multicomponent grading system for OSA severity beyond the AHI and will be discussed in chapter 2.3 of this Handbook, ‘Evaluation of obstructive sleep apnoea severity’. New metrics have been proposed and validated based on advanced signal processing and complex analyses, and appear useful for risk stratification, tailored sleep medicine (or precision medicine) and patient selection for clinical trials. This chapter summarises a number of these emerging alternative metrics that more accurately quantify the respiratory event-specific hypoxaemia, arousal intensity, and autonomic response. The emerging role of genetics and biomarkers will also be highlighted. Event duration The idea of taking apnoea and hypopnoea event duration into account in the severity estimation of OSA was introduced by Kulkas et al. (2013). Its relevance was later confirmed in the Sleep Heart Health Study (SHHS), where an important relationship was observed between the duration of the respiratory events and the overall mortality seen in the SHHS. Short respiratory events could reflect a low arousal threshold, but Key points • Characterising OSA by frequency-oriented metrics is not sufficient for classifying the severity of OSA and risk stratification. • New metrics better quantify the respiratory event-specific hypoxaemia, arousal intensity and autonomic response. • Sophisticated algorithms are mandatory to perform in-depth analyses beyond the AHI. • The applicability of genetics and biomarkers is limited by costs, reproducibility, significant delays for results, and lack of specificity. ERS Handbook: Respiratory Sleep Medicine

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Table 1.  Alternative parameters to the AHI that include information on event duration Parameter

Description

ApDur, s HypDur, s AHDI, %

Single apnoea event duration Single hypopnoea event duration The percentage of sleep time the patient has been in apnoea, hypopnoea and desaturation The total duration of apnoeas as a percentage of total sleep time The total duration of apnoeas The total duration of hypopnoeas as a percentage of total sleep time The total duration of hypopnoeas Alternative names for the total duration of apnoeas and hypopnoeas as a percentage of total sleep time The total duration of apnoeas and hypopnoeas

TAD, % Total apnoea time, s THD, % Total hypopnoea time, s TAHD or AHT or ObsDur, % Total event time, s

AHDI: apnoea–hypopnoea desaturation index; TAD: total apnoea duration; THD: total hypopnoea duration; AHT: apnoea-hypopnoea time; ObsDur: obstruction duration.

subjects with a high loop gain (with unstable ventilatory control) may also terminate respiratory events more quickly than subjects with a low loop gain. However, respiratory event duration has its limitations, since it does not take into account the desaturation depth and degree of hypoxaemia. An overview of the alternative parameters to the AHI that include information on event duration is shown in table 1. Measurement of different oxygen parameters Night-time hypoxaemia Night-time hypoxaemia is a very appealing parameter to explore, as intermittent hypoxia is the hallmark of OSA. Episodes of short intermittent high-frequency hypoxaemia are pivotal in the presence of OSA, while prolonged low-frequency hypoxaemia is seen in chronic pulmonary diseases. The traditional oxygen desaturation index (ODI) represents the average number of desaturation events (also referred to as dips) that drop at least 3% (ODI3) or at least 4% (ODI4), per hour of sleep (or recording time), regardless of their duration and morphology. This does not necessarily mean that oxygen saturation falls below 90%: drops from 95% to 91% are often observed. In the European Sleep Apnoea Database (ESADA), it has been found that the ODI is a better predictor of systemic hypertension in OSA than the AHI. The discrepancy between the AHI and the ODI arises because a hypopnoea can be scored in association with an arousal (without desaturation). Also, small changes in ventilation due to partial airway obstruction may never be observed as changes in the desaturation profile, as a consequence of the non-linear haemoglobin desaturation curve. Moreover, the ODI does not assess the length of oxygen desaturation, nor its depth. Other hypoxaemia metrics are the time spent with peripheral oxygen saturation (SpO2) below 90% (T90 or T90%), the mean oxygen saturation and the nadir of oxygen saturation (SpO2 minimum) (table 2). The 90% threshold is somewhat arbitrary, and a threshold of 88% (T88) has been considered as well. T90 has been shown to be predictive of important outcomes, like platelet aggregation as well as overall mortality. In contrast, in the SAVE (Sleep Apnea cardioVascular Endpoints) trial, the desaturation indices did not show predictive value related to MACE (major adverse cardiac events), a composite end-point frequently used in cardiovascular research. T90 was significantly

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Table 2.  Parameters integrating desaturation Parameter ODI, events·h−1 Mean SpO2, % Minimum SpO2, % T90 or T90% or time 10.1 beats·min−1) is associated with cardiovascular events and all-cause mortality in longitudinal follow-up of the SHHS cohort. The risk associated with a high ΔHR is particularly high in those with a substantial hypoxic burden (≥62%·min·h−1). ΔHR may reflect both the parasympathetic and sympathetic responses to an event. A higher ΔHR may reflect a more pronounced vagally induced bradycardia during an event (larger decrease in heart rate during an event) and a more pronounced sympathetic response to hypoxaemia and hypercapnia (larger increase in heart rate). Such ΔHR relies only on measurement of respiration and pulse oximetry, so is easy to collect with home polygraphy (PG). Cardiopulmonary coupling Cardiopulmonary coupling (CPC) is a technique that generates a sleep spectrogram by calculating the cross-spectral power and coherence of heart rate variability and respiratory tidal volume fluctuations, allowing the dynamic tracking of cardiopulmonary interactions. It can be easily assessed from a continuous, single-lead ECG. CPC has shown that NREM sleep in adults is associated with spontaneous abrupt transitions between high- and low-frequency CPC regimes, with characteristic features in health and disease. Such an approach could be used to gain complementary information to assess sleep stability when scoring NREM sleep stages, while correlations of CPC-derived measures with conventional metrics (such as AHI) have also been reported. Respiratory effort burden: total time of sleep spent with respiratory effort Increased respiratory effort is one of the main features of OSA and is associated with sympathetic overactivity. Available evidence suggests that respiratory effort contributes to increases in nocturnal BP. Recently, a new metric was introduced that automatically incorporates increased respiratory effort derived from measurement of mandibular jaw movements, expressed as a proportion of total sleep time (REMOV %TST). This parameter was identified as a potential new reliable metric to predict prevalent hypertension in patients with OSA. Genetics It has been suggested that genetic factors are likely to play an important role in the individual differences in OSA (minimally symptomatic, those with disrupted sleep and those with EDS). It is only recently that the genetic causes of OSA and its component endotypes are being explored. For example, recent analyses have shown higher heritability indices with respiratory event duration and a multicomponent score comprising six traits (AHI, event duration, two ODIs, snoring and sleepiness). The significant heritability for event duration is of particular interest, given its correlation with an individual’s ventilatory control sensitivity. Overall, this approach is likely to help explain individual differences and susceptibility to the clinical consequences of OSA. ERS Handbook: Respiratory Sleep Medicine

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Biomarkers Panels of biomarkers have the added value of identifying causal pathways affected by OSA, such as systemic inflammation, autonomic dysfunction and oxidative stress. Interleukin (IL)-6 and IL-10 plasma levels have the potential to be good biomarkers in identifying or excluding the presence of OSA in adults, while kallikrein-1, uromodulin, urocortin-3 and orosomucoid-1, when combined, have enough accuracy to be an OSA diagnostic test in children. Along the same lines, high-sensitivity C-reactive protein, glycated haemoglobin (HbA1c), erythropoietin, uric acid, tumour necrosis factor-α, homocysteine and cysteine have been proposed, but none are sensitive and specific enough to predict individual susceptibility to cardiovascular morbidity. Exhaled volatile organic compounds may have value as biomarkers, but again the lack of specificity is a major challenge. Using combinatorial approaches and cut-off values for overnight changes of these biomarkers enables a more reliable prediction of OSA. Such approaches not only yield diagnostic and monitoring opportunities but also could encompass prognostic and predictive information. New developments in this area are the assessment of specific microRNAs related to hypoxia, exosomes, and metabolic, lipidomic, proteomic and gene expression profiles. Also, metabolomics (the study of complete sets of metabolites on cells, tissues and organisms) and proteonomics (the set of technologies applied to explore and evaluate hundreds or thousands of proteins to discover biomarkers) have been explored in OSA and are promising. There are obvious limitations (costs, reproducibility and significant delays for results) in the clinical applicability of these biomarkers for supporting OSA diagnosis. Conclusion Novel parameters, also including genetics and biomarkers, indicate significant differences in severity of OSA in patients having similar AHIs, and could bring new valuable information to support the diagnostics of the severity of OSA. These parameters will also give direction to the concept of different disease subtypes with different endotypes and phenotypes with different predominant pathophysiological features and therapeutic targets.

Further reading • Amatoury J, et al. (2018). New insights into the timing and potential mechanisms of respiratory-induced cortical arousals in obstructive sleep apnea. Sleep; 41: zsy160. • Azarbarzin A, et al. (2019). The hypoxic burden of sleep apnoea predicts cardiovascular disease-related mortality: the Osteoporotic Fractures in Men Study and the Sleep Heart Health Study. Eur Heart J; 40: 1149–1157. • Azarbarzin A, et al. (2021). The sleep apnea-specific pulse-rate response predicts cardiovascular morbidity and mortality. Am J Respir Crit Care Med; 203: 1546–1555. • Bahr K, et al. (2021). Intensity of respiratory cortical arousals is a distinct pathophysiologic feature and is associated with disease severity in obstructive sleep apnea patients. Brain Sci; 11: 282. • Betta M, et al. (2020). Quantifying peripheral sympathetic activations during sleep by means of an automatic method for pulse wave amplitude drop detection. Sleep Med; 69: 220–232. • Blekic N, et al. (2022). Impact of desaturation patterns versus apnea–hypopnea index in the development of cardiovascular comorbidities in obstructive sleep apnea patients. Nat Sci Sleep; 14: 1457–1468.

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• Butler MP, et al. (2019). Apnea–hypopnea event duration predicts mortality in men and women in the Sleep Heart Health Study. Am J Respir Crit Care Med; 199: 903–912. • Gouveris H, et al. (2020). Corticoperipheral neuromuscular disconnection in obstructive sleep apnoea. Brain Commun; 2: fcaa056. • Kulkas A, et al. (2013). Novel parameters indicate significant differences in severity of obstructive sleep apnea with patients having similar apnoea–hypopnoea index. Med Biol Eng Comput; 51: 697–708. • Kwon Y, et al. (2021). Pulse arrival time, a novel sleep cardiovascular marker: the multi-ethnic study of atherosclerosis. Thorax; 76: 1124–1130. • Lebkuchen A, et al. (2021). Advances and challenges in pursuing biomarkers for obstructive sleep apnea: implications for the cardiovascular risk. Trends Cardiovasc Med; 31: 242–249. • Malantis-Ewert S, et al. (2022). A novel quantitative arousal-associated EEG-metric to predict severity of respiratory distress in obstructive sleep apnea patients. Front Physiol; 13: 885270. • Malhotra A, et al. (2021). Metrics of sleep apnea severity: beyond the apnea–hypopnea index. Sleep; 44: zsab030. • Martinot J-B, et al. (2022). Respiratory effort during sleep and prevalent hypertension in obstructive sleep apnoea. Eur Respir J; in press [https://doi.org/10.1183/13993003.014862022]. • Muraja-Murro A, et al. (2012). Total duration of apnea and hypopnea events and average desaturation show significant variation in patients with a similar apnea–hypopnea index. J Med Eng Technol; 36: 393–398. • Strassberger C, et al. (2021). Beyond the AHI – pulse wave analysis during sleep for recognition of cardiovascular risk in sleep apnea patients. J Sleep Res; 30: e13364. • Terrill PI (2020). A review of approaches for analysing obstructive sleep apnoea-related patterns in pulse oximetry data. Respirology; 25: 475–485. • Thomas RJ, et al. (2009). Prevalent hypertension and stroke in the Sleep Heart Health Study: association with an ECG-derived spectrographic marker of cardiopulmonary coupling. Sleep; 32: 897–904. • Tsuji H, et al. (1996). Impact of reduced heart rate variability on risk for cardiac events. The Framingham Heart Study. Circulation; 94: 2850–2855. • Vizzardi E, et al. (2014). Noninvasive assessment of endothelial function: the classic methods and the new peripheral arterial tonometry. J Investig Med; 62: 856–864. • Wang Z, et al. (2023). Heart rate variability changes in patients with obstructive sleep apnea: a systematic review and meta-analysis. J Sleep Res; 32: e13708. • Younes M, et al. (2015). Odds ratio product of sleep EEG as a continuous measure of sleep state. Sleep; 38: 641–654.

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Evaluation of obstructive sleep apnoea severity Dirk Pevernagie, Sophia E. Schiza and Winfried Randerath

OSA is a medical construct rooted in respiratory pathophysiology. The presence of recurrent obstructive respiratory disturbances that are generally associated with arousal from sleep is the basic finding on sleep recordings. SDB events are called “apnoeas” when a complete collapse of the upper airway occurs, whereas “hypopnoeas” denote only partial obstruction. The number of all these respiratory events divided by the total sleep time is the AHI. OSA is a sleep disorder causing symptoms of disrupted nocturnal sleep and impaired functioning during the daytime. As a consequence of several systemic effects, including intermittent hypoxia, subsequent sympathetic activation and sleep fragmentation, OSA is also implicated in metabolic and cardiovascular pathophysiology. OSA may induce organic damage in the long term and may decrease life expectancy in severely affected patients. When OSA is associated with symptoms, signs or comorbidities, the term OSAS is used. Features of OSA suitable for severity grading The AHI is currently used as an identifier for establishing a diagnosis of OSA and as a quantifier for assessing the severity of this disorder. However, the AHI has never been properly validated and is actually a deficient metric. Biomarkers that are better suited for diagnostic and severity grading purposes are needed. Recently, indices of systemic effects have been introduced as new “candidate” markers. Besides markers derived from PSG or body fluids, disease manifestations of OSA are also suitable for severity assessment. Suggestive symptoms of OSA are: complaints of sleep disruption (not being able to initiate or maintain sleep); nonrestorative sleep; snoring; fatigue; excessive sleepiness during the daytime; and cognitive impairment. Key points • The concept of OSA is based on respiratory pathophysiology. • The AHI is a pathophysiological marker used for identification and quantification of this disorder. • The validity of the AHI has been questioned due to poor correlation with clinical outcomes. • New biomarkers for better prediction of OSA severity are being developed.

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OSA may also affect mood, safety and social functioning. Furthermore, OSA may compromise metabolic and cardiovascular health. These characteristics may qualify as relevant variables suitable for severity grading of OSA. However, many of these clinical features lack specificity because they could also be caused by other conditions. Uncertainty about causal relationships between pathophysiological variables and disease manifestations is a limitation of the current OSA disease model. Subjective manifestations of OSA are patient-reported outcomes, which are assessed using questionnaires. When employed properly, patient-reported outcome measures (PROMs) may reliably represent disease severity and can be used to assess therapeutic outcome. Generic sleep questionnaires can be applied to gauge severity of OSArelated symptoms. The Pittsburgh Sleep Quality Index (PSQI) and the ESS are the best known and most commonly used assessment tools. Furthermore, specific OSA questionnaires have been made available to rate disease severity and to evaluate improvement under treatment. The Patient-Reported Apnea Questionnaire (PRAQ) is a recently published, well-validated instrument. Meanwhile, models for integrating pathophysiological markers and disease manifestations have been proposed. These models may contribute to a more holistic, integrative approach to OSA. Again, resolving the specificity issues is mandatory to assure the validity of these new constructs. Assessment of OSA severity derived from sleep study parameters The AHI Based on the observation that the number of apnoeas and hypopnoeas per hour of sleep is elevated in patients with symptomatic OSA, the AHI has been used as the primary predictor of the disorder. By expert consensus, the following parameters were proposed for severity determination (American Academy of Sleep Medicine, 1999): • • • •

AHI 70% of OSA patients are obese (Deegan et al., 1996). When BMI exceeds 25 kg·m−2, sensitivity is 93% and specificity is 74% for developing OSA (Grunstein et al., 1993). Moreover, a 10% weight gain causes a six-fold increase in the risk of developing an AHI of ≥15 events·h−1 (Peppard et al., 2000). A 10% weight gain also induces an approximate AHI increase

Key points • Clinically relevant OSA has an estimated prevalence of at least 4% in males and 2% in females. • Obesity has a very strong relationship with AHI. • CSR has a male predominance. • The prevalence of CSA due to a medical disorder without CSR is limited. • OHS occurs in a minority of patients who are severely overweight.

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of 32%; however, in contrast, a 10% weight loss can cause the AHI to drop by 26%. More recently, the association between central obesity and OSA was assessed and a strong correlation between visceral fat and OSA was noted (Vgontzas et al., 2003). Interestingly, more men than women suffer from OSA (AHI ≥5 events·h−1), at a ratio of 2:1. Recent studies have shown a less pronounced difference in the male:female ratio, at approximately 1.5:1 (Heinzer et al., 2015, and Donovan et al., 2016). The higher prevalence among men could be explained by androgens that stimulate upper airway collapse, whereas progesterone would have a more protective effect. This also explains the increase in prevalence amongst women after menopausal age (Jordan et al., 2003). OSA prevalence also increases with age. An increase is observed until the seventh decade of life, followed by a plateau (Young et al., 2002). Daytime symptoms seem to be less frequent with more advanced age. CSA Most information regarding the epidemiology of CSA comes from clinical populations with a number of predisposing conditions, such as CHF, stroke and opioid use. Such categories are subject to referral pattern bias. In one large, non-clinic-based cohort, CSA has been found to be 53 times less common than OSA (0.9% versus 47.6%) (Donovan et al., 2016), with a prevalence of 1.8% in men and 0.2% in women. Approximately half of the CSA cases were associated with CSR (0.4% overall). In older studies, the prevalence of CSA was noted to be 0.4% overall, but a higher cut-off value of 10 had been employed (Bixler et al., 1998). The prevalence of CSA with CSR CSR is generally seen in those >60 years of age. Its prevalence in a CHF setting has been reported to be 25–50%, with a male predominance. After stroke, ≤70% of patients presents with CSR. These patients often present with OSA but CSA is also common, especially in the acute phase following stroke (Johnson et al., 2010). The risk factors for CSR (in CHF) are male sex, age >60 years, the presence of atrial fibrillation (AF) and daytime hypocapnia (Javaheri et al., 2013). In general, a more pronounced lung congestion predicts a lower PaCO2. Some studies have reported that CSR occurs more often in the supine position, and is related to alterations in the FRC. CSA due to a medical disorder without CSR Due to the heterogeneous aetiology of this type of CSA, prevalence data vary according to the underlying aetiology (Randerath et al., 2017). Acromegaly The prevalence of CSA is usually low in patients with acromegaly and is related to disease activity. Some studies have shown a CSA prevalence of nearly 32%, but it is assumed that up to 10% of patients with acromegaly suffer from relevant CSA. The central apnoea index (CAI) appears to be linked to the production of growth hormone (GH), whereas elevated ventilatory responses are associated with GH and insulin-like growth factor-1 levels. Medical and surgical treatment of acromegaly can reduce OSA; there is a lack of control studies examining the effects of treatment on CSA. Type 2 diabetes mellitus OSA is the dominant type of SDB in patients with type 2 diabetes mellitus (DM). However, the Sleep Heart Health Study reported a small but nonsignificant increase in the number of central apnoeas in patients with DM. Moreover, specific analysis ERS Handbook: Respiratory Sleep Medicine

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of periodic breathing (PB) showed a significant rise in patients with obvious DM. Pharyngeal neuropathy can be a contributing factor but no obvious association has been established between CSA and autonomous dysfunction. End-stage renal disease OSA frequently occurs in end-stage renal disease (ESRD). Tada et al. (2007) reported 30 (38%) patients with sleep apnoea in a group of 78 patients being treated with haemodialysis. The average CAI was 4 events·h−1, and CSA made up 8% of all respiratory events. Eight of the 30 patients showed a CAI of ≥5 events·h−1 and were classified as having CSA. Interestingly, the prevalence of CSA depends on the dialysis procedure and the buffers used: bicarbonate was associated with significantly fewer CSAs compared with acetate buffer, despite similar blood gases. Fluid overload and shift over the course of the night is considered to be the underlying mechanism for a high prevalence of OSA in ESRD. A rise in ventilatory sensitivity and a destabilised respiratory control system also contribute to an increased prevalence of SDB in ESRD, as do comorbid AF and cardiac dysfunction, which points to synergistic effects on the development of sleep apnoeas. Pulmonary hypertension Pulmonary hypertension (PH) is a pathophysiological and haemodynamic condition that is related to multiple clinical afflictions. CSR and CSA are common in CHF, although there are very limited data available on capillary PH. 0–45% of patients with PH have central respiratory events versus a prevalence of 0–56% for OSA. OSA is predominant in chronic thromboembolic and COPD-associated PH, whereas CSA is seen mainly in idiopathic or chronic thromboembolic PH. Interstitial lung diseases In interstitial lung diseases (ILDs), dyspnoea, nocturnal coughing, medication side-effects, periodic limb movements, hypoxaemia, OSA, depression and fatigue encumber sleep. The role of CSA has not been thoroughly investigated. Neurodegenerative diseases Several studies have reported on the prevalence of OSA in patients with different types of dementia. There are no convincing data regarding a higher prevalence of CSA with Alzheimer disease. The prevalence of sleep apnoea in Parkinson disease varies 21–67%, and most studies included low numbers of patients (15–100). OSA is the dominant type of sleep apnoea, while only a limited number of cases of CSA have been reported. Central apnoea at high altitude In healthy subjects, hypobaric hypoxaemia at an altitude of >2000 m can lead to CSA and PB. Generally, the percentage of individuals exhibiting PB during sleep increases progressively at higher altitudes. Usually, 25% exhibit PB at 2500 m, and almost 100% demonstrate PB at 4000 m. As men have higher chemoresponsiveness than women, high-altitude PB is more common in men than in women. CSA due to medication or substance abuse CSA induced by chronic opioid use is described in about 30% of subjects who use methadone to treat heroin addiction (Wang et al., 2005). So far, there are no known sex, race or ethnicity differences. Use of potent long-acting opioids is the most important causal factor, but the effect is dose-dependent. This also indicates that if the drug is successfully tapered down, CSA may resolve or improve.

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Primary CSA The prevalence of primary CSA is much lower than that of OSA. Based on the Sleep Heart Health Study, 0.5% of the general population has primary CSA, 1% in men and 0.1% in women (Donovan et al., 2016). In a clinical cohort, it was estimated that ∼4% of patients who were referred to a sleep clinic with suspicion of SDB predominantly displayed CSA (De Backer et al., 1995). CSA affects healthy individuals physiologically, especially old people and young children. Generally speaking, CSA occurs at an advanced age (Bixler et al., 1998). A high hypercapnic ventilatory response (HCVR) seems to be a major predisposing factor in the development of primary CSA. Primary CSA of infancy and prematurity For more information on these subcategories, the reader is referred to chapter 17, which discusses paediatric respiratory sleep medicine. Treatment-emergent CSA Treatment-emergent CSA (TECSA) affects 2–15% of patients with classic OSA. With ongoing CPAP therapy, a spontaneous decrease in the number of central apnoeas or even their disappearance is expected over time once PAP is well established (Cassel et al., 2011). Hypoventilation syndromes OHS This syndrome occurs in a minority of patients who are severely overweight. Clinically relevant OSA affects 80–90% of patients with OHS. Conversely, OHS is described in 9–11% of patients with OSA. Prevalence estimates for OHS in the general population are unknown but may be estimated using obesity and OSA prevalence data (∼0.4%, or approximately one in 260 people in the US adult population, though this maybe lower in countries with a reduced prevalence of obesity) (Masa et al., 2019). The prevalence of OHS is higher in men than women, but the difference is not as pronounced as in OSA. In one clinical cohort it was reported that the prevalence of OHS is similar in men and women (Palm et al., 2016). Higher levels of obesity are usually associated with more severe sleep-related hypoventilation but individual variations can also play a role (Verbraecken et al., 2013). Use of central nervous system (CNS) depressants may aggravate respiratory impairment further. Congenital central hypoventilation syndrome For further information on this topic, the interested reader is referred to chapter 17 on paediatric respiratory sleep medicine. Late-onset central hypoventilation with hypothalamic dysfunction For further information on this topic, the interested reader is referred to chapter 17 on paediatric respiratory sleep medicine. Idiopathic central alveolar hypoventilation Patients with this diagnosis may have an underlying functional defect affecting respiratory drive and mechanics, which remains undiagnosed. No data are available on its prevalence. The use of CNS depressants (anxiolytics, hypnotics, neuroleptics and alcohol) may further worsen hypercapnia. ERS Handbook: Respiratory Sleep Medicine

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Sleep-related hypoventilation due to medication or substance abuse The prevalence of sleep-related hypoventilation due to the use of respiratory depressants is not known. It is obvious that baseline hypoventilation will worsen following initiation of the medication. The use of medication or a substance that impairs respiratory drive or mechanics is the primary risk factor responsible for hypoventilation, but significant inter-individual differences in sensitivity and tolerance to respiratory depressants do exist. Sleep-related hypoventilation due to a medical disorder The prevalence of sleep-related hypoventilation due to a medical disorder is a function of the underlying condition’s prevalence, degree of severity and clinical characteristics. No thresholds are set for pulmonary parenchymal or vascular disease severity to predict the risk of sleep-related hypoventilation in individual subjects. A reduced HCVR and the use of CNS depressants may further worsen respiratory impairment. Further reading • Benjafield AV, et al. (2019). Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med; 7: 687–698. • Bixler EO, et al. (1998). Effects of age on sleep apnea in men: I. Prevalence and severity. Am J Respir Crit Care Med; 157: 144–148. • Cassel W, et al. (2011). A prospective polysomnographic study on the evolution of complex sleep apnoea. Eur Respir J; 38: 329–337. • De Backer WA, et al. (1995). Central apnea index decreases after prolonged treatment with acetazolamide. Am J Respir Crit Care Med; 151: 87–91. • Deegan PC, et al. (1996). Predictive value of clinical features of the obstructive sleep apnoea syndrome. Eur Respir J; 9: 117–124. • Donovan LM, et al. (2016). Prevalence and characteristics of central compared to obstructive sleep apnea: analyses from the Sleep Heart Health Study Cohort. Sleep; 39: 1353–1359. • Grunstein R, et al. (1993). Snoring and sleep apnoea in men: association with central obesity and hypertension. Int J Obes; 17: 533–540. • Heinzer R, et al. (2015). Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study. Lancet Respir Med; 3: 310–318. • Javaheri S, et al. (2013). Central sleep apnea. Compr Physiol; 3: 141–163. • Johnson KG, et al. (2010). Frequency of sleep apnea in stroke and TIA patients: a meta-analysis. J Clin Sleep Med; 6: 131–137. • Jordan AS, et al. (2003). Gender differences in sleep apnea: epidemiology, clinical presentation and pathogenic mechanisms. Sleep Med Rev; 7: 377–389. • Masa JF, et al. (2019). Obesity hypoventilation syndrome. Eur Respir Rev; 28: 180097. • Palm A, et al. (2016). Gender differences in patients starting long-term home mechanical ventilation due to obesity hypoventilation syndrome. Respir Med; 110: 73–78. • Peppard PE, et al. (2000). Longitudinal study of moderate weight change and sleep disorderedbreathing. J Am Med Assoc; 284; 3015–3021. • Peppard PE, et al. (2013). Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol; 177: 1006–1014.

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• Randerath W, et al. (2017). Definition, discrimination, diagnosis and treatment of central breathing disturbances during sleep. Eur Respir J; 49: 1600959. • Tada T, et al. (2007). The predictors of central and obstructive sleep apnoea in haemodialysis patients. Nephrol Dial Transplant; 22: 1190–1197. • Verbraecken J, et al. (2013). Respiratory mechanics and ventilatory control in overlap syndrome and obesity hypoventilation. Respir Res; 14: 132. • Vgontzas AN, et al. (2003). Metabolic disturbances in obesity versus sleep apnoea: the importance of visceral obesity and insulin resistance. J Intern Med; 254: 32–44. • Wang D, et al. (2005). Central sleep apnea in stable methadone maintenance treatment patients. Chest; 128: 1348–1356. • Young T, et al. (1993). The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med; 328: 1230–1235. • Young T, et al. (2002). Predictors of sleep disordered breathing in community-dwelling adults: the Sleep Heart Health Study. Arch Intern Med; 162: 893–900.

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The pathophysiological concept of upper airway obstruction, the arousal threshold, muscle responsiveness and respiratory drive Ludovico Messineo, Luigi Taranto-Montemurro and Elisa Perger

The repetitive collapse of the pharyngeal airway, characteristic of OSA leads to intermittent oxygen desaturations and sleep fragmentation and downstream consequences. Intermittent hypoxaemia activates the sympathetic nervous system, and it is the major trigger for cardiovascular and metabolic adverse consequences. The occurrence of upper airway obstruction during sleep reflects an interplay between the removal of the wakefulness drive (which helps to maintain airway patency) and an individual susceptibility to collapse. Although individual risk factors are known, the precise pathophysiological pathways leading to upper airway obstruction and their reciprocal influence in patients with OSA need further investigation. Research in the past decade has established that a number of key pathophysiological traits – or endotypes – contribute to the development of OSA (figure 1). These include not only an anatomically small, collapsible upper airway (high passive critical closing pressure of the upper airway (Pcrit)), but also inadequate responsiveness of the upperairway dilator muscles during sleep (minimal increase in muscle activity to negative pharyngeal pressure), waking up prematurely to airway narrowing (a low respiratory arousal threshold) and having an oversensitive respiratory control system (high loop gain). It is likely that other factors such as end-expiratory lung volume, arousal intensity and redistribution of body fluid are also important. Pharyngeal muscle relaxation during sleep and lack of sufficient reactivation are key primary pathophysiological events leading to OSA. The reduced ventilation consequent to an obstructive event increases carbon dioxide (CO2) and ventilatory drive, which could Key points • OSA is a heterogeneous disease with highly varying underlying mechanisms. • Four pathophysiological traits have been identified recently as responsible for OSA aetiology: predisposing anatomically small and collapsible upper airway; inadequate responsiveness of the upper-airway dilator muscles during sleep; a low respiratory arousal threshold; and an oversensitive respiratory control system (high loop gain). • Identifying the mechanism underlying OSA for an individual will permit a personalised therapy to be designed based on the specific characteristics of the subject.

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The pathophysiological concept of upper airway obstruction

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