Diagnosis and Treatment
Antonio Culebras, M.D.
Professor of Neurology
Upstate Medical University
Consultant, The Sleep Center
Community General Hospital
Syracuse, New York, U.S.A.
Anna Ivanenko, M.D., Ph.D.
Loyola University Medical Center
Department of Psychiatry and Behavioral Neuroscience
Maywood, Illinois, U.S.A.
Clete A. Kushida, M.D., Ph.D., RPSGT
Director, Stanford Center for Human Sleep Research
Associate Professor, Stanford University Medical Center
Stanford University Center of Excellence for Sleep Disorders
Stanford, California, U.S.A.
Nathaniel F. Watson, M.D.
University of Washington Sleep Disorders Center
Harborview Medical Center
Seattle, Washington, U.S.A.
1. Clinician’s Guide to Pediatric Sleep Disorders, edited by
Mark A. Richardson and Norman R. Friedman
2. Sleep Disorders and Neurologic Diseases, Second Edition,
edited by Antonio Culebras
3. Obstructive Sleep Apnea: Pathophysiology, Comorbidities, and
Consequences, edited by Clete A. Kushida
4. Obstructive Sleep Apnea: Diagnosis and Treatment,
edited by Clete A. Kushida
Clete A. Kushida
Stanford, California, USA
Diagnosis and Treatment
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Library of Congress Cataloging-in-Publication Data
Obstructive sleep apnea: Diagnosis and treatment / edited
by Clete A. Kushida.
p. ; cm. -- (Sleep disorders ; 4)
Includes bibliographical references and index.
ISBN-13: 978-0-8493-9182-8 (hb : alk. paper)
ISBN-10: 0-8493-9182-2 (hb : alk. paper) 1. Sleep apnea syndromes. I. Kushida,
Clete Anthony, 1960- II. Title: Diagnosis and treatment.
III. Series: Sleep disorders (New York, N.Y.) ; 4.
[DNLM: 1. Sleep Apnea, Obstructive--diagnosis. 2. Sleep Apnea,
Obstructive--therapy. WF 143 O139 2007]
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“When in doubt, pressurize the snout.”
—attributal to Philip R. Westbrook
I often thought of this mantra during my on-call nights when, as a Stanford sleep
medicine fellow, I was awakened from sleep by a technologist informing me that
one of the clinic patients had repetitive obstructive apneas with significant oxygen
desaturations. The technologist would typically ask, “can I start the patient on CPAP?”
Invariably, I would mutter a drowsy “yes,” often chiding myself that on the previous
day I should have clearly written the respiratory thresholds for starting continuous
positive airway pressure on the patient’s sleep-study order sheet. This anecdote
illustrates the fact that continuous positive airway pressure has become such an
important and ubiquitous treatment for obstructive sleep apnea since its develop-
ment over a quarter century ago. The modern sleep specialist has new diagnostic
tools and other treatments, such as upper airway surgery and oral appliances, for
patients with obstructive sleep apnea; nevertheless, our field is still in its adole-
scence with respect to the diagnosis and treatment of obstructive sleep apnea and
other sleep disorders.
The reader might wonder why a neurologist is editing a two-volume set of
books on obstructive sleep apnea, since it is a sleep-related breathing disorder and
would therefore appear to be within the domain of pulmonary physicians. However,
besides pulmonologists—neurologists, psychiatrists, internists, pediatricians, and
otolaryngologists have entered the field of sleep medicine. Many clinicians now
treat patients with sleep disorders on a full-time basis. Sleep medicine has truly
become multidisciplinary, and a sleep clinician is expected to diagnose and treat a
wide range of sleep disorders, from insomnia to restless legs syndrome, that were
previously referred by internists to other specialists.
It is indeed a testament to the ever-increasing knowledge base on obstructive
sleep apnea that there is a need for a two-volume set of books on this topic. The first
volume, Obstructive Sleep Apnea: Pathophysiology, Comorbidities, and Consequences
covers the pathophysiology, comorbidities, and consequences of obstructive sleep
apnea, with sections exploring the features, factors, and characteristics of this disor-
der as well as its associations and consequences. This volume focuses on the diag-
nosis and treatment of obstructive sleep apnea, and includes a section on special
conditions, disorders, and clinical issues. The authors and I have tried to conform
the conditions and disorders described in this book to the second edition of the
International Classification of Sleep Disorders: Diagnostic & Coding Manual published
by the American Academy of Sleep Medicine in 2006, although some terms, such as
obstructive sleep apnea syndrome and sleep-disordered breathing, have been
retained in a few statements when appropriate. We have also tried to discuss new
entities and findings such as complex sleep apnea, oxidative stress, cyclic alternat-
ing pattern, and adaptive servo-ventilation. However, given the rapidity with which
the area of sleep medicine is advancing, it is highly conceivable that two volumes
might not be sufficient to cover the topic of obstructive sleep apnea in just a few
These books could not exist without the excellent contributions of a talented
group of international authors; their detailed and comprehensive works are greatly
appreciated. I am deeply indebted to the renowned and true pioneers of our field of
sleep, William Dement, Christian Guilleminault, Sonia Ancoli-Israel, Chris Gillin,
and Allan Rechtschaffen, who served as my mentors through various stages of my
career. In all of my endeavors, I can always count on my parents, Samiko and Hiroshi
Kushida, to assist me; these books were no exception. I have been very fortunate to
serve, along with Dr. Dement, as Principal Investigator of the multicenter, rand-
omized, double-blind, placebo-controlled Apnea Positive Pressure Long-Term
Efficacy Study, sponsored by the National Heart, Lung, and Blood Institute of the
National Institutes of Health. To date, this is the largest controlled trial funded by
the National Institutes of Health in the field of sleep.
This book is dedicated not only to my parents but also to the marvelous core
team of the Apnea Positive Pressure Long-Term Efficacy Study, consisting of William
Dement, Pamela Hyde, Deborah Nichols, Eileen Leary, Tyson Holmes, Dan Bloch,
as well as National Heart, Lung, and Blood Institute officials (Michael Twery and
Gail Weinmann), site directors, coordinators, consultants, committee members, key
Stanford site personnel (Chia-Yu Cardell, Rhonda Wong, Pete Silva, Jennifer Blair),
Data and Safety Monitoring Board members, and other personnel without whom
this project could not have functioned in such a meticulous and efficient manner.
It is my sincere hope that the reader will strive to become expert in the field of
sleep. Although there is always room for improvement, awareness of sleep disor-
ders by patients, physicians, and the general public is at an all-time high. However,
available funding for sleep research and the number of young investigators inter-
ested in a career in basic or clinical sleep research are areas that need enhancement.
The interested reader can directly contribute to this field in several ways: applying
for membership in the American Academy of Sleep Medicine or Sleep Research
Society, serving on committees in these organizations, becoming board certified in
sleep medicine, submitting a sleep-related grant proposal to the National Institutes
of Health, and/or just simply learning more about sleep and its disorders.
Lastly, etched forever in my memory is a sticker posted on the door of Mary
Carskadon’s former office at Stanford that contained words to live by: “Be alert. The
world needs more lerts.”
Clete A. Kushida
Preface . . . . iii
Contributors . . . . vii
SECTION I: DIAGNOSIS
1. History and Physical Examination 1
Rory Ramsey, Amit Khanna, and Kingman P. Strohl
2. Screening and Case Finding 21
Charles F. P. George
3. Polysomnography and Cardiorespiratory Monitoring 35
Michael R. Littner
4. Upper Airway Imaging 61
Nirav P. Patel and Richard J. Schwab
5. Alertness and Sleepiness Assessment 89
SECTION II: TREATMENT
6. Continuous Positive Airway Pressure 101
Peter R. Buchanan and Ronald R. Grunstein
7. Bilevel Pressure and Adaptive Servo-Ventilation for Obstructive
and Complex Sleep Apnea 125
Peter C. Gay
8. Auto-Positive Airway Pressure 137
Richard B. Berry
9. Critical Factors in Positive Pressure Therapy 151
Scott M. Leibowitz and Mark S. Aloia
10. Noninvasive Positive Ventilation 173
Dominique Robert and Laurent Argaud
11. Upper Airway Surgery in the Adult 191
Donald M. Sesso, Nelson B. Powell, Robert W. Riley, and Jerome E. Hester
12. Oral Appliances 217
Peter A. Cistulli and M. Ali Darendeliler
13. Adjunctive and Alternative Therapies 233
Alan T. Mulgrew, Krista Sigurdson, and Najib T. Ayas
SECTION III: SPECIAL CONDITIONS, DISORDERS, AND CLINICAL ISSUES
14. Gender Differences in Obstructive Sleep Apnea 247
Vidya Krishnan and Nancy A. Collop
15. Obstructive Sleep Apnea in Children 261
Rafael Pelayo and Kasey K. Li
16. Obstructive Sleep Apnea in the Elderly 281
Lavinia Fiorentino and Sonia Ancoli-Israel
17. Medication Effects 295
Julie A. Dopheide
18. Snoring and Upper Airway Resistance Syndrome 305
Riccardo A. Stoohs and Antoine Aschmann
19. Central Sleep Apnea 321
M. Safwan Badr
20. Other Respiratory Conditions and Disorders 333
21. Other Sleep Disorders 347
Meeta H. Bhatt and Sudhansu Chokroverty
22. Neurological Disorders 367
Maha Alattar and Bradley V. Vaughn
23. Medical Disorders 389
Robert D. Ballard
24. Legal Implications of Obstructive Sleep Apnea 405
Daniel B. Brown
25. A Concluding Note and Future Directions 425
William C. Dement
Index . . . . 427
Maha Alattar Department of Neurology, University of North Carolina, Chapel Hill,
North Carolina, U.S.A.
Mark S. Aloia Butler Hospital, Providence, Rhode Island, U.S.A.
Sonia Ancoli-Israel Department of Psychiatry, University of California, San Diego
and Veterans Affairs San Diego Healthcare System, San Diego, California, U.S.A.
Laurent Argaud Emergency and Intensive Care Department, Edouard Herriot
Hospital, Lyon, France
Antoine Aschmann Medica Surgical Private Clinics, Mülheim, Germany
Najib T. Ayas Sleep Disorders Program and Respiratory Division, University of
British Columbia, Vancouver, British Columbia, Canada
M. Safwan Badr Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine,
Wayne State University School of Medicine, Detroit, Michigan, U.S.A.
Robert D. Ballard Advanced Center for Sleep Medicine, Presbyterian/St. Luke’s
Medical Center, Denver, Colorado, U.S.A.
Richard B. Berry Division of Pulmonary, Critical Care, and Sleep Medicine,
University of Florida, Gainesville, Florida, U.S.A.
Meeta H. Bhatt New York University School of Medicine and New York Sleep
Institute, New York, New York, U.S.A.
Daniel B. Brown Greenberg Traurig, LLP, Atlanta, Georgia, U.S.A.
Peter R. Buchanan Royal Prince Alfred Hospital, Woolcock Institute of Medical
Research and The University of Sydney, Sydney, Australia
Sudhansu Chokroverty Department of Neurology, NJ Neuroscience Institute at JFK
Medical Center, Edison, New Jersey and Seton Hall University, South Orange,
New Jersey, U.S.A.
Peter A. Cistulli Centre for Sleep Health & Research, Royal North Shore Hospital and
The University of Sydney and Woolcock Institute of Medical Research, Sydney,
Nancy A. Collop Division of Pulmonary/Critical Care Medicine, Johns Hopkins
University, Baltimore, Maryland, U.S.A.
M. Ali Darendeliler Discipline of Orthodontics, Faculty of Dentistry, The University
of Sydney and Department of Orthodontics, Sydney Dental Hospital, Sydney, Australia
William C. Dement Stanford Sleep Research Center, Palo Alto, California, U.S.A.
Julie A. Dopheide Schools of Pharmacy and Medicine, University of Southern
California, Los Angeles, California, U.S.A.
Francesco Fanfulla Centro di Medicina del Sonno ad indirizzo cardio-respiratorio,
Istituto Scientifico di Montescano IRCCS, Fondazione Salvatore Maugeri, Montescano
Lavinia Fiorentino Department of Psychiatry, University of California, San Diego
and Veterans Affairs San Diego Healthcare System, San Diego, California, U.S.A.
Peter C. Gay Pulmonary, Critical Care, and Sleep Medicine, Mayo Clinic College
of Medicine, Rochester, Minnesota, U.S.A.
Charles F. P. George University of Western Ontario, London Health Sciences
Centre, London, Ontario, Canada
Ronald R. Grunstein Royal Prince Alfred Hospital, Woolcock Institute of Medical
Research and The University of Sydney, Sydney, Australia
Jerome E. Hester Department of Otolaryngology/Head and Neck Surgery,
Stanford University Medical Center, Palo Alto, California, U.S.A.
Max Hirshkowitz Sleep Center, VA Medical Center and Departments of Medicine
and Psychiatry, Baylor College of Medicine, Houston, Texas, U.S.A.
Amit Khanna Department of Family Medicine, Case Western Reserve University,
Cleveland, Ohio, U.S.A.
Vidya Krishnan Division of Pulmonary/Critical Care Medicine, Johns Hopkins
University, Baltimore, Maryland, U.S.A.
Scott M. Leibowitz The Sleep Disorders Center of Cardiac Disease Specialists,
Atlanta, Georgia, U.S.A.
Kasey K. Li Sleep Apnea Surgery Center, East Palo Alto, California, U.S.A.
Michael R. Littner VA Greater Los Angeles Healthcare System, Sulpulveda,
California and David Geffen School of Medicine, University of California, Los Angeles,
Alan T. Mulgrew Sleep Disorders Program and Respiratory Division, University of
British Columbia, Vancouver, British Columbia, Canada
Nirav P. Patel Division of Pulmonary, Critical Care, and Allergy, Center for
Sleep & Respiratory Neurobiology, University of Pennsylvania Medical Center,
Philadelphia, Pennsylvania, U.S.A.
Rafael Pelayo Stanford University Center of Excellence for Sleep Disorders,
Stanford, California, U.S.A.
Nelson B. Powell Department of Otolaryngology/Head and Neck Surgery,
Stanford University Medical Center and Division of Sleep Medicine, Department of
Behavioral Sciences, Stanford School of Medicine, Palo Alto, California, U.S.A.
Rory Ramsey Division of Pulmonary and Critical Care Medicine, Department of
Medicine, Case Western Reserve University, Cleveland, Ohio, U.S.A.
Robert W. Riley Department of Otolaryngology/Head and Neck Surgery, Stanford
University Medical Center and Division of Sleep Medicine, Department of Behavioral
Sciences, Stanford School of Medicine, Palo Alto, California, U.S.A.
Dominique Robert University Claude Bernard and Edouard Herriot Hospital,
Richard J. Schwab Division of Sleep Medicine, Pulmonary, Allergy, and Critical Care
Division; University of Pennsylvania Medical Center, Philadelphia, Pennsylvania, U.S.A.
Donald M. Sesso Department of Otolaryngology/Head and Neck Surgery,
Stanford University Medical Center, Palo Alto, California, U.S.A.
Krista Sigurdson Sleep Disorders Program and Respiratory Division, University of
British Columbia, Vancouver, British Columbia, Canada
Riccardo A. Stoohs Somnolab Sleep Disorders Centers—Dortmund, Essen,
Kingman P. Strohl Division of Pulmonary and Critical Care Medicine, Department
of Medicine, Case Western Reserve University, Cleveland, Ohio, U.S.A.
Bradley V. Vaughn Department of Neurology, University of North Carolina, Chapel
Hill, North Carolina, U.S.A.
History and Physical Examination
Division of Pulmonary and Critical Care Medicine, Department of Medicine,
Case Western Reserve University, Cleveland, Ohio, U.S.A.
Department of Family Medicine, Case Western Reserve University, Cleveland,
Kingman P. Strohl
Division of Pulmonary and Critical Care Medicine, Department of Medicine,
Case Western Reserve University, Cleveland, Ohio, U.S.A.
The patient-physician encounter is an inquiry designed to disclose and test disease
hypotheses. Physicians detect “cues” early in a patient interview and use them to
generate predictions about a disease presence or state. They then ask questions to
test the likelihood of hypothesized disease(s) and answers modify perceived proba-
bility. This process continues until a reasonable list of potential problems, a differential
diagnosis, is shaped and decisions become more explicit. Physicians rely on their
own experience, skill, and knowledge base to assign values to the presence or
absence of key clinical features.
The purpose of this chapter is to identify those features in sleep history taking
that are more likely to assign diagnostic value. The chapter will start by outlining
some of the cues that physicians use to direct a general sleep history and then detail
the contribution of other elements important in the consideration of obstructive
sleep apnea (OSA). A sleep-specific physical examination will then be discussed.
Adult and pediatric issues will be compared.
It is important to recognize that there are major limitations in the current
literature. To a considerable extent, the recommendations in this chapter result from
data from uncontrolled studies, case series, consensus guidelines, practice para-
meters, and other less rigorous forms of evidence like expert opinion; most of this
literature is not focused on how a history and physical help in patient management
or outcome. There is an enormous heterogeneity in study design, quality and in
populations studied, so that a concordance among studies on the history and physi-
cal is difficult to discuss at more than a superficial level. Finally, clinical studies
express associations in terms that are not always interchangeable, for example,
relative risk (RR), odds ratio (OR), correlation coefficient (r), positive predictive
value, likelihood ratio (LR), sensitivity, specificity, and so on. These features led to
challenges in producing this review.
Yet, there is value still present in a general medical examination. No features
or combination of features are ever fully sensitive and specific for sleep apnea or for
sleep problems. A physician–patient encounter should do more than just capture a
single suspected diagnosis. The process may involve not only a sleep outcome, but
Section I: Diagnosis
2 Ramsey et al.
can disclose comorbid conditions and personal issues that could optimize testing
Excessive Daytime Sleepiness
Excessive daytime sleepiness (EDS) is very common. Epidemiological studies
estimate the prevalence to be 8% to 30% in the general population (1–3) depending
on the definition of sleepiness and the population sampled. A primary care clinic-
based study found that 38.8% experienced wake time tiredness or fatigue at least
three to four times per week (4) and a study of sleep clinic patients found 50%
complaining of excess sleepiness (5).
EDS is a challenging symptom because of a significant overlap with features
of fatigue and the difficulty patients and physicians have in differentiating between
the two symptoms. Sleepiness is the tendency to fall asleep, whereas fatigue involves
a task context with musculoskeletal and/or neurasthenic qualities. For instance, one
author has described fatigue as a drive for rest, and daytime sleepiness as a drive for
sleep (6). Sleepiness may occur more often during “passive” conditions, such as
watching television or sitting as a passenger in a car; but in its most severe form,
sleepiness can intrude into “active” conditions, such as talking to someone or
driving a car.
Several instruments have been developed to identify and measure symptoms
of EDS (7). Currently, the most useful instrument may be the Epworth sleepiness
scale (8). It is a list of eight questions that measure the propensity to sleep in familiar
situations and has good test–retest reliability (9).
EDS is not particularly useful in directing one towards the consideration of
any one disease. It is, however, very important in the quantification of the problem
of sleepiness and should be described in regard to onset, situation, and chronicity.
Specifics about functional sleepiness are probably the most useful. It is also important
to note that people often underestimate their sleepiness (10). While the differential
diagnosis of EDS is wide, sleep-related disorders should be considered early on.
Snoring (See Also Chapter 18)
Community-based studies have estimated that as many 30% to 40% of the general
population (2,3,11) snores and others have shown that more than 50% of primary care
populations snore (4). Studies from community, primary care, and sleep clinics have
all shown a significant relationship between the presence of snoring and sleep apnea
(2,4,12–15), and some have incorporated snoring into clinical prediction rules for
sleep apnea (4,12–14). While snoring is clearly helpful in suggesting sleep apnea as
a diagnostic possibility, its high prevalence in widespread populations impairs its
ability to identify real disease. Nevertheless, this symptom should be described in
regard to onset, severity, frequency, quality, and chronicity.
Witnessed Apneas/Bed Partner’s Reports of Choking or Gasping
Polls have estimated that the prevalence of witnessed apneas in the general adult
population ranges from 3.8% to 6% (2,11,16). A primary care population survey
found that 11.1% of responders had observed breathing pauses at least one to two
times per month (4). Community-, primary care-, and sleep clinic-based studies
have all demonstrated a strong relationship between sleep apnea and witnessed
apneas or nocturnal choking and several have incorporated these reports into
multivariate prediction models for sleep apnea (12,14,17). One community-based
History and Physical Examination
study found that the report of apnea increased the odds of sleep apnea nine-fold (2)
and another sleep clinic-based study found that it increased the odds 19-fold (18). In
the Sleep Heart Health Study, witnessed apneas had the highest prevalence of all
collected demographic and predictor variables in patients with an apnea–hypopnea
index (AHI) ≥ 15 (15). However, the utility of this report is limited by a low sensitivity
as only 9% of these sleep apnea subjects reported this symptom (15). Two additional
limitations include the fact that not everyone has a bed partner and people may be
poor reporters when it comes to describing respiratory events at night (19).
Insomnia is a repeated difficulty with sleep initiation, duration, consolidation, or
quality that occurs despite adequate time and opportunity for sleep and results in
some form of daytime impairment (20). Collectively, insomnia encompasses:
adequate sleep opportunity, a persistent sleep difficulty, and associated daytime
dysfunction. Among adults, insomnia typically manifests as difficulty initiating sleep,
maintaining sleep, waking up too early, or sleep that is chronically nonrestorative or
poor in quality. When considering adults who experience daytime sleepiness a few
days a week versus those who do not, 73% complain of at least one symptom of
insomnia, while only 25% carry a diagnosis of a sleep disorder (21). Detailed inquiry
regarding the use of sleep aids is important when assessing patients for insomnia.
Addressing the sleep environment and associated behaviors that revolve around
sleep onset are crucial pieces of history when trying to understand the etiology
of one’s sleep complaints and potential confounding or contributing factors.
Understanding the location of one’s sleep environment, the nature of temperature,
light and sound exposure, and whether or not the bed is shared (i.e., number of persons,
pets, etc.) can help address a patient’s sleep complaints. Activities prior to bedtime
must also be discussed. Ingestions (food, caffeine, alcohol, medications, tobacco, illicit
substances) close to bedtime can have varied effects on one’s sleep. Stimulating activi-
ties prior to bedtime, such as exercise (within 6 hours), television watching, reading,
working, music listening, can all affect sleep quality. The location of where such
activities are occurring must also be appreciated. Bedtime rituals (i.e., bathing, clothing
worn to sleep, etc.) can also enhance one’s insight to a patient’s sleep complaints.
Asking the patient for his/her Sleep-Wake schedule is important in demarcating a
sleep complaint. Schedule abnormalities in wake and sleep times are clues to the
more common sleep disorders (i.e., insomnia and circadian rhythm disorders) and
can also be useful in suggesting the presence of other sleep disorders (i.e., narcolepsy
and sleep apnea). It can be important to elicit prebedtime rituals, such as caffein-
ated/acidic foods, prescription/illicit/over-the-counter (OTC)/herbal pharmaceu-
ticals, tobacco, and presleep physical, emotional, or cognitive stimulations. Nocturnal
waking behaviors, especially nocturia, are proposed as a clue for sleep apnea (22).
An inquiry would include details on usual bedtime, time to falling asleep
(sleep latency), awakenings from sleep (frequency, length, identifiable causes), final
wake-up time (naturally; prompted by alarm, pet, or another person), and nap times
and nap length. A formal sleep log over several days to weeks can be useful, since
there can often be a discrepancy between remembrance reports on the first visit and
prospectively recorded events.
4 Ramsey et al.
Cataplexy is specifically used in the diagnosis of narcolepsy. Cataplexy refers to a
sudden loss of postural tone that is precipitated by the experience of strong emotion.
Triggers include joy, sadness, anger, and hilarity. Laughter is the most common
trigger (23,24). Cataplexy can manifest in many ways including head nodding,
collapsing, dropping an item, and so on. The knees, face, and/or neck are the most
common muscle groups involved; oculomotor involvement can also occur, affecting
one’s vision. Respiratory muscles are not affected by cataplexy. Loss of conscious-
ness is rare (23). Cataplectic events are usually short, ranging from a few seconds to
at most several minutes, and recovery is immediate and complete (23). People found
to have narcolepsy, however, often carry a variety of other diagnoses such as peri-
odic paralysis, absence seizures, and fugue states, often because the trigger events
are not elicited or ignored. Other associated features of narcolepsy include sleep paral-
ysis, hypnagogic hallucinations, and reports of poorly consolidated sleep (described
below). These symptoms can however occur, albeit infrequently, in normal patients
and isolated symptoms may be present in those with other sleep disorders.
A curious correlation is reported with one’s month of birth and increased odds
of manifesting narcolepsy. Retrospectively, when 800 birthdates were reviewed
from confirmed narcoleptics with cataplexy in North America and Europe, the
monthly distribution of birth yielded a peak in March with a maximal OR at 1.45
and a trough in September with a minimal OR at 0.63 (24,25).
Sleep paralysis is a transient, generalized inability to move or to speak during the
transition between sleep and wakefulness. Such experiences can be frightening to
the patient, as muscle control is regained within a few minutes. The sensation of
being unable to breathe is sometimes perceived. Sleep paralysis is reported in
40% to 80% of narcoleptics, and may occur with sleep deprivation and can be
Hypnagogic hallucinations are vivid perceptual (visual, tactile, auditory) experi-
ences typically occurring at sleep onset. These experiences are often accompanied
by feelings of fear. Recurring themes include being caught in a fire, being attacked,
or flying through the air. Recurrent hypnagogic hallucinations are experienced by
40% to 80% of patients with narcolepsy and cataplexy (20).
Movements During Sleep
This class of symptoms can encompass a number of disparate but important entities
including restless leg syndrome (RLS), periodic limb movement disorder (PLMD),
and parasomnias. RLS is characterized by an irresistible urge to move the legs and
is usually accompanied by uncomfortable and unpleasant sensations in the legs
(dysesthesias). Spontaneous unpleasant sensations (e.g., “creepy-crawly,” “ants
marching up my legs”) of the limbs occur at rest and are usually relieved by move-
ment. This typical description is extremely suggestive if not diagnostic. RLS is
estimated to occur in approximately 2.5% to 15% of the population, with a female
predominance, occurring 1.5 to 2 times more commonly in women, and its preva-
lence increases with age (26–29). Other considerations include conditions such as
History and Physical Examination
muscle cramps and myotonic jerks, positional discomfort, hypotensive akathisias,
sleep starts (hypnic jerks), neuroleptic-induced akathisias, sleep-related leg cramps,
pain associated with arthritis, vascular conditions, injuries, and neuropathy can all
mimic RLS to a certain degree.
A majority of patients with RLS also have concomitant periodic limb move-
ments during sleep (PLMS). These PLMS primarily manifest by kicking or jerking
leg movements at night, but they can also affect the arms. PLMS can be mistaken as
leg kicks that occur around the time of arousal following an apnea or more rarely as
a seizure. The individual leg movements that comprise PLMS need to meet specific
polysomnographic criteria (i.e., 25% of baseline amplitude, 0.5–5 seconds duration,
separated by 4–90 seconds, train of four individual leg movements equals one
periodic leg movement) as well as resulting in a clinical sleep disturbance or day-
time fatigue in order to meet the criteria for PLMD (20).
For the most part, parasomnias involve complex, seemingly purposeful, and
goal-directed behaviors. There are 12 core categories of parasomnias, with only
rapid eye movement (REM) sleep behavior disorder (RBD) requiring polysomno-
graphic confirmation for diagnosis (20). Although the remaining 11 categories are
clinical diagnoses, coexisting polysomnographic collaboration findings can be very
helpful adjuncts at confirming or excluding certain diagnoses. OSA-induced arousals
from REM or non-REM (NREM) sleep with complex or violent behaviors may
trigger parasomnias, including: RBD, confusional arousals, sleepwalking, and sleep
terrors, and sleep-related eating disorder. The differential diagnosis would include
nocturnal complex seizures or nocturnal dissociative states. It is suggested that
rebound slow-wave sleep with initiation of therapy for sleep apnea could trigger
SPECIFIC CUES FOR SLEEP APNEA WITH AN EMPHASIS
ON ADULT PRESENTATIONS
Sleep apnea is a very prevalent disorder in important populations. Epidemiological
studies estimate the prevalence to be 2% to 4% in the general population (3,30,31),
while other, more selected population studies achieved a prevalence range of 7% to
16% (2,32). Prevalence estimates (and therefore pretest probability) increase in
clinical populations due to an enrichment of medical problems. Rates encountered
in the primary care or hospital settings are particularly high: primary care (high risk
37.5%) (4), obese 40% to 60% (33), bariatric surgery evaluation 71% to 87% (34,35),
hypertension 38% (36), stable outpatient congestive heart failure (CHF) > 50%
(37,38), coronary artery disease (CAD) > 50% (39), acute stroke > 70% (40,41), and
sleep clinic 67% (29).
In regards to the presentation of sleep apnea, studies show a strong relation-
ship between age and sleep apnea (see also Chapter 16) (15,30,42,43). Duran (2) found
that sleep apnea prevalence increased with age with an OR of 2.2 for each 10-year
increase. The Sleep Heart Health Study noted that prevalence rose steadily with age
up to 60 years at which point a plateau in prevalence occurs around 20% (15). It has
also been shown that the severity of sleep apnea (42) and the effect of body mass
index (BMI) seem to decrease with age (15,43) and that the magnitude of associations
for sleep apnea, snoring, and breathing pauses also decreases with age (15).
Men have a higher prevalence of sleep apnea than women across all ages in
epidemiological (3,31,44) and clinic-based studies (see also Chapter 14). This effect
6 Ramsey et al.
diminishes with time, however, and both sexes achieve a similar incidence by age 50
(43). A study of OSA incidence and its risk factors found the risk for sleep apnea in
men increased only marginally with age, while it increased very significantly in
women: the OR (confidence interval) for increased AHI per 10-year increase was 2.41
in women (1.78–3.26) and only 1.15 (0.78–1.68) in men (43). A study of Hong Kong
women found a 12-fold rise in the prevalence of sleep apnea in women between the
fourth and sixth decades (31). There is a large amount of literature to support the
role of menopause in modulating this increased risk for sleep apnea in women
around the age of 50 (44–46). In general, men and women are present with the
same constellation of sleep-related symptoms and complications (47). Women
with OSA may be slightly older, more obese, more likely to use sedatives, and
complain of insomnia and depression (48).
It is not clear if race can be categorically used to confer risk, or if race difference
is just a surrogate for a different risk profile. A study of sleep apnea risk factors in
the Sleep Heart Health Study did not show a significantly higher prevalence in
African-Americans (15) and another did not note any differences in respiratory
disturbance index (RDI) when adjusted for known confounders (49). In contrast,
a study of older community dwelling adults found that African-Americans had a
2.5 times greater odds of having an AHI > 30 (50), and the Cleveland Family Study
found the prevalence of sleep apnea in young African-Americans was higher than
that of Caucasians (51).
Studies in Asia estimate the prevalence of sleep apnea to be similar to that of
the West (30,31). This is an intriguing finding given that obesity, the risk factor
believed to modulate a large part of the risk for sleep apnea in the West, is less
common in Asia. Other factors must therefore act in the expression of this disorder.
Craniofacial morphology has been implicated as a modifier of risk in nonobese
populations but could also interact with obesity as well (52–54).
History of Present Illness
General issues in the presentation would be the age of onset of symptoms as well as
some consideration of the trajectory of illness severity. Some of these features are
listed in Table 1, and includes features important in both adult and pediatric popu-
lations. The pediatric examination is also discussed in a separate section below.
Sleepiness is very common in sleep apnea patients: 38% to 51% in one epide-
miological study (55) and 47% to 73% in a sleep clinic population (56). Despite this
it is not associated with sleep apnea in clinical studies. This is in large part due to
difficulty in differentiating sleep from fatigue. In a study of sleep apnea patients’
perception of their problems, lack of energy, tiredness, and fatigue were more prev-
alent complaints than sleepiness (56).
Snoring is extremely common in sleep apnea patients and its absence should
make OSA less likely (13). In one study only 6% of patients with OSA did not report
snoring. Keep in mind however, that many patients have misperceptions about their
snoring and tend to underestimate it (57). Some studies have shown that a report of
“loud” habitual snoring strengthens by seven-fold the statistical association with
sleep apnea and snoring (4,15,58). Witnessed apneas are relatively specific for sleep
apnea, but have a low sensitivity (15).
Insomnia complaints are highly prevalent in OSA. Fifty-five percent of patients
being referred for possible evaluation of OSA were noted to have complaints of
insomnia, with difficulties maintaining sleep (38.8%) being more common than
History and Physical Examination
difficulties initiating sleep (33.4%) or early morning awakenings (31.4%). Despite the
overall high prevalence of insomnia complaints in this study population, insomnia
was more common in patients without rather than with significant sleep-disordered
breathing (81.5% with AHI < 10 vs. 51.7% with AHI > 10) (59). The high prevalence of
insomnia complaints may be attributable to the fact that the sleep disruption associ-
ated with OSA may be perceived as insomnia, or perhaps such patients with insomnia
and OSA are more symptomatic, thus more likely to seek medical attention.
Weight gain increases the probability of sleep apnea. One large population-
based study found a 10% weight gain and predicted a 32% increase in AHI. This
translated to a six-fold increase in the odds of developing (moderate-to-severe)
sleep apnea (32). Inversely, a decrease in weight leads to an improvement in sleep
apnea. Studies in bariatric surgery patients show a dramatic improvement in RDI
after weight loss (35,60).
Frequent awakening from sleep to urinate is common in sleep apnea patients.
One retrospective study found a prevalence of 49% in sleep apnea patients (61) and
others have noted frequent nocturia is related to sleep apnea severity (61–63).
Nocturnal angina may be related to apneas in some patients with ischemic
heart disease and sleep-disordered breathing. Small series in patients with ischemic
heart disease and relatively severe sleep apnea suggested a link between myocardial
ischemia and apneas (64,65). However, these findings conflict with a larger study
that included patients with less severe sleep apnea and failed to appreciate a signifi-
cant association (66).
Past Medical History
OSA will coexist with other sleep disorders. A retrospective analysis of 643 OSA
patients found that 31% had another sleep disorder: 14.5% had poor sleep hygiene
and 8.1% had PLMD (67). In two other studies more than 50% of sleep apnea patients
complained of insomnia (59,68).
Sleep apnea is not only associated with cardiovascular disease but may directly
contribute to its pathogenesis. It was present in 38% of hypertensive subjects in one
TABLE 1 Features of Emphasis in the Adult and Pediatric Examination
Impact on daytime
Irritability, mood swings, hyperactivity, automatic behaviors, work
or academic performance, behavior of concern (inappropriate
napping, inattentiveness), absences from work or school
Usual bedtime, fall asleep time, wake time, napping habits,
Presleep routines and related transitional objects (television,
pacifier, toy, etc.)
Shared or private room, bed partners (including pets/toys/stuffed
animals); electronics or other toys that may impede sleep
routines; persistence or resolution of sleep complaints in other
environments (hotels, sleepovers, etc.)
Side sleeping and/or neck hyperextension to relieve obstruction
Caffeinated beverages, tobacco products, recreational drugs
Snoring, witnessed apneas, paradoxical breathing, mouth
breathing with dry mouth and throat, morning headaches,
gastroesophageal reflux, sweating (may suggest increased
work of breathing), stereotypic movements/complaints
suggestive of seizure or movement disorders (including
parasomnias and restless legs syndrome)
Customs surrounding sleep
Body position(s) during sleep
Other sleep behaviors
8 Ramsey et al.
study (36). A dose–response relationship is present (69) and several trials found a small
but significant improvement in hypertension with sleep apnea treatment (70–72).
Others suggest that the prevalence of sleep apnea in patients with CAD, postmyo-
cardial infarction, CHF, and poststroke to be > 50% (37–41). Results from the Sleep
Heart Health Study show increasing odds of self-reported heart failure, stroke, and
CAD in subjects with a high AHI (73). Additionally, a pathogenic role is suggested
by observational studies that show fewer adverse cardiovascular outcomes in
treated versus untreated patients (74–76).
Several studies have found that sleep apnea is independently associated with
glucose intolerance and insulin resistance (33,77). The Sleep Heart Health Study
found that patients with mild and moderate/severe OSA had increased adjusted
ORs for fasting glucose intolerance: 1.27 (0.98, 1.64) and 1.46 (1.09, 1.97), respec-
tively (77). At least one treatment study has found improvement in glucose control
in patients treated for sleep apnea (78).
Depression is linked to sleep apnea in a number of correlation studies. Most
are small, use different instruments to measure depression, and indicate that 24% to
58% of sleep apnea patients have some measure of depression (79,80). In a larger
European telephonic survey, 17.6% with a Diagnostic and Statistical Manual of
Mental Disorders (DSM-IV) breathing-related sleep disorder also had a diagnosis of
a major depressive disorder (81).
Sleep apnea in the setting of pulmonary diseases is called the “overlap
syndrome.” Chronic obstructive pulmonary disease is the most common of these,
but has a prevalence in the sleep apnea population similar to that of the general
population (82). Pulmonary arterial hypertension is another disease but is much less
common and the prevalence of sleep apnea in these patients is not well studied (83).
Hypothyroidism symptoms of fatigue can overlap with those of sleep apnea.
Case series have reported improvement or resolution of sleep apnea in selected
patients treated with thyroxine alone (84). Nonetheless, the limited evidence
available suggests the prevalence of hypothyroidism in sleep apnea patients is no
different than that seen in the general population (85) and routine screening in the
absence of other signs of hypothyroidism is not cost-effective. Cases have also
described lingual thyroids causing airways obstruction at night (86).
Glaucoma (87), end-stage renal disease (88,89), and gastroesophageal reflux
disease (90,91) have been reported to occur with OSA, but the specificity of the asso-
ciations are not established.
The occurrence of sleep disturbances during pregnancy is well documented,
but the prevalence and incidence of specific sleep disorders is not confirmed in
large-scale population studies. A spectrum of association between pregnancy and
sleep disturbances ranges from an increased incidence of excessive sleepiness, insom-
nia, nocturnal awakenings, and parasomnias (especially restless legs syndrome) to
snoring, and both obstructive and central sleep apnea (92). Although specific sleep
disorders tend to emerge during different stages of pregnancy, the third trimester
appears to be the most vulnerable. Of special attention are those women who gain
excessive weight during pregnancy. Thus, during routine perinatal obstetrical care,
the sleep history should be periodically revisited.
Sleep apnea significantly worsens after heavy alcohol ingestion (93,94). The effect of
more moderate levels of alcohol ingestion on sleep apnea are not as clear and results
History and Physical Examination
are conflicting (95,96). Some proposed mechanisms include increased nasal resis-
tance due to edema, and reduced hypoglossus motor nerve activity.
Data from the Wisconsin Sleep Cohort Study found current smokers to have an
increased risk of having moderate sleep apnea compared to nonsmokers (OR 4.44).
Heavy smokers had the higher risk (OR 40.47) (97). One sleep clinic study found
current smokers to have increased adjusted odds of sleep apnea [OR 2.5, confidence
interval (CI) 1.3–4.7, p = 0.0049] (98).
The Cleveland Family Study found that there is a familial aggregation to sleep
apnea. Families with an index case of sleep apnea had a higher prevalence of sleep
apnea than in those without (21% vs. 9%, p = 0.02) and risk increased with addi-
tional affected members (99). Ongoing genetic studies are trying to find the relative
role of different anatomical risk factors in mediating this increased risk. At the
present time routine assessment and testing of family members is not advocated in
the absence of clinical illness.
The sleepiness and lack of concentration that accompanies sleep apnea impair
work performance, driving ability (100,101) and have deleterious effects on family
relationships. Commercial drivers are a special group that is receiving an increasing
amount of attention, as driving risk becomes a public safety issue. Moreno et al.
(102) administered the Berlin questionnaire to a large group of truck drivers and
found that 26% were at high risk for sleep apnea; however, the presence of inactivity
and obesity were also strongly implicated in this pretest probability estimate.
There is an accounting of medication use for sleep apnea in Chapter 17. In general,
medications to note during the history and physical fall into three categories:
(i) those that are associated with OSA, (ii) those that sedate and/or decrease respira-
tory drive, and (iii) those that impair sleep onset or maintenance (Table 2).
Drug-induced sleepiness is the most commonly reported side effect of central
nervous system active pharmacological agents; the 1990 Drug Interactions and Side
Effects Index of the Physicians’ Desk Reference lists drowsiness as a side effect of
584 prescription or OTC preparations (103).
Nasal obstruction contributes to the worsening of sleep-disordered breathing, but
the extent to which this might be related to allergic rhinitis is not known. One case-
control study did show that sleep apnea patients had a higher rate of perennial
allergic rhinitis and atopy than controls (104).
THE PHYSICAL EXAMINATION FOR ADULT SLEEP APNEA
A sleep physical examination is directed at modifying the probability of sleep-
disordered breathing based on the history, looking for evidence of associated or
complicating disease, and excluding other potential causes for neurologic or cardio-
vascular symptoms. A broader examination incorporating many of the other organ
systems should be employed when considering other sleep disorders that may be
caused by or confounded by other diagnoses.
10 Ramsey et al.
TABLE 2 Medications as Clues to Predisposing Factors
Medications that sedate and/or
reduce respiratory drive
Medications that impair sleep
onset or maintenance
Ethanol, illicit narcotics
Hypertensive and diabetic
Ethanol, illicit narcotics
Genitourinary smooth muscle
Melatonin receptor agonists
Monoamine oxidase inhibitors
Selective serotonin reuptake
Valerian root, kava kava, melatonin,
Caffeine, nicotine, ethanol,
Monoamine oxidase inhibitors
Abbreviations: NSAIDs, nonsteroidal anti-inflammatory drugs; OSA, obstructive sleep apnea.
Many population-based studies have shown that hypertension is independently
associated with sleep-disordered breathing studies (105–110). Blood pressure has
been integrated into several clinical prediction rules for sleep apnea (4,12,17,18).
One study found hypertension to have an adjusted OR of 11.9 for an AHI ≥ 30 (17).
More recently, a causal relationship has been suggested by a number of studies that
have shown an improvement in hypertension with sleep apnea treatment (70–72).
Although a number of different measures of obesity have been used in clinical studies
the BMI is probably the best and certainly the most practical. It has been found to be
strongly associated with the presence of sleep apnea (4,12–15,18,108,111–115) and
has been incorporated into a number of clinical prediction rules (4,13,14,18) for this
disorder (see also Chapter 2).
History and Physical Examination
Neck circumference is, in part, a surrogate for obesity but clinical studies have also
found an independent association with sleep apnea (12,15,113,114), and one study
has incorporated it into a multivariate clinical prediction rule for sleep apnea (12).
One epidemiological study found the OR for an AHI ≥ 15 with an increment of one
standard deviation (SD) in neck circumference to be 1.5 (15).
Nasal obstruction has been implicated as a potential cause of sleep apnea. It can
lead to higher inspiratory upper airway pressures and increased collapsibility of
pharyngeal walls (116). Also, it appears to predispose to mouth breathing and the
downward and backward displacement of the mandible (111), which may worsen
airway obstruction at the level of the base of the tongue. Nasal resistance, as mea-
sured by posterior rhinometry, was significantly higher in patients with sleep apnea
(115). A combination of nasal obstruction and a high Mallampati score (3 or 4; see
below) are associated with an increased risk for the diagnosis of sleep apnea (RR
2.45, CI 1.23–4.84) (113). In this latter study, obstruction was measured by having the
patient gently block one nostril, breathe through the other and having the physician
listen for evidence of obstruction.
The external nasal valve comprises the columella, the nasal floor, and the nasal rim
[inferior border of the lower lateral alae nasi (nasal cartilage)], which normally is dilated
by the nasalis muscle during inspiration. Collapse of the nasal rim upon inspiration
through the nose alone, is also often a sign of OSA-associated nasal resistance (Fig. 1).
Pharyngeal and Craniofacial Features
Pharyngeal and craniofacial morphology play an important role in the etiology of
sleep apnea. Some anatomical variants result in a crowded oropharyngeal space
FIGURE 1 (See color insert.) Bilateral
collapse of the nasal rim that is frequently
observed during inspiration in patients
with obstructive sleep apnea-associated
nasal resistance. Source: Photograph
courtesy of Kannan Ramar, M.D.
12 Ramsey et al.
and predispose to obstruction during sleep. Many clinical studies have taken
different measures of pharyngeal and craniofacial morphology and found associations
between them and the presence of sleep apnea. Their utility however, has been
notably impaired by their lack of simplicity and practicality at the bedside.
One measure used in the assessment is the Mallampati score (Fig. 2). Designed
originally by anesthetists to grade intubation difficulty, the Mallampati grade
correlated well with the severity of RDI: r = 0.34, p < 0.001 (117). In another study
comparing apneic versus nonapneic patients, sleep apnea patients more often had a
Mallampati score of 3 or 4 (78.8% vs. 46%, p < 0.001) (112).
Two other pharyngeal measurements that were independently associated
with sleep apnea: tonsillar enlargement (OR 2.6) and lateral narrowing of the
pharyngeal wall (OR 2.0) (Fig. 3) (118). Other potentially useful pharyngeal measures
include tongue size (118), uvula size (118,119), and palatal height (112,114).
“Scalloping” or dental impressions at the edge of the tongue may indicate the
presence of an enlarged tongue that habitually presses against the teeth.
FIGURE 3 (See color insert.)
Lateral narrowing of the pharyn-
geal wall. Source: Photograph
courtesy of Kannan Ramar, M.D.
FIGURE 2 Mallampati classification system based on visualization of posterior oropharyngeal
structures. Class 1, soft palate, fauces, uvula, anterior and posterior pillars visible; Class 2, soft
palate, fauces and uvula visible; Class 3, soft palate and base of uvula visible; Class 4, soft palate
History and Physical Examination
Retrognathia, micrognathia, and overbite (120) are craniofacial features that
capture jaw factors that are associated with a restricted posterior pharynx. These are
recognized qualitatively by noting the relative size of the jaw to the maxilla, forward
protrusion of the upper teeth over the lower teeth, and absent lower teeth that were
surgically removed due to crowding. Quantitative measures of these features are
typically obtained by cephalometric radiographs and may be useful in modifying
Other Features in the Examination
There is no intention of ignoring other features of the physical examination, as no
one feature or collection of findings is ever fully sensitive and specific for sleep
apnea and since the physician-patient encounter is designed to do more than just
capture a suspected diagnosis. A respiratory, cardiovascular, and neurologic
examination can contribute to the sleep workup. A normal neurological examination
is particularly important in a patient suspected of having restless legs syndrome
and/or PLMD with an emphasis on spinal cord and peripheral nerve function.
Similarly, a normal peripheral vascular examination would exclude pathology that
might be mistaken for restless legs syndrome.
THE EXAMINATION OF THE PEDIATRIC PATIENT
Sleep complaints or disorders occur in 1% to 28% of the pediatric population
(121–123) (More information on the examination of the pediatric patient can be
found in Chapter 15.). The incidence of OSA in children can range from 1% to 10%,
while snoring can occur in 3% to 12% (123–126). These studies often emphasize the
idea that the presentation of sleep disorders in children is different from those in
adults. While this literature is interesting, for the purposes of this chapter, the basic
elements of the history and physical have limitations in regard to common mea-
sures and approaches from which to make firm evidence-based conclusions.
One issue is relatively clear however. Children, with the possible exception of
those who are obese, less commonly present with EDS (127). Rather, they present
with a failure to thrive, attention and behavior problems, and show impaired
academic performance. Thus, the approach and the thresholds for clinical suspicion
of sleep disorders differ somewhat in the pediatric population.
It is crucial that an appropriate historian (parent, guardian or other adult caregiver)
is involved in the diagnostic process. A substantial proportion of pediatric sleep
disorders involve psychosocial issues that can only be assessed by talking with the
primary caregiver, and the value of teacher input is emphasized in this literature as
well. The parent-child interaction should be observed closely for clues to extrinsic
problems, such as sleep-onset association, limit setting disorder, or child maltreat-
ment syndrome (126).
An age-appropriate history should be obtained. Certain elements stressed or
elaborated on during a pediatric sleep-related history are similar to adults, while
others are not (Table 1). As with the adult patient, features of the age of onset, degree
of severity/stability, and frequency of the patient’s sleep complaint, as well
as responses to any attempts at treating the problem can give clues. Any new day-
time or nocturnal symptoms or signs can also provide information; for instance,
14 Ramsey et al.
new-onset enuresis in a child can indicate a sleep disorder like sleep apnea.
Occasionally, a child is recognized with a sleep disorder during a family vacation
when there is greater opportunity for parents to observe children while sharing
hotel rooms. Home-video footage both of waking and sleep behaviors can also pro-
vide invaluable information, if available.
Routine key vital statistics include blood pressure, respiratory rate, heart rate,
height, weight, age-appropriate BMI (for children with OSA can be either too thin or
too heavy), and their position (relative population-based percentile standing) on
age-appropriate growth charts.
A thorough examination would include mention of the child’s general appear-
ance and craniofacial characteristics such as midface hypoplasia, micrognathia, and
occlusal relationships. In infants, septal deviation, choanal atresia, nasolacrimal
cysts, and nasal aperture stenosis must be excluded, while in older children, nasal
polyps and turbinate hypertrophy must be considered as a cause for upper airway
Adenotonsillar hypertrophy is a common finding not only in the general
pediatric population but in pediatric OSA patients as well. Oral assessment includes
screening for signs of mouth breathing, tissue redundancy, and cleft lip/palate,
as well as evaluation of tongue size, dentition, and tonsillar grading, including
Mallampati staging (Fig. 2). Consideration for adenoid assessment with lateral neck
radiography or nasopharyngoscopic examination can also be helpful.
Neuromuscular disorders, such as myotonic dystrophy, can be associated
with chronic obstructive hypoventilation from a combination of oropharyngeal
muscle weakness that leads to airway collapse and hypoventilation from diminished
respiratory muscle excursion (126). Hoarseness of the voice, decreased gag reflex,
and abnormal tendon reflexes could be clues to brainstem abnormalities, such as
Chiari malformation (129).
Congenital craniofacial syndromes are associated with OSA. Children who
have syndromes with craniosynostosis, such as Apert’s syndrome, Crouzon’s
disease, Pfeiffer’s syndrome, and Saethre-Chotzen syndrome; abnormalities of the
skull base; and accompanying maxillary hypoplasia may have nasopharyngeal
obstruction (126). Children with syndromes that involve micrognathia, such as
Treacher Collins syndrome, Pierre Robin syndrome, and Goldenhar’s syndrome,
become obstructed at the hypopharyngeal level; and children with trisomy 21 often
have a narrow upper airway combined with macroglossia and hypotonic musculature
predisposing them to OSA (126).
The clinical evaluation for a patient being evaluated for presumed OSA represents
an essential first step in the diagnosis of this common sleep disorder. The primary
care physician or sleep specialist needs to inquire about the key symptoms of sleep
apnea, such as EDS, snoring, and witnessed sleep-disordered breathing symptoms;
other important symptoms or practices that affect the patient’s sleep such as
insomnia, sleep hygiene, and the patient’s Sleep-Wake schedule also need to
be assessed. Symptoms of other sleep disorders, such as those associated with nar-
colepsy, restless legs syndrome, PLMD, and parasomnias should be ruled out as
History and Physical Examination
possible contributors to the patient’s sleep complaints. The experienced clinician
should be aware of the high prevalence of sleep apnea; the relationships between
this disorder and age, gender, and ethnicity; and the more complex associations
including those between OSA and cardiovascular disease, glucose intolerance and
insulin resistance, depression, pulmonary disease, and hypothyroidism. A careful
evaluation of the patient’s social, family, medication, and allergy history is critical for
identifying or discounting possible risk factors for sleep apnea. The physical exami-
nation for adult patients with suspected OSA should be comprehensive, and should
include assessment of blood pressure, indicators of obesity (e.g., BMI, neck circum-
ference), nasal function, pharyngeal, and craniofacial features. Lastly, the examina-
tions of pediatric versus adult patients with suspected OSA are not identical, since
the presentation, symptoms, and physical signs associated with childhood OSA are
markedly distinct from those associated with adult OSA.
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Screening and Case Finding
Charles F. P. George
University of Western Ontario, London Health Sciences Centre,
London, Ontario, Canada
Obstructive sleep apnea (OSA) is becoming increasingly recognized. OSA with
daytime impairment is estimated to occur in one of 20 adults but is often unrecog-
nized or undiagnosed. Minimally symptomatic or asymptomatic OSA is estimated
to occur in one of five adults (1). A primary goal of screening is the detection of a risk
factor or disease at an early stage, when it can be corrected or cured. Disorders with
a long latency period increase the potential gains associated with detection; however,
in many cases early detection does not necessarily influence survival. Screening tech-
niques must be cost-effective, if they are to be applied to large populations. Costs
include not only the expense of testing but also time away from work and potential
risks. When the risk-to-benefit ratio is less favorable, it is useful to provide information
to patients and factor their perspectives into the decision-making process.
In the case of sleep apnea, while the prevalence is high, it remains unclear
exactly which cases will develop significant cardiovascular disease, or which will be
victims of increased accidents. Because it may be the minority of cases that actually
develop morbidity, widespread screening cannot yet be considered for patients with
Case finding on the other hand mirrors clinical practice more closely and for
this reason this chapter will focus on case finding in clinical practice and the tools at
the disposal of the sleep medicine physician.
DIAGNOSIS OF SLEEP APNEA
Modern diagnosis of sleep apnea has its roots in the landmark descriptions by
Gastaut (2,3), Jung and Kuhlo (4), and others (5,6). Since these early descriptions of
polygraphic recordings, the nocturnal, attended in-lab polysomnogram has become
the standard for diagnosing OSA. In the early days of our understanding of the
condition, the patient presented at advanced stages with severe symptoms, cardio-
vascular comorbidities, and a prominent history of loud snoring and witnessed
apneic episodes. Over the years, a wider spectrum of disease severity has emerged
and it is clear that many patients are minimally affected or not symptomatic at all.
These patients are usually brought to attention because of their bed partner’s
descriptions of snoring and concerns about not breathing. This has increased the
demands on in-lab polysomnography, still a limited resource, and prompted alter-
native strategies for diagnosing sleep apnea.
DEFINITIONS OF SLEEP-DISORDERED BREATHING
Apnea was first defined by Guilleminault et al. (7) as a cessation of air flow at the nose
and mouth for at least 10 seconds and a sleep apnea syndrome was diagnosed if
during seven hours of nocturnal sleep, at least 30 apneic events are observed in both
rapid eye movement (REM) and non-rapid eye movement (NREM) sleep, some of
which must appear repetitively in NREM sleep. The International Classification of
Sleep Disorders (second edition) (ICSD-2) includes both clinical and polysomno-
graphic (PSG) features for defining OSA with “five or more scorable respiratory events
per hour of sleep” as the PSG cut-off for disease (8). The concept of hypopnea [as a
reflection of an increased but not complete (i.e., apnea) upper airway obstruction] was
first highlighted by Gould (9) and the subsequent demonstration of an increased spec-
trum of upper airways resistance by Guilleminault (10) has led to a more inclusive
term of sleep-disordered breathing that includes apneas, hypopneas, and respiratory
effort-related arousals (RERA) from sleep. The ICSD-2 and updated practice parame-
ters of the American Academy of Sleep Medicine both highlight the lack of clinical
consensus for hypopnea and RERA definitions, although research definitions have
been established. More recently, these definitions have been consistent amongst stud-
ies but such consistency is missing from some of the early literature (vide infra).
CLINICAL PREDICTION MODELS
Well-established sleep apnea is characterized by loud snoring, witnessed apneic
episodes, disturbed nocturnal sleep, daytime sleepiness, and impaired cognition
and is typically associated with obesity and (in men) a large neck size. Given this
profile, it is not surprising that clinical prediction models would arise in an effort to
diagnose OSA in larger populations. Virtually all of these studies have been done in
sleep clinic populations rather than in the general population. One of the earliest
studies showed that witnessed apneic episodes combined with loud snoring
predicted an apnea-hypopnea index (AHI) > 10 with a sensitivity of 78% and speci-
ficity of 67% (11). Crocker et al. (12) used an alternative approach and developed a
statistical model using clinical data to predict disturbance of sleep-disordered
breathing in 114 consecutive patients. Witnessed apneic episodes, hypertension,
body mass index (BMI), and age provided a sensitivity of 92% but a specificity of
only 51% for an AHI > 15. Using 410 clinic patients, Viner et al. (13) developed a
model incorporating snoring, BMI, age, and sex and came up with sensitivity and
specificity of 94% and 28%, respectively. The higher the pretest probability of sleep
apnea, the better the positive predictive value of their model. Maislin et al. (14)
added to this and developed the multivariable apnea prediction index, which
includes questions about frequency of symptoms of apnea as well as measurements
of BMI, age, and gender. Using this tool, at a BMI > 40, the likelihood of apnea is very
high with or without symptoms, while at lower values of BMI, the likelihood of sleep
apnea is much more dependent on whether or not the individual has symptoms.
Predictive abilities assessed using receiver operating characteristic (ROC) curves
noted that for BMI alone the ROC value was 0.73, and for an index measuring a self-
report of apnea symptoms it was 0.7. Many other papers have demonstrated varying
degrees of variability and specificity with their clinical prediction models (15–17).
Netzer et al. (18) assessed the utility of the Berlin Questionnaire to diagnose
sleep apnea in a primary care setting. This questionnaire asks about risk factors for
sleep apnea, namely snoring behavior, wake time sleepiness or fatigue, and the
presence of obesity or hypertension. A subset of patients underwent overnight portable
recording using a six-channel recorder (EdenTrace® Recording System, vide infra). This
approach resulted in a sensitivity of 86%, a specificity of 77%, and a positive
predictive value of 89% for OSA. This questionnaire appears to be a useful tool but
Screening and Case Finding
needs to be tested in other populations with neck circumference, age, and race
added to the predictive model.
Rodsutti et al. (19) derived and validated a clinical decision rule to assess risk
of sleep apnea and prioritized those for polysomnography. Five variables—age, sex,
BMI, snoring, and stopped breathing during sleep—were significantly associated
with sleep apnea. ROC analysis for both derivation and validation sets gave area
under the curve (AUC) values of 0.81 and 0.79, respectively.
The uses of neural networks for predicting or excluding sleep apnea have
been demonstrated in a few studies. Artificial neural networks (ANNs) are com-
puter programs modeled after the nervous system and are capable of recognizing
complex patterns in data. ANNs are ‘‘trained’’ by presenting a set of data together
with the outcomes that the trainer wishes the network to learn. The trained ANN
can then be evaluated by inputting similar but previously unseen data. This
approach for outcome prediction has been used successfully in medical applications,
including the prediction of acute myocardial infarction in patients presenting to an
emergency room physician (20), the diagnosis of pulmonary embolism (21,22), and
the predicted length of stay of patients in an intensive care unit (23).
El-Solh et al. (24) utilized a back-propagation ANN algorithm on 189 patients
as a training set and validated it prospectively on 80 additional patients. Predictive
accuracy at different AHI thresholds was assessed by the c-index, which is equi-
valent to the area under the ROC curve. The c-index for predicting OSA in the
validation set was 0.96, 0.95, and 0.935 using thresholds of > 10, > 15, and > 20/hour,
respectively. They suggested that ANN may be useful as a predictive tool for OSA.
Using a backward error propagation ANN with 23 clinical variables and a leave-
k-out strategy, Kirby et al. (25) found the positive predictive value that a patient
would not have sleep apnea to be 98%, with a negative predictive value the
patient would have sleep apnea (AHI > 10) to be 89%. In that study the use of
the ANN would have reduced the number of PSGs performed by 22%.
Additional approaches have also been taken. Kushida et al. (26) incorporated
oral cavity measures into a morphometric model of OSA, using a degree of maxil-
lary overjet, intermolar distance, and maxillary mandibular planes and palatal
height, combined with neck circumference and BMI. This model had a sensitivity of
98%, specificity of 100% and a positive predictive value of 100% for an AHI > 5.
Despite these impressive results, this technique has not been replicated in other
centers, possibly because it is somewhat labor intensive. More recently, Tsai et al.
(27) noted the three main reliable clinical symptoms of sleep apnea (snoring,
witnessed apneas, and hypertension) and three signs of sleep apnea (thyro-mental
space less than 1.5 cm, pharyngeal grade > 2, and the presence of an overbite)
provided a positive predictive value of 95%. A thyro-mental space of > 1.5 cm
excluded sleep apnea with a negative predictive value of 100%.
Rowley et al. (28) prospectively studied the utility of four clinical prediction
models for predicting the presence of sleep apnea or prioritizing patients for a
split-night protocol. They took four clinical prediction formulae of Crocker, Viner,
Flemons, and Maislin to calculate the probability of sleep apnea for each model in
each of 370 clinic patients. For an AHI > 10, their sensitivity ranged using these
models from 76% to 96%, specificity of only 13% to 54%, and a positive predictive
value ranging between 69% and 77%. They concluded that clinical prediction
models are not sufficiently accurate to discriminate between patients with or with-
out sleep apnea but could be useful in prioritizing patients for split-night
LABORATORY DIAGNOSIS OF SLEEP APNEA
Attended laboratory-based polysomnography has been and remains a de facto gold
standard for diagnosis of sleep-disordered breathing, even though the utility of a single
overnight recording for diagnosis or exclusion of significant sleep has never been
clearly addressed in the literature. It is clear that there is considerable night-to-night
variability in AHI particularly, when the AHI is low (29–32). Standard overnight
polysomnography involves: (i) recordings of sleep-related electroencephalography
(EEG), electromyography (EMG) of the chin and leg muscles, electrooculography
(EOG), and electrocardiography (ECG); (ii) oxygen saturation; and (iii) measures of
respiratory effort and airflow. Examples of typical polysomnography are shown in
Figure 1. This figure shows clear-cut repetitive obstructive apneic events and for these
FIGURE 1 Overnight polysomnogram showing repetitive obstructive apneas. Channels recorded:
both legs (on single channel); electroencephalogram (EEG) from standard locations left central/right
auricular reference EEG (C3/A2), right central/left auricular reference EEG (C4/A1), left occipital/
right auricular reference EEG (O1/A2); left (LEOG) and right (REOG) electrooculogram; electrocar-
diogram (EKG); oxygen saturation (SaO2); airflow, measured by oro-nasal pressure transducer;
respiratory effort of ribcage and abdomen; and pulse rate. In this 3-minute example, there are at
least five apneas with only modest oxygen desaturation, none below 90%.
Screening and Case Finding
patients a diagnosis of sleep apnea may be established on more simple recordings.
This has led to a number of recording devices ranging from simple oximetry, snoring
sound, respiratory effort, and airflow to full portable attended PSG devices.
PORTABLE MONITORING (SEE ALSO CHAPTER 3)
A number of portable monitoring techniques have been developed over the past
20 years in an attempt to simplify the ambulatory diagnosis of sleep apnea. Some
showing initial promises are either no longer available [i.e., NightwatchTM, Respironics,
Murrysville, Pennsylvania, U.S. (33,34)] or no longer marketed for a diagnostic pur-
pose [i.e., AutoSet®, ResMed, Poway, California, U.S. (35,36)]. Currently available
devices range from comprehensive portable polysomnography to simple oximetry. A
committee of the American Academy of Sleep Medicine, American Thoracic Society,
and American College of Chest Physicians (37) classified portable monitors into three
categories: (i) Type 2 monitors include a minimum of seven channels, including EEG,
EOG, chin EMG, ECG or heart rate, airflow, respiratory effort, and oxygen saturation;
(ii) Type 3 monitors include a minimum of four channels, including ventilation or
airflow (at least two channels of respiratory movement, or respiratory movement and
airflow), heart rate or ECG and oxygen saturation; and (iii) Type 4 monitors, where
most monitors of this type measure a single parameter or two parameters. For com-
parison, Type 1 monitoring is in-laboratory, attended polysomnography.
Type 2 Monitors
A comprehensive monitor has been successfully used in the large-scale Sleep Heart
Health Study (P-Series PS2, Compumedics Limited, Victoria, Australia). Where
home and in-lab PSG were compared, median respiratory disturbance index (RDI)
was similar in the unattended home and attended laboratory setting with differ-
ences of small magnitude in some sleep parameters (38). Differences in RDI between
settings resulted in a rate of disease misclassification that is similar to repeated
studies in the same setting.
The DigiTrace Home Sleep System (DHSS, SleepMed, Inc., Columbia, South
Carolina, U.S.) can acquire, store, and analyze full polysomnographic data in the
ambulatory setting as illustrated in the study by Fry (39). In this study of 77 subjects,
more than 95% of all epochs were scorable for sleep and breathing parameters.
While these data suggest that full polysomnography can be extended to large patient
populations, potentially freeing up valuable lab resources, such an approach has not yet
become widely accepted. One likely reason is the need for much technical expertise.
Therefore simpler, patient-friendly, even patient-applied devices are more desirable.
Type 3 Monitors
The EdenTrace® II Recording System (Nellcor Puritan Bennett Ltd., Kanata,
Ontario, Canada) is a portable monitor that measures nasal and oral air flow via
thermistry, chest wall impedance, snoring intensity, oxygen saturation via finger
pulse oximetry, heart rate, and body position. Movement is detected by electrical
comparison of the signals from the ECG and the pulse oximetry, and discrepancies
between these channels are indicated as ‘‘motion’’ on the saturation channel. Several
studies have been performed comparing either the ambulatory device only in the
laboratory with simultaneously recorded polysomnography, or both home and
laboratory recordings (40–43). In all studies there is good agreement between the
AHI measured in the laboratory and with the EdenTrace device in the ambulatory
setting. Ten percent or more of studies required repetition because of difficulty with
recordings; the study by Whittle et al. (43) suggested that home sleep studies have
benefits in terms of time and cost, but for diagnostic reliability, an in-laboratory
sleep study may be required in more than half of the cases.
The Embletta® Recording System (Medcare, Reykjavik, Iceland) consists of a
nasal pressure detector using a nasal cannulae/pressure transducer system (recording
the square root of pressure as an index of flow), thoracoabdominal movement detec-
tion through two piezoelectric belts, a finger pulse oximeter, and a body position
detector. This technology is part of the parent in-lab PSG system (Embla®). Dingli
et al. (44) performed a synchronous comparison to polysomnography in 40 patients
and a comparison of home Embletta studies with in-laboratory polysomnography in
61 patients. Sleep apnea was classified as definite (AHI > 20), possible (AHI 10–20),
and not present (AHI < 10) based on Embletta results in symptomatic patients.
Using this classification, all nine patients categorized as not having sleep apnea AHI
≤ 15 on PSG and all 23 with definite sleep apnea on Embletta had an AHI ≥ 15 on
polysomnography. Eighteen patients fell into the possible sleep apnea category
potentially requiring further investigation and 11 home studies failed. Most patients
were satisfactorily classified by home Embletta studies but 29 out of 61 required
further investigations. The study suggested a 42% saving in diagnostic costs over
polysomnography if this approach were adopted.
The Stardust® Sleep Recorder (Respironics, Murrysville, Pennsylvania, U.S.)
detects nasal airflow and snoring (pressure sensor); thoracic or abdominal move-
ment (one strain gauge); arterial oxygen saturation and pulse rate (finger probe);
and body position. There are limited published data with this device. One in-lab
study from Japan found that Stardust and PSG AHI correlated well with a mean
bias of 3.7 ± 13.1/hour (45). Specificity was lowest (25%) in patients with milder
sleep apnea (AHI < 15) increasing to 97% with AHI > 50. Sensitivity was high (> 90%)
at any level of PSG-derived AHI. We found similar results (K Ferguson, personal
communication) but much more data are needed to properly determine the role of
this and similar devices.
Type 4 Monitors
Changes in oxygen saturation occur with most, if not all, apneic events. The magnitude
of desaturation depends on a number of factors including end-expiratory lung
volume at the time of the event, baseline saturation, degree of respiratory effort
during the event, and degree of upper airway obstruction (complete or partial).
While changes in saturation can result from obstructive apnea, obstructive hypopnea,
or central apnea, oximetry cannot by itself provide details of the type of sleep-
disordered breathing. Nonetheless, it does give some indication of the frequency
and severity of the sleep-disordered breathing. Many laboratory-based studies
comparing oximetry to full polysomnography have been published with varying
degrees of sensitivity and specificity (46–50). However, they do not provide any
information on the utility of such simple measurements in the ambulatory setting.
The study of Series et al. (51) is one of few comparing ambulatory oximetry with
subsequent polysomnography. Although home oximetry detected only 108 of
176 patients with OSA (positive predictive value, 61.4%), it correctly excluded 62 of
Screening and Case Finding
64 patients (negative predictive value, 96.9%). In a more recent albeit smaller study
by Hussain and Fleetham (52) all (12 of 30) patients with sleep apnea had a 2%
oxygen desaturations index of less than 10/hour. The sensitivity of oximetry
increased at lower desaturations indices but this was associated with decreased
specificity. Review of oximetry waveform pattern, by experienced physicians, did
not improve the diagnostic accuracy. Combining oximetry with a clinical prediction
rule would have reduced the need for polysomnography by 30%. The authors con-
cluded that many patients, who present with snoring and/or witnessed apnea and
are referred to a sleep disorder clinic for suspected OSA, may have significant OSA
even if they deny EDS.
Despite the extensive literature on oximetry, there is no uniformity in the results.
This can be explained by the fact that different devices record at different sampling
rates, store and analyze the data in different ways. Moreover, and perhaps more
importantly, the definition of sleep apnea-hypopnea is not uniform amongst studies.
Oximetry and Snoring
The Calgary group has added a measurement of snoring to saturation detection in
an attempt to improve the ambulatory diagnosis of sleep apnea. In an initial report,
they compared the SnoreSat® (SagaTech Electronics, Calgary, Alberta, Canada; now
known as the Remmers Sleep Recorder) with standard overnight polysomnography (53).
Depending on the severity of apnea and the referral population, the sensitivity and
specificity of the monitor in detecting OSA ranged between 84% and 90% (sensitivity)
and 95% and 98% (specificity). In subsequent studies comparing both at-home with
simultaneous in-laboratory measurements (54), the PSG-derived AHI and oximeter-
derived RDI were highly correlated (R = 0.97). The mean (2SD) of the differences
between AHI and RDI was 2.18 (12.34)/hour. The sensitivity and specificity of the
algorithm depended on the AHI and RDI criteria selected for OSA case designation.
Using a cut-off of 15/hour for AHI and RDI, the sensitivity and specificity were 98%
and 88%, respectively. If the PSG-derived AHI included EEG-based arousals as part
of the hypopnea definition, the mean (2SD) of the differences between RDI and AHI
was −0.12 (15.62)/hour and the sensitivity and specificity profile did not change
significantly. While these data suggest that oximetry, or oximetry combined with
snoring measurement, may be used in identifying patients with OSA, it is important
to remember that not all oximeters are made equally and that some events may not
be detected by some devices in some situations (55–57). Indeed, the same group
found that oximetry is not useful in a pediatric population (58). Using different
devices with different algorithms have shown different results and clinicians need
to be aware of these limitations when interpreting the literature.
Snoring is added to oximetry in a number of other devices included MESAM
IV® (Madaus Medizin-Elektronik, Cologne, Germany) and the newer ARES™
Unicorder (Apnea Risk Evaluation System, Advanced Brain Monitoring, Inc.,
Carlsbad, California, U.S.) (heart rate is also recorded; thus these do not technically fit
with the classification but are included for completeness). The MESAM IV recording
device, widely used in Europe, evaluates sleep-disordered breathing based on an
analysis of snoring and saturation change as well as heart rate. Cyclical variation of
heart rate in association with decreases in saturation and snoring are taken into
account at the same time to determine sleep apnea. Several previous studies evalu-
ating the diagnostic validity of MESAM IV have had conflicting results (59–63). This
is because not all studies have the same design, patient populations varied and
diagnostic criteria were different.
The ARES™ Unicorder is easily affixed to the forehead by the user and acquires
data on oxygen saturation, airflow (nasal pressure), pulse rate, snoring level (micro-
phone), and head position/movement (accelerometers). Proprietary ARES™ soft-
ware uses oxygen saturation measured by pulse oximetry (SpO2) as the primary
signal, and analyzes changes in pulse rate, snoring sounds, head movement, and
the slope of the resaturation curve to identify behavioral markers of arousal that
follow desaturation events. The analysis algorithm assumes that desaturation events
are terminated by arousal due to sleep apnea. A study by Westbrook (64) reported
284 valid comparisons of in-laboratory simultaneous polysomnography and ARES™
and 187 valid comparisons of in-laboratory polysomnography with a separate two
nights of unattended self-applied ARES™ recording. Using a diagnostic AHI cut-off
> 10 to establish the accuracy and validity of the ARES™, the concurrent in-labora-
tory comparison yielded a sensitivity of 97.4%, a specificity of 85.6%, a positive predic-
tive value of 93.6%, and a negative predictive value of 93.9%; in-home comparison
sensitivity, specificity, positive predictive value, and negative predictive value were
91.5, 85.7, 91.5, and 85.7%, respectively. The authors concluded that the ARES™ dem-
onstrates a consistently high sensitivity and specificity for both in-laboratory and in-
home recordings; and that for patients at risk for sleep apnea who do not a priori
need an attended study, the ARES™ could provide a low-cost alternative to tradi-
Oximetry and Peripheral Arterial Tonometry
A wrist-worn device, Watch-PAT 100 System (Itamar Medical Ltd., Caesarea, Israel)
has been developed to detect OSA. The peripheral arterial tonometry (PAT) technol-
ogy is based on the principle that episodic vasoconstriction of digital vascular beds
from sympathetic stimulation (mediated by alpha receptors) causes attenuation of
the PAT signal and that discrete obstructive airway events (e.g., apneas, hypopneas,
and upper airway resistance) cause arousals from sleep, sympathetic activation, and
peripheral vasoconstriction (65). Thus, this represents a noninvasive measurement
of variable sympathetic activation that occurs as part of sleep-disordered breathing
events. Two finger probes extend from the main body of the device: one is the
pneumo-optical sensor that detects the PAT signal; the other measures arterial
oxygen saturation. The body of the device also contains an actigraph, which is used
to estimate sleep. The finger-mounted pneumo-optical sensor eliminates venous
pulsations and continuously measures the pulse volume of the digit. An automated
computerized algorithm is used to calculate the frequency of respiratory events per
hour of actigraphy-measured sleep. This algorithm also incorporates the PAT signal
attenuation and the oxygen desaturation. Ayas et al. (66) found good correlation
between the Watch-PAT 100 System and the gold standard PSG. Schnall et al.
(67) found a high correlation between standard polysomnography-scored apnea-
hypopnea events and PAT-vasoconstriction events with concurrent tachycardia in
an initial study with the bedside version of the system. Bar et al. (68) showed that
detection of apnea and hypopnea events based on combined data from PAT and
pulse oximetry was highly correlated with standard polysomnography-scored
results, a finding confirmed by Pittman et al. (69) using both manual and automatic
analysis. A more recent study from Sweden compared the Watch-Pat 100 System
with simultaneous in-home unattended full PSG (70). Subjects were instrumented
in the lab for full PSG (using the Embla system, Medcare, Reykjavik, Iceland)
and the Watch-Pat 100 System and sent home for overnight study. The accuracy
of the Watch-Pat 100 System in RDI, AHI, oxygen desaturation index (ODI), and
Screening and Case Finding
sleep-wake detection was assessed by comparison with data from simultaneous
PSG recordings. The mean PSG-AHI in this population was 25.5 ± 22.9 events per
hour. The Watch-Pat 100 System RDI, AHI, and ODI correlated closely at 0.88, 0.90,
and 0.92, respectively ( p < 0.0001) with the corresponding indexes obtained by PSG.
The AUC for the ROC curves for Watch-Pat 100 System AHI and RDI were 0.93 and
0.90 for the PSG-AHI and RDI thresholds 10 and 20, respectively ( p < 0.0001). The
agreement of the Sleep-Wake assessment based on 30-second bins between the two
systems was 82 ± 7%. The authors concluded that the Watch-Pat 100 System was
reasonably accurate for unattended home diagnosis of OSA in a population sample
not preselected for OSA symptoms. Moreover, they proposed simultaneous home
PSG recordings in population-based cohorts as a reasonable validation standard for
assessment of simplified recording tools for OSA diagnosis.
Oximetry and Flow
A number of devices combining airflow measurement with oximetry are available
primarily in Europe. These include the Reggie (oximetry, airflow, actigraphy (71);
Camtech Ltd., Sandvika, Norway) and the SOMNOcheck® (oximetry, flow, snoring,
heart rate and position (72); Weinmann Diagnostics GmbH and Co. KG, Hamburg,
Germany). As with other devices reasonable agreement with in-lab PSG have been
reported but limited data prevent widespread use or endorsement at this time.
Tracheal Sound Analysis
Recognizing that a suprasternal pressure transducer can accurately reflect respira-
tory efforts (73), Nakano et al. (74) have developed a novel method of using tracheal
sound analysis for the diagnosis of sleep apnea-hypopnea syndrome. In a retrospec-
tive study involving 383 patients for suspected sleep apnea-hypopnea syndrome,
overnight polysomnography with simultaneous tracheal sound recording was per-
formed. The AHI was calculated as the number of apnea and hypopnea events per
hour of sleep. Tracheal sounds were digitized and recorded as power spectra. An
automated computer program detected transient falls (TS-dip) in the time series of
moving average of the logarithmic power of tracheal sound. Tracheal sound-respi-
ratory disturbance index (TS-RDI) was reported as the number of
TS-dips per hour of examination and the ODI was calculated as the number of SaO2
dips of at least 4% per hour of examination. The TS-RDI highly correlated with AHI
(r = 0.93). The mean (± SD) difference between the TS-RDI and AHI was −8.4 ± 10.4.
The diagnostic sensitivity and specificity of the TS-RDI when the same cut-off value
was used as for AHI were 93% and 67% for the AHI cut-off value of 5, and 79% and
95% for the AHI cut-off value of 15, respectively. The agreement between the
TS-RDI and AHI was better than that between the ODI and AHI. The authors
concluded that fully automated tracheal sound analysis is useful for the portable
monitoring of the sleep apnea-hypopnea syndrome.
OTHER DIAGNOSTIC APPROACHES
Actigraphy refers to methods using miniaturized computerized wristwatch-like
devices to monitor and collect data generated by movements. Most actigraphs con-
tain an analog system to detect movements. In some devices, a piezoelectric beam
detects movement in two or three axes and the detected movements are translated to
digital counts accumulated across predesigned epoch intervals (e.g., 1 minute) and
stored in the internal memory. While these have been very useful in determining
disorders of the sleep-wake rhythm (75) and are even quite good in estimating sleep
time in patients with sleep apnea (76), they cannot really be used to diagnose
sleep apnea (77).
Other surrogate markers have been used to detect sleep apnea including
cyclical changes in heart rate on 24-hour Holter monitoring (78) and pulse transit
time (79–81). While both of these reflect physiologic changes accompanying obstruc-
tive events, the sensitivity and specificity of these are insufficient (owing to wide
extremes in spectrum of disease) to warrant routine use.
In assessing the validity and applicability of any ambulatory diagnostic
system, several standards should be met (82). These include: (i) an independent
blind comparison with a reference standard; (ii) an appropriate spectrum of patients;
(iii) avoidance of work-up bias; (iv) methods for performing the test described in
detail, allowing for duplication of the study; (v) adequate description of study
population; (vi) adequate sample size (conservative estimate is that there should be
greater than or equal to 200 patients in the study, approximately equally divided
between those with and without the condition. This allows for confidence intervals
of approximately ± 10% for estimates of sensitivity and specificity); (vii) avoidance
of a selection bias—in the case of patients with sleep apnea, this means a consecu-
tive sample of patients referred to a sleep clinic rather than those referred to a sleep
laboratory, avoiding filtering of patients; and (viii) adequate description of study
setting and appropriate setting—for instance, basic descriptors of the study should
include whether it is a tertiary referral sleep clinic or a community sleep clinic, types of
physicians referring to the sleep clinic, population base, types of patients, and so on.
Because most portable monitors are intended for use outside of the sleep labo-
ratory, this is the setting in which they should be studied. Still, there are very few
studies that meet these criteria.
Polysomnography remains the standard for diagnosis of sleep apnea and other
disorders of sleep. However, depending on the prevalence of sleep apnea in the
population in question and the diagnostic device used, an ambulatory strategy
could easily be adopted. Patients can be stratified according to history (symptoms),
physical examination, and clinical prediction strategies. The probability of sleep
apnea can be estimated, and when there is a moderate-to-high probability, portable
monitoring can confirm the suspicion and subjects can immediately go on to treat-
ment. If there is no sleep apnea and patients are asymptomatic, it can be argued that
no further testing is necessary and only follow-up is required. Symptomatic patients,
regardless of their pretest probability, would go on to polysomnography. If the
pretest probability for sleep apnea is low, polysomnography would be used only for
symptomatic patients. While we await long-term outcome studies of such an
approach, individual clinicians will have to apply this algorithm within the confines
of the local resources, patient expectations, and clinical practice.
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35 Download full-text
Polysomnography and Cardiorespiratory
Michael R. Littner
V A Greater Los Angeles Healthcare System, Sulpulveda, California and
David Geffen School of Medicine, University of California, Los Angeles,
The obstructive sleep apnea-hypopnea syndrome (OSA) is recognized pre-
dominantly by daytime somnolence and night-time snoring often in obese individ-
uals (1,2). The diagnosis is confirmed by demonstrating a sufficient number of
obstructive apneas (absence of airflow with continued respiratory effort) and/or
obstructive hypopneas (reduction in airflow despite sufficient respiratory effort to
produce normal airflow) (1). The daytime somnolence appears to result, in large
part, from short, amnestic arousals that fragment and reduce the efficiency of sleep.
OSA appears to affect about 4% of men and 2% of women between 30 and 60 years
of age (3). OSA is associated with systemic hypertension, myocardial infarction,
motor vehicle accidents, and cerebrovascular accidents (4–7).
Daytime somnolence is a nonspecific symptom and may be due to narcolepsy,
insufficient sleep, and idiopathic hypersomnia among other conditions (2). In addi-
tion, snoring is a nonspecific finding; for example, 67% of obese patients [body mass
index (BMI) ≥ 30] who snored loudly (patient report) had OSA (8). The general non-
specificity of daytime sleepiness and snoring requires objective measurement of
apneas and hypopneas during sleep for confirmation of OSA.
In general, confirmation involves an overnight sleep study while monitoring
a number of respiratory channels (nasal and oral airflow, chest wall and abdominal
movement, and oximetry), sleep staging by electroencephalogram (EEG) (central
and occipital electrodes usually referenced to the ear), electro-oculogram (right and
left eye movement) and chin electromyogram, at least a one-lead electrocardiogram,
as well as leg movements (bilateral anterior tibialis electrodes) which may also pro-
duce frequent arousals (9). The study is attended by a technician (poly somnographic
or sleep technologist) to perform and observe the study, ensure quality and safety,
and make needed interventions including application of the most frequently used
therapy, continuous positive airway pressure (CPAP). This approach is called
The number of potential patients usually exceeds the number of sleep laboratory
facilities capable of performing the test in a timely fashion. The labor intensity of the
attendant, scoring and interpretation of the study, and cost of the space and equipment
make PSG relatively expensive, typically costing $1000 or more per study (10).
To increase access to diagnosis and potentially reduce cost, there has been an
effort to produce systems that incorporate part or all of the PSG but make it portable
and ideally usable without an attendant technician. The ideal system would measure
the minimum number of channels necessary, be self-contained and self-administered
by the patient, be amenable to rapid and accurate scoring, and provide information