uva-dare (digital academic repository) sleep …...osa are snoring, gasping for air, restless sleep,...
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Sleep apnea in stroke: Diagnosis, consequences & treatment
Aaronson, J.A.
Link to publication
Citation for published version (APA):Aaronson, J. A. (2016). Sleep apnea in stroke: Diagnosis, consequences & treatment.
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Download date: 26 Aug 2020
Printing of this thesis was kindly supported by Heliomare Research & Development, University of Amsterdam, ApneuVereniging, Nederlandse Vereniging voor Slaap- en Waak onderzoek, SomnoMed Goedegebuure, SnörEx, SomnoClinic, Heinen + Lowenstein Benelux and TotalCare.
Cover design: Richard FickertBack cover text: "A good laugh and a long sleep are the two best cures for anything" - Irish proverb
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© J.A. Aaronson, 2015
All rights are reserved. No part of this thesis may be reproduced or transmitted in any form or by any means, without the prior permission in writing of the author.
The research described in this thesis was carried out in collaboration with Heliomare
Research & Development and the University of Amsterdam.
The work presented in this thesis was financially supported by Heliomare Research &
Development. Part of the thesis was financially supported by the Netherlands Organization
for Health Research and Development (ZonMw).
SLEEP APNEA IN STROKEDiagnosis, consequences & treatment
ACADEMISCH PROEFSCHRIFT
ter verkrijging van de graad van doctor
aan de Universiteit van Amsterdam
op gezag van de Rector Magnificus
prof. dr. D.C. van den Boom
ten overstaan van een door het College
voor Promoties ingestelde commissie,
in het openbaar te verdedigen in de Agnietenkapel
op vrijdag 22 januari 2016, te 14.00 uur
door
Justine Anna Aaronson
geboren te Amsterdam
Promotores: Prof. dr. B.A. Schmand Universiteit van Amsterdam
Prof. dr. C.A.M. van Bennekom Universiteit van Amsterdam
Copromotor: Dr. W.F. Hofman Universiteit van Amsterdam
Overige leden: Dr. J.G. van den Aardweg Medisch Centrum Alkmaar
Dr. J.M. Karemaker Academisch Medisch Centrum
Prof. dr. R.P.C. Kessels Radboud Universiteit
Prof. dr. J.M.J. Murre Universiteit van Amsterdam
Prof. dr. G.M. Ribbers Erasmus MC
Prof. dr. Y.B.W.E.M. Roos Universiteit van Amsterdam
Faculteit der Maatschappij- en Gedragswetenschappen
TABLE OF CONTENTS
Chapter 1 General introduction 7
Chapter 2 Neuropsychological functioning after CPAP treatment 19
in obstructive sleep apnea: a meta-analysis
Chapter 3 Diagnostic accuracy of nocturnal oximetry for 37
detection of sleep apnea in stroke rehabilitation
Chapter 4 Can a prediction model combining self-reported 45
symptoms, sociodemographic and clinical features
serve as a reliable first screening method for sleep
apnea syndrome in patients with stroke?
Chapter 5 The effect of obstructive sleep apnea and treatment 59
with continuous positive airway pressure on stroke
rehabilitation: rationale, design and methods
of the TOROS study
Chapter 6 Obstructive sleep apnea is related to impaired 73
cognitive and functional status after stroke
Chapter 7 Effects of continuous positive airway pressure 89
on cognitive and functional outcome of stroke
patients with obstructive sleep apnea:
a randomized controlled trial
Chapter 8 Sleep apnea in stroke: summary and general discussion 113
Appendix Neuropsychological tests by cognitive domain 127
Addendum Sleep apnea in stroke: Nederlandse samenvatting 135
Dankwoord 143
List of abbreviations 149
List of publications & presentations 153
Curriculum vitae 157
Sponsors 159
GENERAL INTRODUCTION
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1STROKEStroke is defined as rapidly developing clinical signs of focal (or global) disturbance of cerebral
function, with symptoms lasting 24 hours or longer or leading to death, with no apparent
cause other than of vascular origin.1 This disturbance can either be caused by ischemia or
hemorrhage. The clinical manifestation of stroke is diverse. The most common clinical
consequences include impaired motor functioning and paralysis, visual field defects, cognitive
impairments such as concentration problems, memory deficits, aphasia and neglect, and
emotional problems such as depression and anxiety, and post-stroke fatigue.2,3 These deficits
often lead to difficulties in daily functioning and decreased quality of life after stroke.4
Stroke is one of the most frequent causes of death and long-term disabilities
worldwide.5 In the Netherlands, every year around 45.000 people are hospitalized with
a stroke.6 Depending on the type of stroke the one-month mortality rate is between
21% and 49% percent. Five years after stroke this is approximately 50% for all types
of stroke combined.7 Moreover, after one year of survival, stroke patients have a 20%
risk of suffering from a recurrent stroke.8 After hospitalization approximately 65% of the
surviving stroke patients are discharged home. If needed, they receive follow-up treatment
in the community or at outpatient rehabilitation clinics. Around 10% of the patients
are discharged to an inpatient rehabilitation center for an intensive neurorehabilitation
program. In the remaining 25% of patients admission to a nursing home is indicated,
because intensive neurorehabilitation is not (yet) feasible. Their stay can be either
temporary or permanent.9 One third of all patients is still care dependent one year post
stroke, and up to 42% is still not able to live fully independently after 5 years.10,11 The
total costs of stroke care in the Netherlands are assessed at 2,3 billion euros per year. This
equals 2,5 percent of the total healthcare budget.12
Due to a rapidly aging population and the improved acute treatment of stroke, the
number of surviving stroke patients is only expected to increase in coming years. According
to the World Health Organization the reduction of stroke related death and disability will
largely have to come from preventive methods and not from cure.5 Stroke prevention has
therefore become a hot topic. The American Heart Association has published elaborate
guidelines on the prevention of modifiable risk factors such as high blood pressure,
smoking, diabetes mellitus, atrial fibrillation, dyslipidemia, poor diet, physical inactivity
and obesity.13 Secondly, in recognition of the morbidity of recurrent stroke, evidence-
based recommendations for the prevention of future stroke among survivors have also
been published recently.14 In both guidelines potential new modifiable risk factors are
incorporated. One of these newly incorporated risk factors is obstructive sleep apnea.
OBSTRUCTIVE SLEEP APNEA Sleep apnea is a type of sleep related breathing disorder, a spectrum also including sleep
related hypoventilation and hypoxia. Sleep apnea is characterized by repetitive cessations of
breathing during sleep. There are three forms of sleep apnea, obstructive sleep apnea (OSA),
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1 central sleep apnea (CSA) and mixed sleep apnea (a combination of both). In OSA breathing
is interrupted due to the obstruction of the upper airway, despite an ongoing effort to
breathe, while in CSA breathing cessations are caused by lack of respiratory effort. Complete
pauses of breathing are called apneas and partial reductions of breathing hypopneas. The
severity of sleep apnea is expressed by the mean number of apneas and hypopneas per hour
sleep, the apnea-hypopnea index (AHI), and is by convention classified as mild, moderate
or severe (AHI, respectively ≥5; ≥15; ≥30).15 OSA is by far the most prevalent type of sleep
apnea and therefore we will subsequently focus on OSA. OSA leads to oxygen desaturations,
hypercapnia, increases in blood pressure and sleep fragmentation.16 Nighttime symptoms of
OSA are snoring, gasping for air, restless sleep, sweating and nocturia. Commonly reported
daytime symptoms are waking up unrefreshed, morning headaches, excessive daytime
sleepiness, irritability, depressed mood, and cognitive complaints of concentration and
memory loss. Studies on the effect of OSA on cognitive functioning confirm impairments
in the domains of attention and memory, and also report impairments in the domains of
vigilance, executive functioning, and motor coordination.17,18
The prevalence of moderate to severe OSA in the general population is estimated
between 3-17% depending on age and gender, with higher prevalence in older age and
men.19 Other important risk factors for OSA are obesity, anomalies of the upper airway,
smoking and alcohol consumption.20 In turn, OSA itself is also not without consequences;
it has been associated with an increased risk of stroke or death from any cause, and this
increase is independent of other cardiovascular risk factors, including hypertension.21
Clinical assessment of OSA often includes history of OSA symptoms, physical
examination and screening questionnaires such as the Epworth sleepiness scale or the
Berlin questionnaire.22,23 Screening with nocturnal oximetry, a method to monitor the
oxygen saturation during sleep, is also common practice.24 However, for the true diagnosis
of OSA polysomnography is considered the golden standard. Polygraphy is also often
applied, as it is less burdensome and costly, and requires less technical expertise. Clinical
guidelines for the use of polygraphy for diagnosis of OSA have been published.25 These
guidelines are based on a large number of validation studies and recommend to restrict
the use of polygraphy to patients with a high a priori test probability of moderate to severe
OSA. Both sleep recordings include airflow, oxygen saturation, respiratory effect and body
position. Additionally, polysomnography includes recordings of electroencephalogram
(EEG), electrooculogram (EOG), electromyogram (EMG) and electrocardiogram (ECG).
TREATMENT OF OSAThere are a number of treatment options for OSA, including conservative measures such
as weight loss and cessation of smoking, positional therapy, mandibular repositioning
appliances and surgery. Continuous positive airway pressure (CPAP) is, however, the
treatment of choice for OSA. CPAP is a respiratory ventilation treatment that corrects
the respiratory disturbances by blowing pressurized air into the upper airway through a
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1mask. This leads to improved breathing, better blood oxygen saturation and less sleep
fragmentation during sleep.26 Consequently, it is expected that CPAP treatment will
improve daytime functioning. A Cochrane meta-analysis showed that CPAP significantly
reduces AHI, objective and subjective sleepiness, and depressive symptoms.27 The effect
on other daytime consequences such as impaired cognitive functioning is still less clear. A
number of reviews have documented partial reversibility of cognitive dysfunction after CPAP
treatment, mostly in the domains of vigilance, attention and executive functioning.17,28,29
However, these reviews used qualitative methods and included a number of different study
designs. Their conclusions, therefore, differed widely with respect to the domains in which
recovery was found as well as the extent of recovery.17,28,29 To quantify the magnitude of
the effect of CPAP treatment in the different cognitive domains more precisely, a meta-
analysis, employing stringent inclusion criteria, would be needed.
Although CPAP has been found to clearly improve sleep quality and functional
outcome, the clinical effectiveness in daily practice seems to be more limited due to low
acceptance, poor tolerance and subsequent compliance problems. The estimation of non-
compliance ranges from 46 to 83% of patients, when non-compliance is defined as a
mean of less than 4 hours of use per night.30 It seems that patients either tolerate CPAP
from the beginning or do not learn to tolerate it at all. Most patients who show long-term
compliance are adherent within the first week of treatment, with compliance generally
gradually increasing over time.31,32 Besides early adherence, higher levels of self-reported
daytime sleepiness, greater OSA severity as well as better problem solving skills, a positive
attitude towards CPAP treatment, and higher self-efficacy are all associated with better
long-term compliance.30,33,34 Supportive interventions, such as frequent contact and follow-
up, education and cognitive behavioral therapy seem to improve CPAP compliance.35
OSA AND STROKEOSA is the most common sleep disorder in the stroke population, with reported prevalence
rates between 30% and 70% depending on the OSA severity.36 As mentioned above, OSA
has been identified as an independent risk factor for first-time and recurrent stroke. A large
community based study found that moderate to severe OSA in men is associated with a
three-fold increase risk of ischemic stroke.37 Despite this high prevalence of OSA in the stroke
population and the good treatment options, OSA is often left undiagnosed and untreated.38,39
Main causes of under-diagnosis are lack of awareness of health care professionals, lack of
complaints by patients, and difficult access to sleep laboratory-based testing.40 Screening
instruments such as the Epworth sleepiness scale or the Berlin questionnaire seem less
suitable for early recognition of OSA in the stroke population, while the use of nocturnal
oximetry as screening instrument has yet to be validated in stroke patients.41,42
Since OSA leads to impairments of daily functioning in otherwise healthy patients, it
has been hypothesized that untreated OSA contributes to less optimal recovery from stroke.
This hypothesis is supported by a number of studies that showed that OSA is associated with
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1 poor functional recovery, prolonged hospitalization and higher mortality rates.43-46 To date,
the effect of OSA on other aspects of daily functioning such as cognition, sleepiness and
mood have not received much attention in the stroke population. In the case of cognition,
only a small number of studies looked into the association between OSA and cognitive
functioning using a global cognitive screening instrument (Mini Mental State Examination;
MMSE47).43,44,48 One of these studies found a weak association between indirect measures
of OSA (nocturnal oxygen desaturation) and the MMSE, while two other studies found no
relationship. In one of these latter studies a possible explanation for the non-findings was
suggested; the authors noted that the MMSE is not designed to detect subtle cognitive
changes and recommended using more sensitive neuropsychological tests.44
Prior to the research project of this thesis, a small pilot study on the effect of OSA
on cognitive functioning and mood in stroke patients was performed in Heliomare.49 In
this study, the association between OSA severity and neuropsychological performance
measured with a number of specific tests was assessed. It was found that OSA is associated
with lower performance on tests of processing speed, attention, verbal memory, visual
scanning and higher levels of depressive symptoms. In another study, in a larger group of
patients with traumatic brain injury, similar results were reported. This study found that
OSA is associated with greater impairment of sustained attention and memory.50 Only two
studies investigated the effect of OSA on excessive daytime sleepiness in stroke patients and
they reported contradictory results.43,45 For mood, only one study reporting an association
between OSA and depressed mood in geriatric stroke patients was found.48 Considering
the small number of studies and the limited use of sensitive cognitive measures, studies
in larger groups of stroke patients using elaborate neuropsychological assessment of
cognition are required to better understand the possible negative implications of OSA on
daily functioning after stroke.
CPAP TREATMENT OF OSA IN STROKE The research field of CPAP treatment in stroke patients with OSA is still relatively young.
The first study on the subject was published in 2001, and in a recent review by Tomfohr and
colleagues a PubMed search revealed only 17 studies.51,52 These studies were heterogeneous
with regard to study design, timing of treatment onset, and duration of treatment, as well
as the chosen outcomes measures, making it difficult to generalize among studies. Of
the 17 studies, nine were observational, seven studies randomized patients to CPAP or
treatment as usual, while one study compared CPAP treatment to sham CPAP. The authors
of this review conclude that there is some preliminary evidence that long term CPAP
treatment may be associated with delayed onset of new cardiovascular incidents.
Moving to the implications for daily functioning, they conclude that none of the
studies have found improvements in activities of daily living (ADL) or cognitive functioning,
and that studies on the beneficial effect of CPAP on neurological recovery, daytime
sleepiness and depressive symptoms are still inconclusive. More specific, three studies
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1found improvement after CPAP on neurological status, while two others did not.53-57 The
two latter studies suffered from low compliance and insufficient power, which might have
hampered their findings. The effect on cognitive functioning was investigated in three
studies. However, as with the association between cognitive functioning and OSA, two
studies only used the MMSE as a measure of cognition.57,58 In the other study specific
neuropsychological measures of vigilance and executive functioning were assessed.55
Again it could be argued that the lack of findings on cognitive measures may be partially
explained by lack of sensitivity of these tests.
The studies discussed above suffered from a number of limitations. First of all, patients’
enrollment in the studies was a major issue; on average 70% of the stroke patients, who
were considered for inclusion into a randomized controlled trial (RCT), suffered from at
least one exclusion criteria. Furthermore, around 50% of the eligible patients declined
to participate in the RCT. Second, compliance was a challenge in this population. The
adherence rates of stroke patients assigned to CPAP ranged from 10% to 90% at follow
up, with an overall compliance around 50%. The main reasons for low adherence reported
by patients are summarized in the review by Tomfohr and colleagues52 and include
stroke-related impairment, mask discomfort, sleep disturbances, and claustrophobia.
Additionally, they found that many patients refused initial treatment because they were
too overwhelmed by the stroke and associated recovery activities.52 Another problem that
was mentioned is unfamiliarity and resistance to CPAP treatment of nursing staff on the
neurorehabilitation unit and of primary care-givers when patients are discharged home.
This may translate in even poorer compliance, as patients often need assistance to initiate
CPAP treatment and to continue treatment at home. Training of the nursing staff as well
as education of the primary care givers therefore seem important ingredients for the
improvement of CPAP compliance.
GENERAL AIM OF THIS THESISOSA is the most common sleep disorder in stroke, but is often left unrecognized and
untreated. It has been suggested that OSA impedes stroke recovery, and a number of
studies support this hypothesis. However, the extent to which OSA hampers the different
aspects of daily functioning is still unclear. Furthermore, CPAP treatment of OSA in the
general OSA population elicits improvement of daily functioning, but the effect of CPAP
treatment on stroke recovery is inconclusive. This seems at least partly due to the small
number of studies in this population, the use of insensitive outcome measures, as well
as the low compliance within these studies. The overall aim of the studies described in
this thesis is to improve our understanding of the effects of OSA and its treatment on
the recovery of stroke patients. In order to overcome the above-mentioned limitations,
we aimed to include a large group of stroke patients, to employ more sensitive outcome
measures, and to improve compliance by offering treatment by a well-trained and
specialized team of nurses and physicians.
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1 More specifically, the three main aims of this thesis were:
1. To improve early recognition of sleep apnea in stroke patients during inpatient
rehabilitation.
2. To examine the effects of OSA on daily functioning of stroke patients, and more
specifically on cognitive and functional status, at admission to stroke rehabilitation.
3. To investigate whether CPAP treatment can ameliorate the recovery of cognitive and
functional outcome after stroke.
THESIS OUTLINEIn Chapter 2 we performed a meta-analysis to quantify the effect of CPAP treatment
on neuropsychological functioning in the general OSA population. We included thirteen
RCT studies based on a literature search from January 1990 to July 2012. Multiple
neuropsychological tests were used across studies to assess cognitive functioning. We
therefore classified all neuropsychological tests into seven cognitive domains, including
processing speed, attention, vigilance, working memory, memory, verbal fluency and
visuoconstruction, and calculated the effect of CPAP treatment per domain.
Chapter 3 describes the diagnostic accuracy of nocturnal oximetry for screening of
sleep apnea in the stroke population. In this study, we analyzed the sensitivity, specificity,
and positive and negative predictive values of the nocturnal oximetry in stroke patients for
different cut-offs of sleep apnea severity. In Chapter 4 we examined whether a prediction
model combining self-reported symptoms, socio-demographic and clinical parameters
could serve as a reliable first screening method in a step-by-step diagnostic approach to
sleep apnea in stroke rehabilitation. We classified stroke patients into low or high likelihood
of sleep apnea, built a prediction model using backward multivariate logistic regression
and evaluated diagnostic accuracy using receiver operating characteristic analysis.
Chapter 5 describes the study design of the Treatment of OSA and Rehabilitation
Outcome in Stroke (TOROS) study. The results of this study are presented in the following
two chapters. In Chapter 6 the case-control part of the TOROS study is presented. In this
study, we compared stroke patients with OSA to stroke patients without OSA on cognitive
and functional status. For cognition, we administered an elaborate neuropsychological
examination consisting of nine cognitive domains, while functional status was determined
by two neurological scales and a measure of functional independence. Chapter 7 reports
on the effects of CPAP treatment on stroke recovery. In a RCT design stroke patients
were randomized to four weeks of CPAP treatment or to treatment as usual. Before and
after the four-week intervention the cognitive and functional status were assessed. We
compared the improvement of the CPAP group to the group that received treatment as
usual, to determine whether CPAP had a beneficial effect on stroke recovery.
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148 Sandberg O, Franklin K, Bucht G, Gustafson Y. Sleep apnea, delirium, depressed mood, cognition, and ADL ability after stroke. JAGS 2001:49,391-397.
49 Jacobs J, Groet E, Schmand B. De invloed van het slaapapneusyndroom op het cognitieve functioneren bij CVA-patiënten: Een verkennend onderzoek. Tijdschrift voor Neuropsychologie 2008;6:131–137.
50 Wilde MC, Castriotta RJ, Lai JM, Atanasov S, Masel BE, Kuna ST. Cognitive impairment in patients with traumatic brain injury and obstructive sleep apnea. Arch Phys Med Rehabil 2007;88:1284-1288.
51 Wessendorf T, Wang Y, Thilmann A, Sorgenfrei U, Konietzko N, Teschler H. Treatment of obstructive sleep apnoea with nasal continuous positive airway pressure in stroke. Eur Respir J 2001;18:623–629.
52 Tomfohr LM, Hemmen T, Natarajan L, Ancoli-Israel S, Loredo JS, Heaton RK, Bardwell W, et al. Continuous Positive Airway Pressure for Treatment of Obstructive Sleep Apnea in Stroke Survivors What Do We Really Know? Stroke 2012;43:3118-3123.
53 Bravata DM, Concato J, Fried T, Ranjbar N, Sadarangani T, McClain F, Struve F, et al. Continuous positive airway pressure: Evaluation
of a novel therapy for patients with acute ischemic stroke. Sleep 2011;34:1271-1277.
54 Parra O, Sanchez-Armengol A, Bonnin M. Early treatment of obstructive sleep apnoea and stroke outcome: a randomised controlled trial. Eur Respir J 2011;37:1128-1136.
55 Ryan CM, Bayley M, Green R, Murray BJ, Bradley TD. Influence of continuous positive airway pressure on outcomes of rehabilitation in stroke patients with obstructive sleep apnea. Stroke 2011;42:1062-1067.
56 Hsu C, Vennelle M, Li H, Engleman HM, Dennis MS, Douglas NJ. Sleep-disordered breathing after stroke: a randomised controlled trial of continuous positive airway pressure. J Neurol Neurosurg Psychiatry 2006;77:1143-1149.
57 Brown DL, Chervin RD, Kalbfleisch JD, Zupancic MJ, Migda EM, Svatikova A, ConCannon M, Martin C, Weatherwax KJ, Morgenstern LB. Sleep apnea treatment after stroke (SASTS) trial: Is It feasible? J Stroke Cerebrovas Dis 2013;22:1216-1224.
58 Sandberg O, Franklin KA, Bucht G, Eriksson S, Gustafson, Y. Nasal continuous positive airway pressure in stroke patients with sleep apnoea: a randomized treatment study. Eur Respir J. 2001;18:630-634.
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Kylstra WA*, Aaronson JA*, Hofman WF, Schmand BA
* These authors contributed equally to this manuscript
Sleep Medicine Reviews 2013; 17:341-347
NEUROPSYCHOLOGICAL FUNCTIONING AFTER CPAP
TREATMENT IN OBSTRUCTIVE SLEEP APNEA: A META-ANALYSIS
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ABSTRACTThe generally held clinical view is that treatment with continuous positive airway pressure
(CPAP) improves cognition in patients with obstructive sleep apnea (OSA). However, the
cognitive domains in which recovery is found differ between studies. A meta-analysis
was conducted to quantify the effect of CPAP treatment in OSA on neuropsychological
functioning. A literature search of studies published from January 1990 to July 2012
was performed. The inclusion criteria were: randomized controlled trial, diagnosis of
OSA by poly(somno)graphy, apnea/hypopnea index, duration and compliance of CPAP
treatment reported, use of one or more standardized neuropsychological tests. Mean
weighted effect sizes of CPAP treatment for seven cognitive domains were calculated,
including processing speed, attention, vigilance, working memory, memory, verbal fluency
and visuoconstruction. Thirteen studies encompassing 554 OSA patients were included.
A small, significant effect on attention was observed in favor of CPAP (d = 0.19). For
the other cognitive domains the effect sizes did not reach significance. Improvement on
measures of sleepiness was modest (d = 0.30-0.53) and comparable to prior research.
In conclusion, this meta-analysis indicates that the effect of CPAP on cognition is small
and limited to attention. Contrary to the general assumption, only slight improvement of
neuropsychological functioning after CPAP treatment can be expected.
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INTRODUCTIONObstructive sleep apnea (OSA) is characterized by complete cessations (apneas) and partial
decreases (hypopneas) in respiration caused by pharyngeal collapse during sleep. The
reduction of airflow causes oxygen desaturation, which can lead to sleep fragmentation and
hypoxemia. Due to this, patients with OSA often wake up feeling tired, with excessive daytime
sleepiness being the most reported complaint. In turn, this can hamper daily functioning.1
OSA has also been associated with an increased risk for serious medical conditions, particularly
cardiovascular diseases, such as hypertension, heart disease and stroke.2,3
Extensive research on neuropsychological functioning among adults with untreated
obstructive sleep apnea has shown that OSA negatively affects cognitive and psychological
functioning.4 Vigilance, attention, executive functioning, memory and motor coordination
have been found to be moderately to markedly affected. No substantial effects on
intelligence, verbal functioning, or visual perception have been reported.1,5,6
The treatment of choice for OSA is continuous positive airway pressure (CPAP). CPAP
corrects the respiratory disturbances and the resultant transient desaturation, leading to less
sleep fragmentation during sleep.7 Consequently, it is expected that when sleep is normalized by
CPAP treatment, functioning in daily living, cognitive functioning, and psychological wellbeing
of OSA patients will improve. Previous meta-analyses demonstrated significant improvement in
sleepiness and self-reported health status with CPAP when compared to placebo treatment or
conservative management.8-10 A number of reviews on cognitive functioning in OSA patients
after CPAP treatment have documented partial reversibility of cognitive dysfunction.1,11,12 The
cognitive domains in which recovery was noted, as well as the extent of recovery, differed widely
across the studies reviewed. This might be due to differences in methods, neuropsychological
measures, clinical characteristics and treatment compliance. Importantly, these reviews were
qualitative and their conclusions were based on reported statistical significance levels without
consideration of the magnitude of any observed effects.
In order to quantify the magnitude of the overall effect of CPAP treatment in OSA
on neuropsychological functioning, we carried out a meta-analysis of the randomized
controlled trials of CPAP treatment in OSA.
METHODSLiterature search
We initially performed a literature search of Medline, PsycInfo, Embase and Cochrane
Library covering the period from January 1990 to December 2011. In August 2012 we
update our search. Search terms for OSA were apnea (MeSH), apn*ea, obstructive sleep
apn*ea, OSA, hypopn*ea and SAHS. Treatment search terms included positive pressure
respiration (MeSH), continuous positive airway* pressure, CPAP, bilevel positive airway
pressure, BiPAP, positive pressure therapy and nocturnal ventilation. Search terms for
cognitive measures were mental processes (MeSH), neuropsychol*, mental status,
cogniti*, memory, attention, vigilance, executive and psychomotor. Searches for all
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possible combinations of OSA, treatment and cognitive measures were conducted. We
identified additional published studies by scanning the reference lists of the identified
papers and checking for journal publications of conference abstracts. Two independent
assessors identified relevant studies based on title and abstract that included empirical
data related to the treatment effect on neuropsychological functioning in OSA.
Inclusion Criteria
Studies had to meet the following criteria to be included in the meta-analysis:
• The diagnosis OSA was made by polysomnography (PSG) or polygraphy (PG) and the
number of apneas and hypopneas per hour sleep was stated by apnea/hypopnea index
(AHI) or respiratory disturbance index (RDI).
• The treatment of CPAP was investigated within a randomized controlled design.
• Duration and compliance of CPAP treatment for both the experimental and control
group was reported. In case this information was not originally reported, we contacted
the study authors to obtain relevant data.
• Assessment using at least one standardized neuropsychological test was employed as
a dependent variable.
• Test scores were reported for both the experimental and control group at baseline
and after treatment (mean and standard deviation), or other statistics that could be
converted to effect sizes. In case this information was not originally reported, we
contacted the study authors to obtain relevant statistics.
When articles reported overlapping samples of participants, the article with the largest
sample size was included. The quality of the randomized controlled trial (RCT) in the final
selection was judged using the Jadad rating score assessing randomization procedure,
blinding and description of dropout.13
Exclusion Criteria
Studies were eliminated according to the following exclusion criteria: monographs, letters,
book chapters, commentaries, review articles, case studies, dissertations, abstracts, studies
within pediatric (<18 years) or elderly populations (>65 years) and studies within special
medical populations with OSA (e.g., dementia, stroke or Down’s syndrome).
OUTCOME MEASURESPrimary outcome measures
Multiple neuropsychological tests were used across studies to assess cognitive functioning.
All neuropsychological tests were classified into seven cognitive domains following two
standard textbooks of neuropsychological assessment14,15: processing speed, attention,
vigilance, working memory, memory, verbal fluency and visuoconstruction. Supplement I
lists the included tests per cognitive domain.
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Secondary outcome measures
For subjective sleepiness, the Epworth sleepiness scale (ESS), a self-rating of recent
sleepiness behavior, was used, as it was employed in the majority of studies reviewed.
For similar reasons, objective sleepiness was quantified by the multiple sleep latency test
(MSLT), a measure of the time taken to fall asleep, and the maintenance of wakefulness
test (MWT), a measure of the ability to stay awake. Mood was assessed by the hospital
anxiety and depression scale (HADS), a self-report questionnaire consisting of an anxiety
and a depression subscale.
Participant and study variables
We recorded variables that are considered important risk factors for OSA such as age and
body mass index (BMI). BMI was classified according to the World Health Organization
criteria as normal (BMI 20.0-24.9 kg/m2), overweight (BMI 25.0-29.99 kg/m2) or obese
(BMI ≥ 30 kg/m2).16 We registered the AHI in order to quantify the severity of OSA. By
convention OSA severity is classified as mild, moderate or severe (AHI, ≥ 5; ≥ 15; ≥ 30 per
hour, respectively).17 We recorded the average number of hours usage of CPAP per night.
Patients were considered compliant when using CPAP for more than five days a week
and for more than 4 h a night, as defined by Kribbs et al.18 Duration of treatment was
registered in weeks as noted in the study designs.
Calculation of effect sizes and statistical analysis
For parallel studies, we calculated the effect size Hedges’ g by dividing the mean
difference between the CPAP treatment and control intervention (i.e., sham, placebo or
no treatment) by the pooled standard deviation. In case of cross-over trials, we conducted
a paired analysis using the mean difference, p-values from a paired t-test, or confidence
intervals from paired analyses. When the data required to include a paired analysis were
not reported, available data were analyzed as if the trial was a parallel group design
with treatment versus control intervention. We considered this to be a conservative
approach in that studies were under-weighted; the advantage of the reduced influence of
confounding covariates in cross-over designs was disregarded. In order to pool the results
across studies, we calculated a pooled d-value for all seven cognitive domains, weighted
for the sample sizes of the individual studies.
By convention, an effect size of 0.2 was considered small, 0.5 moderate, and 0.8
large.19 A positive direction of effect sizes implies improved performance. When studies
used more than one measure in a cognitive domain, we computed an averaged effect size
to avoid one study over-influencing the results for any given domain.
Heterogeneity in the results could be expected, given the diversity of both clinical
variables and cognitive assessments. Therefore, we applied the chi-square statistic Q and
the I2 index. The Q-statistic quantifies the degree to which the studies contributing to
each respective mean effect size can be regarded as homogeneous. A significant Q-value
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indicates heterogeneity among studies contributing to the particular mean, in which
case a further search for potential moderator variables is needed. When meta-analyses
encompass a relatively small number of studies, the Q-statistic is believed to have limited
value in detecting true heterogeneity among studies because of its rather low power.
Therefore, the I2 was also calculated to evaluate the degree of inconsistency in the study
results. I2 is calculated using the equation (Q - df) / Q x 100%, where df signifies the
degrees of freedom (= number of studies - 1). The I2 index reflects the percentage of total
variation across studies caused by heterogeneity rather than by chance. A value of 0%
indicates no observed heterogeneity and larger values indicate increasing heterogeneity.
For all effect sizes, the statistical uncertainty was accounted for in a 90% confidence
interval. We chose a 90% confidence interval because the treatment effect was expected
to be in a positive direction. All analyses were conducted using Comprehensive Meta-
Analysis software 2.0 (Englewood, NJ, USA, 2005).
Publication bias
To assess publication bias, we inspected funnel plots. The distribution of these plots
should approximate a symmetric funnel shape, in which the small studies with relatively
large variance scatter at the bottom and the larger studies converge, forming a peak at
the average effect size. Asymmetry of the funnel plot may indicate publication bias.20 In
addition, we used Orwin’s fail-safe N formula to estimate the number of studies that would
be theoretically needed to overturn the obtained effect and yield a non-significant effect.21
RESULTSIdentification of studies
The final searches yielded 938 articles for consideration (January 1990-July 2012). Ninety-
five articles were initially selected by the independent assessors with substantial inter-
observer agreement (97%; Cohen’s kappa = 0.78). Of these, nine articles did not describe
treatment studies and two articles were case studies. Of the remaining 84 articles, 26
were excluded because no control group was included, 16 were excluded because the
comparison group was made up of healthy individuals, 21 studies were excluded because
CPAP was compared to other treatments or CPAP withdrawal was investigated, and five
studies did not use standardized neuropsychological tests. Of the remaining 16 randomized
controlled studies that met most of our criteria, three had to be excluded because it was
neither possible to extract data to calculate effect sizes from the article nor to obtain
relevant data directly from the authors. Thus the meta-analysis was based on 13 RCTs.
We calculated the Jadad rating score, assessing randomization procedure, blinding
and description of dropout for the final set of 13 studies. Four studies reached a maximum
score of 5, one a score of 4, two a score of 3, and six had a poor score of 2 or lower.
The poor scores were due to a lack of information on the randomization method, an
inappropriate method of blinding, and no report on withdrawals and dropouts. Study
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characteristics are shown in Table 1. We note that the majority of the studies reviewed
used a cross-over design. Demographic and clinical characteristics of the samples of all
studies included in the meta-analysis are displayed in Table 2.
Participant and study characteristics
In total, neuropsychological test results of 533 OSA patients who received CPAP treatment
and 497 OSA patients who participated in a control condition were included in the meta-
analysis. The average sample size of the 13 studies was 54.2. As can be seen in Table 3,
patients had a mean age of 50.5 years and an average BMI of 30.4, which is qualified
as obese by the World Health Organization.16 On average, the OSA severity was mild
(AHI < 15) in three studies,22-24 moderate (15 ≤ AHI < 30) in four studies25-28 and severe
(AHI ≥ 30) in six studies.29-34 The overall use of CPAP was 4.5 hours per night, which can
be considered as sufficiently compliant (> 4 hours/night for > 5 days/week equals > 2.9
hours/night). However, CPAP usage was slightly below the defined level of compliance in
three studies.23,24,31 The treatment duration varied widely, ranging from 1 to 13 weeks.
Effect sizes
We calculated mean weighted effect sizes (d-values) of CPAP treatment for each cognitive
domain (Table 4). A significant but small effect on attention was observed in favor of CPAP
treatment (d = 0.19, 90% CI 0.08-0.31, p = 0.005). The Q-statistic and the I2 index indicated
that there was no significant heterogeneity for attention. There were no significant effects
Table 1. Design, quality and duration of studies included in the meta-analysis
Author Year RCT-design Control treatment Jadad Duration
Barbé et al, 29 2001 Parallel Sham CPAP 5 6 weeks
Bardwell et al. 30 2001 Parallel Sham CPAP 1 1 week
Barnes et al. 22 2002 Cross-over Oral placebo 5 8 weeks
Barnes et al. 25 2004 Cross-over Oral placebo 5 3 months
Engleman et al. 26 1994 Cross-over Oral placebo 2 4 weeks
Engleman et al. 23 1997 Cross-over Oral placebo 2 4 weeks
Engleman et al. 31 1998 Cross-over Oral placebo 2 4 weeks
Engleman et al. 24 1999 Cross-over Oral placebo 2 4 weeks
Gast et al. 27 2006 Parallel No treatment 2 1 week
Lee et al. 32 2012 Parallel Sham CPAP 4 3 weeks
Marshall et al. 28 2005 Cross-over Sham CPAP 5 3 weeks
Monasterio et al. 33 2001 Parallel Conservative 3 3/6 months
Pelletier-Fleury et al. 34 2004 Parallel No treatment 3 6 months
RCT, randomized controlled trial; JADAD, rating score for the quality of a randomized controlled trial; CPAP, continuous positive airway pressure; duration, duration of CPAP treatment.
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Table 2. Demographic and clinical characteristics of studies included in the meta-analysis
Author N (E/C) Age (y) BMI (kg/m2) AHI (events/h) CPAP usage (h/night)
Barbé et al. 29 54 (20/25) 53.1 29.0 55.4 4.5b
Bardwell et al. 30 36 (20/16) 47.4 31.2 51.2 5.4b
Barnes et al. 22 28 45.5 30.2 13.0 3.5b
Barnes et al. 25 80 46.4 31.0 21.0 3.6b
Engleman et al. 26 32 49.0 33.0 28.0 3.4b
Engleman et al. 23 16 52.0 29.8 11.0 2.8
Engleman et al. 31 23 47.0 30.0 43.0 2.8
Engleman et al. 24 34 44.0 30.0 10.0 2.8
Gast et al. 27 29 (17/12) 52.3 35.9 20.4 -a
Lee et al. 32 38 (21/17) 48.6 28.6 30.9 6.1b
Marshall et al. 28 29 50.5 31.5 22.0 4.9b
Monasterio et al. 33 125 (66/59) 53.5 29.4 58.1 4.8b
Pelletier-Fleury et al.34 171 (82/89) 52.9 30.9 53.2 5.4b
N, number of subjects (E, in experimental condition/ C, in control condition); BMI, body mass index; AHI, apnea/hypopnea index pre-treatment; CPAP, continuous positive airway pressure.a Usage was > 5 hours/night for 9 patients and ≤ 5 for 8 patients b CPAP usage considered to be compliant (> 4 hours/night for > 5 days/week).
of CPAP in the other cognitive domains. Unexpectedly, working memory worsened with
CPAP treatment, although not significantly (d = -0.18, 90% CI -0.34 to -0.02, p = 0.06).
In an exploratory analysis, the effect sizes for the subtests of attention were calculated.
A small, significant treatment effect was found for the paced auditory serial addition task
(PASAT; d = 0.39, 90% CI 0.24-0.55, p < 0.001) and the trail making test B (TMT B; d =
0.18, 90% CI 0.006-0.30, p =0.02). No significant effect of CPAP treatment was observed
for the Trail making test A (TMT A; d = 0.15, 90% CI 0.01-0.29, p = 0.07) nor for the
Stroop color-word test (d = 0.11, 90% CI -0.14 to 0.36, p = 0.47).
The mean weighted effect of CPAP treatment on sleepiness and mood was calculated
from the data reported in the selected studies (Table 5). Objective sleepiness was assessed
in six studies and showed a small, significant effect (d = 0.30; 90% CI 0.16-0.44, p <
0.001). The effect of subjective sleepiness calculated from eight studies was moderate
(d = 0.53; 90% CI .27 to.78, p < 0.001). The effect of CPAP on depression reached
significance, showing a small effect (d = 0.35; 90% CI 0.12-0.44, p = 0.04). However,
there was moderate statistical heterogeneity in the analyses for subjective sleepiness and
depression, limiting the value of these effect sizes.
Publication bias
We conducted funnel plot analyses for all cognitive domains, measures of sleepiness and
mood. We did not observe any asymmetry toward positive effects for any of the outcome
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2Table 3. Summary of demographic and clinical characteristics of studies included in the meta-analysis
Variables M SD Range N
Sample size 54.2 43.7 16-172 13
Age (y) 50.5 6.8 44.0-53.5 13
BMI (kg/m2) 30.4 3.8 28.6-38.2 13
AHI (events/h) 34.6 11.5 10.0-55.5 13
CPAP usage (h/night) 4.5 1.4 2.8-6.1 12
Treatment duration (weeks) 8.2 - 1-13 13
M, mean; SD, standard deviation; N, number of studies in which the variable was reported; BMI, body mass index; AHI, apnea/hypopnea index pre-treatment; CPAP, continuous positive airway pressure.
Table 4. Mean weighted effect sizes, confidence interval and heterogeneity for each cognitive domain
Cognitive domain k N d 90% CI Q p(Q) I2 Fail-safe N
Processing speed 10 466 0.10 -0.03 to 0.22 9.87 .36 9 -
Attention 12 666 0.19 0.08 to 0.31 2.30 1.00 0 13
Vigilance 10 467 0.05 -0.07 to 0.16 2.12 .99 0 -
Memory 9 425 0.09 -0.03 to 0.22 6.56 .58 0 -
Working memory 6 362 -0.18 -0.34 to -0.02 2.41 .79 0 -
Verbal fluency 8 378 -0.02 -0.14 to 0.11 1.55 .98 0 -
Visuoconstruction 5 250 -0.01 -0.17 to 0.16 5.08 .28 21 -
k, number of studies; N, number of patients; d, mean effect size; CI, confidence interval; Q, within domain heterogeneity; p(Q), p-value for heterogeneity; I2, percentage of heterogeneity due to study differences; Fail-safe N, number of studies theoretically needed to yield the observed effect to non-significance; p < 0.05.
Table 5. Mean weighted effect sizes, confidence interval and heterogeneity for measures of sleepiness and mood
k N d 90% CI Q p(Q) I2 Fail-safe N
Sleep
Objective 6 214 0.30 0.16 to 0.44 3.49 0.63 0 13
Subjective 8 478 0.53 0.27 to 0.78 22.60 0.01 56 75
Mood
Depression 5 102 0.35 0.07 to 0.64 11.75 0.01 66 10
Anxiety 5 102 0.10 -0.16 to 0.36 7.10 0.13 43 -
k, number of studies; N, number of patients; d, mean effect size; CI, confidence interval; Q, within domain heterogeneity; p(Q), p-value for heterogeneity; I2, percentage of heterogeneity due to study differences; Fail-safe N, number of studies theoretically needed to yield the observed effect to non-significance; p < 0.05.
measures; thus there was no indication of publication bias in the selection of studies
included in the current analysis. Figure 1 displays the funnel plot for attention, the only
cognitive domain in which CPAP treatment had a significant, positive effect.
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We calculated the fail-safe N value for attention, sleepiness and depression, the only
measures that showed significant difference between CPAP treatment and control conditions.
Thirteen studies would be required to reduce the observed effect on attention to a non-
significant level. For objective and subjective sleepiness, 13 and 75 studies would be needed,
respectively. Ten studies would be sufficient to reach non-significance in depression. Although,
all fail-safe N values exceeded the number of included studies per outcome measure (k), they
are considered to be relatively small for attention, objective sleepiness and depression.
Figure 1. Funnel plot for attention.
DISCUSSIONThe current meta-analysis of neuropsychological functioning after CPAP showed a small
improvement in cognitive functioning, specifically in the attention domain, in patients with
OSA. Only the divided attention tasks PASAT and TMT B showed significant improvement,
with the PASAT, the most demanding attention task, revealing the largest effect. Within
the selected studies a small decrease in the objective sleepiness after CPAP was observed.
Although a moderate improvement of subjective sleepiness and a small decrease of depressive
symptoms was found, these findings are of limited value due to moderate heterogeneity.
The generally held clinical view is optimistic: CPAP improves cognition substantially.
Several qualitative reviews have reported a wide range of positive effects of CPAP on
cognition. The most frequently reported improvement is in the domain of attention and
vigilance, but changes in the domains of memory and executive function have also been
noted.1,11,12 However, the minimal treatment effect observed in the current, quantitative
meta-analysis does not support widely held clinical beliefs or the relatively positive
conclusions drawn in previous reviews. The difference between our findings and those
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of previous reviews is probably due to less stringent criteria for study inclusion and the
absence of appropriate weighing of the study effects. As a result of the latter, small
positive effects may be overestimated, while negative results may be underexposed.
A drawback of administering stringent inclusion criteria in the current meta-analysis
is the loss of possibly valuable information from studies that compare CPAP with other
treatments. Examples of such treatments are dental devices, postural therapy and other
positive airway therapies. Other studies that were excluded aim to improve CPAP compliance
using a behavioral approach compared to the regular administration of CPAP therapy.
It seems plausible to assume that improvement of cognitive function after CPAP
treatment will be most pronounced in the domains affected by OSA. One could question
whether the present results are in line with this expectation. In a meta-analysis of
neuropsychological effects of untreated OSA, Beebe et al.6 reported a substantial negative
impact upon vigilance, executive functioning (EF) and motor coordination. Vigilance and
motor coordination are relatively one-dimensional concepts, whereas EF is an umbrella
concept, composed of a number of cognitive functions, including working memory,
mental flexibility, planning, problem-solving, inhibition and verbal fluency. In our study, EF
was divided in the subdomains of attention, working memory and verbal fluency in order
to evaluate the treatment effect more precisely. Our exploratory analysis of the attention
domain showed improvement on the PASAT and TMT B after CPAP treatment. Both
tests are included within the cognitive domain of EF according to Beebe and colleagues.
Unexpectedly, a small negative effect of CPAP on working memory was seen, albeit not
significant. There was no notable effect on verbal fluency. Although Beebe et al. did not
report which tests accounted for the significant deterioration of EF in OSA, our findings
at least partly support the assumption that the most affected domains improve most
with treatment. In addition, despite similar operationalization of the vigilance domain in
both Beebe et al.’s and our studies, we did not find significant improvement after CPAP
treatment. No statement on the treatment effect on motor functioning can be made, as it
was not investigated in the studies included in our meta-analysis.
Daytime sleepiness is one of the most frequently reported complaints of patients with
OSA and is expected to improve with CPAP treatment. Several meta-analyses have been
performed to determine whether CPAP improves sleepiness.8-10 These meta-analyses showed
that subjective sleepiness (ESS) as well as objective sleepiness measured by the MWT were
significantly reduced by CPAP. Possibly, the minimal treatment effect we found on cognition
might be due to a smaller improvement in sleepiness and mood symptoms than is generally
observed. In order to test this hypothesis, we compared the observed effect of CPAP treatment
on sleepiness and mood symptoms to the findings of the meta-analysis by Giles et al.8 They
reported significant improvement in subjective sleepiness (ESS; d = 0.37, 90% CI 0.32-0.42,
p < 0.001), while objective sleepiness improved partially (MWT; d = 0.19, 90% CI 0.05- 0.33,
p = 0.02 and MSLT; d = 0.11, 90% CI -0.07 to 0.29, p = 0.31). The available data in our
study, showing overlap with Giles et al., showed a clear but modest improvement of CPAP
treatment in objective as well as subjective sleepiness (Table 5). Our findings are comparable
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to those of the more extensive meta-analysis by Giles et al. In our meta-analysis, we observed
a decline in depressive symptoms after CPAP treatment, but no change in anxiety level
(HADS). In a smaller study sample, Giles et al. found a comparable small effect on depression
favoring CPAP (d = 0.20). When corrected for heterogeneity, the effect was reduced to a non-
significant level. Considering the similar improvement on sleepiness and mood symptoms
compared to prior findings, the minimal treatment effect on cognition in our meta-analysis is
unlikely to be due to a lack of improvement in sleepiness or mood.
There are some methodological limitations to this meta-analysis that could have
influenced our findings. First, we used uncorrected test scores. In clinical practice,
test results are often corrected demographically using normative data,14 because test
performance is strongly influenced by age and level of education. Despite the use of
uncorrected data, the possible influence of age was considered to be negligible as all
selected studies included middle-aged patient samples. The effect of level of education
could not be included in our analyses, because only two studies provided these data.27,29
It is conceivable that larger effect sizes would have emerged if the studies had used
demographically corrected test scores, because this correction removes sources of variance
that are irrelevant to the treatment effect.
Second, no moderator analysis was conducted, because it was considered statistically
inappropriate due to the limited number of studies included in the meta-analysis per
domain and the minimal treatment effect found. Nevertheless, the severity of OSA,
treatment compliance and treatment duration could have moderated the effect of CPAP
treatment on cognition, as these factors differed substantially across studies. To inspect
whether studies with mild OSA and low compliance diminished the effects, we recalculated
effect sizes of cognition for studies with moderate to severe OSA (AHI ≥ 15 events/h, k
= 10) and for studies with sufficient compliance ( > 4 hours for > 5 days, k = 9). Neither
recalculation had a significant influence on the observed effect sizes.
Third, we used a conservative approach to calculate effect sizes for three of the cross-
over studies.23,24,26 This may have resulted in a reduced contribution of these studies to the
overall results. An additional analysis, excluding these three studies, did not yield different
effect sizes for the attention domain. This suggests that the relatively modest treatment
effect we found on attention was not due to our conservative approach of data analysis.
Cross-over studies have the advantage over parallel studies of reducing the influence of
confounding covariates. However, a carry-over effect can distort the results in a cross-over
study. One strategy for minimizing carry-over effects is to include a wash-out period between
treatment arms. Only two of the seven cross-over studies in our meta-analysis included a
washout period (both of two weeks).25,28 Yet, in all of the cross-over studies a statistical analysis
was conducted to check whether a significant interaction of treatment had occurred. Only
one study reported a significant carry-over effect on a cognitive outcome measure, resulting in
adjustment of the analysis.26 Thus, because the cross-over studies included in our analysis either
employed a design to minimize possible carry-over effects, or adjusted for such possible effects
statistically, we have no reason to believe that this design issue had an effect on our results.
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In conclusion, in this meta-analysis we observed only a minimal effect of CPAP
treatment on cognition in patients with OSA. The only cognitive domain in which
a treatment effect was found was attention, and the improvement seen was modest.
Future studies should investigate the possible moderating effect of OSA severity, daytime
sleepiness, compliance, and treatment duration on study outcomes, preferably in the
context of a randomized, controlled trial.
Practice Points
• The results of this meta-analysis indicate that CPAP has a small, positive effect on
attention, and no effect on other cognitive domains. These results do not support the
conclusion drawn in previous qualitative reviews that CPAP improves functioning in
several cognitive domains.
• In accordance with previous quantitative reviews sleepiness and mood moderately
improve after CPAP.
Research Agenda
• Additional RCTs are required to quantify the influence of OSA severity, treatment
duration and compliance with CPAP treatment on cognitive functioning.
• Studies should follow the CONSORT guidelines for standardized reporting of randomized
trials to improve their methodology and to augment their suitability for meta-analyses.
• Neuropsychological test batteries should include demanding attention tasks, such as
PASAT and TMT B.
• Further research of the effect of CPAP on cognition in the elderly and neurological
patients with OSA (e.g., patients with dementia or stroke) is required.
CONFLICTS OF INTERESTThere is no conflict of interest.
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REFERENCES
1 Aloia MS, Arnedt JT, Davis JD, Riggs RL, Byrd D. Neuropsychological sequelae of obstructive sleep apnea-hypopnea syndrome: a critical review. J Int Neuropsychol Soc 2004;10:772-785.
2 Yaggi H, Mohsenin V. Obstructive sleep apnoea and stroke. Lancet Neurol 2004;3:333-342.
3 Parish JM, Somers VK. Obstructive Sleep Apnea and Cardiovascular Disease. Mayo Clin Proc 2004;79:1036-1046.
4 Alchanatis M, Zias N, Deligiorgis N, Liappas I, Chroneou A, Soldatos C, et al. Comparison of cognitive performance among different age groups in patients with obstructive sleep apnea. Sleep Breath 2008;12:17-24.
5 Engleman HM, Kingshott RN, Martin SE, Douglas NJ. Cognitive function in the sleep apnea/hypopnea syndrome (SAHS). Sleep 2000;23:S102-108.
6 Beebe D, Groesz L, Wells C, Nichols A, McGee K. The neuropsychological effects of obstructive sleep apnea: A meta-analysis of norm-referenced and case-controlled data. Sleep 2003;26:298-307.
7 Lamphere J, Roehrs T, Zorick F, Conway W, Roth T. Recovery of alertness after CPAP in apnea. Chest 1989;96:1364-1367.
8 Giles TL, Lasserson TJ, Smith BH, White J, Wright J, Cates CJ. Continuous positive airways pressure for obstructive sleep apnoea in adults. The Cochrane Library 2006;4:1-107.
9 Marshall NS, Barnes M, Travier N, Campbell AJ, Pierce RJ, McEvoy RD, et al. Continuous positive airway pressure reduces daytime sleepiness in mild to moderate obstructive sleep apnoea: a meta-analysis. Thorax 2006;61:430-434.
10 McDaid C, Griffin S, Weatherly H, Duree K, van der BM, van HS, et al. Continuous positive airway pressure devices for the treatment of obstructive sleep apnoea-hypopnoea syndrome: A systematic review and economic analysis. Health Technol Asses 2009;13:1-162.
11 Weaver TE, Chasens ER. Continuous positive airway pressure treatment for sleep apnea in older adults. Sleep Med Rev 2007;11:99-111.
12 Sánchez AI, Martínez P, Miró E, Bardwell WA, Buela-Casal G. CPAP and behavioral therapies in patients with obstructive sleep apnea: Effects on daytime sleepiness, mood, and cognitive function. Sleep Med Rev 2009;13:223-233.
13 Jadad A, Moore R, Carroll D, Jenkinson C, Reynolds D, Gavaghan D, et al. Assessing the quality of reports of randomized clinical trials: Is blinding necessary? Control Clin Trials . 1996;17:1-12.
14 Lezak M. Neuropsychological assessment. 2nd ed. New York: Oxford University Press; 2004.
15 Strauss E, Sherman E, Spreen O. A compendium of neuropsychological tests: administration, norms and, commentary. New York: Oxford University Press; 2006.
16 World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. WHO; 2000.
17 American Academy of Sleep Medicine. International classification of sleep disorders. Diagnostic and Coding Manual. 2nd ed. Westchester: ASSM; 2005.
18 Kribbs NB, Pack AI, Kline LR, Getsy JE, Schuett JS, Henry JN, et al. Effects of one night without nasal CPAP treatment on sleep and sleepiness in patients with obstructive sleep apnea. Am Rev Respir Dis 1993;147:1162-1168.
19 Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. New York: Academic Press; 1988.
20 Borenstein M, Hedges L, Higgins J, Rothstein H. Introduction to meta-analysis. Hoboken, NJ: John Wiley & Sons Inc.; 2009.
21 Orwin RG. A Fail-Safe N for Effect Size in Meta-Analysis. J Educ Stat 1983;8:157-159.
22 Barnes M, Houston D, Worsnop CJ, Neill AM, Mykytyn IJ, Kay A, et al. A randomized controlled trial of continuous positive airway pressure in mild obstructive sleep apnea. Am J Respir Crit Care Med 2002;165:773-780.
23 Engleman HM, Martin SE, Deary IJ, Douglas NJ. Effect of CPAP therapy on daytime function in patients with mild sleep apnoea/hypopnoea syndrome. Thorax 1997;52:114-119.
24 Engleman HM, Kingshott RN, Wraith PK, Mackay TW, Deary IJ, Douglas NJ. Randomized placebo-controlled crossover trial of continuous positive airway pressure for mild sleep Apnea/Hypopnea syndrome. Am J Respir Crit Care Med 1999;159:461-467.
25 Barnes M, McEvoy RD, Banks S, Tarquinio N, Murray CG, Vowles N, et al. Efficacy of positive airway pressure and oral appliance in mild to
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moderate obstructive sleep apnea. Am J Respir Crit Care Med 2004;170:565-664.
26 Engleman HM, Martin SE, Deary IJ, Douglas NJ. Effect of continuous positive airway pressure treatment on daytime function in sleep apnoea/hypopnoea syndrome. Lancet 1994;343:572-575.
27 Gast H, Schwalen S, Ringendahl H, Jorg J, Hirshkowitz M. Sleep-related breathing disorders and continuous positive airway pressure-related changes in cognition. Sleep Med Clin 2006;1:499-511.
28 Marshall NS, Neill AM, Campbell AJ, Sheppard DS. Randomised controlled crossover trial of humidified continuous positive airway pressure in mild obstructive sleep apnoea. Thorax 2005;60:427-432.
29 Barbe F, Mayoralas LR, Duran J, Masa JF, Maimo A, Montserrat JM, et al. Treatment with continuous positive airway pressure is not effective in patients with sleep apnea but no daytime sleepiness: A randomized, controlled trial. Ann Int Med 2001;134:1015-1023.
30 Bardwell WA, Ancoli-Israel S, Berry CC, Dimsdale JE Neuropsychological effects of one-week continuous positive airway pressure
treatment in patients with obstructive sleep apnea: A placebo-controlled study. Psychosom Med 2001;63:579-584
31 Engleman HM, Martin SE, Kingshott RN, Mackay TW, Deary IJ, Douglas NJ. Randomised placebo controlled trial of daytime function after continuous positive airway pressure (CPAP) therapy for the sleep apnoea/hypopnoea syndrome. Thorax1998;53:341-345.
32 Lee I, Bardwell WA, Kamat R, Tomfohr L, Heaton RK, Ancoli-Israel S, et al. A model for studying neuropsychological effects of sleep intervention: the effect of three-week continuous positive airway pressure treatment. Drug Discov Today: Dis Model 2011;8:147-154.
33 Monasterio C, Vidal S, Duran J, Ferrer M, Carmona C, Barbe F, et al. Effectiveness of continuous positive airway pressure in mild sleep apnea-hypopnea syndrome. Am J Crit Care Med 2001;164:939-943.
34 Pelletier-Fleury N, Meslier N, Gagnadoux F, Person C, Rakotonanahary D, Ouksel H, et al. Economic arguments for the immediate management of moderate-to-severe obstructive sleep apnoea syndrome. Eur Res J 2004;23:53-60.
33
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Supplement I
Domain Test k
Processing Speed 10
Digit symbol substitution (WAIS) 9
Symbol search (WAIS) 1
8-choice reaction time 3
Attention 12
Trail making test A/B 12
Stroop color-word 5
Paced auditory serial addition task 8
Vigilance 10
Psychomotor vigilance task 2
Steer clear 6
Rapid visual information processing 3
Digit vigilance 2
Memory 9
Verbal paired associated (WMS) 2
California verbal learning test 1
Word pair memory recall 2
Hopkins verbal learning test 1
Logical memory 1&2 (RBMT) 1
Visual reproduction (WMS) 1
Benton-R visual retention test 3
Brief visuospatial memory test 1
Working memory 6
Digit span (WAIS) 6
Letter-number sequencing (WAIS) 1
Digit Ordering Test 1
Mental control (WMS) 2
k, number of studies.
34
DIAGNOSTIC ACCURACY OF NOCTURNAL OXIMETRY FOR DETECTION OF SLEEP APNEA IN STROKE REHABILITATION
Aaronson JA, van Bezeij T, van den Aardweg JG, van Bennekom CAM, Hofman WF
Stroke 2012; 43:2491-231
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ABSTRACTBackground and purpose Sleep apnea syndrome (SAS) is a common sleep disorder
in stroke patients and is associated with decreased recovery, increased risk of recurrent
stroke and mortality. The standard diagnostic test for SAS is poly(somno)graphy, but this
is often not feasible in stroke rehabilitation settings. This study investigated the diagnostic
value of nocturnal oximetry for screening SAS in stroke rehabilitation.
Methods Fifty-six stroke patients underwent nocturnal polygraphy and oximetry.
Sensitivity, specificity, positive and negative predictive values for the oxygen desaturation
index were calculated. Patient and sleep characteristics were used to develop a predictive
model of apnea-hypopnea index.
Results Forty-six percent of the stroke patients had SAS. The majority of SAS patients
was male, older and had a higher body mass index than patients without SAS. Sensitivity,
specificity, positive and negative predictive value for the oxygen desaturation index ≥ 15
were respectively 77%, 100%, 100% and 83%. Oxygen desaturation index predicted 87%
of the variance in apnea-hypopnea index. Patient characteristics did not add significantly
to the prediction model.
Conclusion Nocturnal oximetry is an accurate diagnostic screening instrument for the
detection of SAS in stroke patients.
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INTRODUCTIONSleep apnea syndrome (SAS) is much more common in stroke patients than in the
general population, with prevalence ranging from 38% to 78% (2-4% in the general
population).1,2 SAS increases the risk of a stroke independent of other risk factors such as
age, obesity, gender and hypertension.3 In turn, SAS in stroke is associated with decreased
functional recovery, increased risk of recurrent stroke and mortality.4
The standard diagnostic test for SAS is overnight polysomnography or multichannel
polygraphy. Full sleep examination, however, is costly, requires technical expertise and
forms an additional burden for the patient. Nocturnal oximetry has been proposed as
a screening instrument.5 In the general sleep population, sensitivity and specificity have
been found to vary widely (sensitivity 31%-100% and specificity 41%-100%).5 The aim of
this study was to validate the use of nocturnal oximetry for screening SAS and to identify
possible clinical predictors of SAS in stroke rehabilitation.
SUBJECTS AND METHODSSubjects
We conducted a retrospective study of stroke patients admitted to Heliomare, a
rehabilitation center in the Netherlands, during an 18-month period. All patients received
active rehabilitation and underwent polygraphy within 2 months following admission.
Diagnosis of stroke was confirmed by neurologists based on a history of sudden onset of a
neurological deficit lasting > 24 hours and a brain lesion compatible with the neurological
deficit on computerized tomography or magnetic resonance imaging. Exclusion criteria
were: (1) previously diagnosed of SAS or treated for SAS; (2) > 6 months after stroke;
(3) concomitant central nervous system diseases; (4) traumatic brain injury; and (5)
paraplegia. Body mass index (BMI; kg/m2) was calculated for all patients and classified as
overweight (25.0-29.9) and obese (≥ 30) according to World Health Organization criteria.
Stroke was classified by subtype (ischemic or hemorrhagic) and location (supratentorial or
infratentorial). Degree of disability due to stroke was classified with the modified Rankin
Scale. The study was approved by the Institutional Review Board of Heliomare.
Measurements
Polygraphy included recordings of airflow, oxygen saturation, heart rate, and respiratory
effort. The data were recorded with a multichannel digital polygraph, which has been
validated against hospital-based polysomnography (POLY-MESAM; Martinsried, Germany).6
Polygraphic records were analyzed automatically and checked manually by an
experienced physician (T.V.B.). Apnea was defined as a reduction of airflow of 90 to 100%
from baseline lasting > 10 seconds and hypopnea as a reduction of airflow of 50 to
90% from baseline lasting > 10 seconds associated with an oxygen desaturation of ≥
4%. Baseline was determined as an average value over the previous 10 seconds. Apneas
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with thoracic motion, without thoracic motion or with initial lack of motion followed by
respiratory effort, were classified as obstructive, central, or mixed, respectively.
The apnea-hypopnea index (AHI) was defined as the mean number of apneas and
hypopneas per hour. SAS was defined by an AHI of ≥ 15. Oxygen desaturation was
determined by a pulse oximeter. The oxygen desaturation index (ODI) was defined as
the mean number of desaturations of ≥ 4% from baseline per hour. Subjective daytime
sleepiness was measured with the Epworth Sleepiness Scale.7
Statistical analysis
The patient sample was characterized with descriptive statistics. We visually checked for
outliers in AHI and ODI and used Grubbs’ extreme deviate test to exclude the outliers
from further analysis. Patients with and without SAS (SAS/no SAS) were compared with
χ2, Mann-Whitney or Student t tests. We calculated Pearson correlation coefficients for
AHI and ODI. Sensitivity, specificity, positive predictive value, and negative predictive value
with 95% confidence interval (CI) were calculated. The diagnostic accuracy of ODI was
assessed with receiver-operator characteristic analyses. We developed a predictive model of
AHI by calculating a correlation matrix and entering significantly correlating variables into a
simultaneous linear regression analysis. Statistical significance was set at 0.05. A Bonferroni-
Holm correction for multiple testing was applied to polygraphic and sleepiness data.
RESULTSWe obtained sleep recordings of 67 stroke patients. Ten patients who met the exclusion
criteria and 1 outlier were omitted from further analysis. Fifty-six patients were included
in the final analysis (Table 1). Sixty-two percent of the patients were male. Mean age was
55.6 years (± 10.3; range 26-74). The average duration from stroke onset was 30.3 days
(± 27.1). Seventy-three percent of the patients had an ischemic stroke and 88% had a
stroke in the supratentorial region. Sixteen patients were moderately disabled, 26 had a
moderately severe disability and 14 had a severe disability. Twenty-three percent of the
patients were overweight and 18% were obese.
Forty-six percent of the stroke patients had SAS diagnosed. All SAS patients had
predominantly obstructive apneas. The majority of SAS patients was male (78% vs. 22%
female), older, and had a higher BMI and ODI than patients without SAS. SAS patients
did not differ from patients without SAS in stroke subtype, stroke location or degree of
disability. Excessive daytime sleepiness (Epworth Sleepiness Scale ≥ 10) was reported by
one-third of both patients with and without SAS.
The ODI correlated strongly with the AHI (r = 0.92; p < 0.01). The sensitivity and
specificity of pulse oximetry are presented in Table 2. With ODI ≥ 15, the sensitivity for
SAS was 77% (CI, 56%-90%) with 100% specificity (CI, 86%-100%). A lower ODI cut-off
(≥ 5) increased the sensitivity to 96% (CI, 79%-100&%), but the specificity declined to
43% (CI, 26%-62%).
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Table 1. Demographic, clinical and polygraphic data
Mean (SD) SAS (n=26) No SAS (n=30) P-value
Gender male/female 21/5 14/16 <0.01
Age (y) 59.9 (7.4) 51.9 (11.1) <0.01
Body mass index 27.2 (4.6) 24.2 (4.6) 0.02
Stroke characteristics
Subtype 0.49
Ischemic 18 23
Hemorrhage 5 6
Ischemic and hemorrhage 3 1
Location 0.17
Supratentorial 25 24
Infratentorial 1 4
Supratentorial and infratentorial 0 2
mRS, median (range) 4.0 (3-5) 4 (3-5) 0.94
AHI 29.5 (15.4) 6.9 (4.4) <0.01
Apneas (events) 0.29
Obstructive 43.2 (34.1) 6.3 (7.5) <0.01
Central 2.7 (6.8) 0.7 (1.9) 0.12
Mixed 6.3 (15.1) 0.5 (0.9) 0.04
Hypopneas 136.5 (91.7) 37.2 (25.4) <0.01
ODI 27 (16.2) 6.0 (4.1) <0.01
Epworth Sleepiness Scale 8.0 (5.6) 7.1 (5.3) 0.53
AHI indicates apnea-hypopnea index; mRS, modified Rankin Scale; ODI, oxygen desaturation index; SAS, sleep apnea syndrome; SD, standard deviation. Significant after Bonferroni-Holm correction.
Table 2. Sensitivity and specificity of pulse oximetry at different ODI and AHI cut-off values
ODI
Sensitivity Specificity
AHI ≥ 5 ≥ 10 ≥ 15 ≥ 5 ≥ 10 ≥ 15
≥ 5 86 64 46 67 92 100
≥ 10 89 75 56 50 90 100
≥ 15 96 88 77 43 80 100
AHI indicates apnea-hypopnea index; ODI, oxygen desaturation index.
The diagnostic accuracy of ODI for SAS is represented by the receiver-operator
characteristic curve curve in the Figure. Given a 46% prevalence of SAS in stroke,
the positive predictive value of oximetry was 100% (CI, 80%-100%), with a negative
predictive value of 83% (CI, 67%-93%).
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3 Figure 1. Receiver-operator characteristic (ROC) curve of the sensitivity and specificity of oxygen desaturation index (ODI) for diagnosing sleep apnea syndrome (SAS) (apnea-hypopnea index [AHI ] ≥ 15). The area under the curve was 0.93 (confidence interval, 0.86-1.00). The symbols represent different ODI cut-off values: ▲= ≥ 5, ●= ≥ 10, ■= ≥ 15.
The clinical variables age, gender and BMI correlated significantly with AHI. The
regression model showed that age and BMI were significant predictors, explaining 51%
of the variance in AHI. When ODI was added to the regression analysis, 87 % of the
variance in AHI was explained, with ODI being the only significant predictor. The resultant
regression equation is: Predicted AHI = 2.66 + 0.94 * ODI.
DISCUSSIONIn the current study, 46% of the stroke patients had SAS diagnosed. We found that nocturnal
oximetry is an accurate predictor for SAS with a sensitivity of 77% and a specificity of
100%. Given the high prevalence of SAS in stroke, a positive oximetry result increased the
likelihood of SAS to 100%, whereas a negative result lowered the probability to 17%.
The majority of the SAS patients were male. In addition, SAS patients were older, with
a higher BMI than patients without SAS.
Oximetry was the best predictor of SAS, explaining > 80% of the variance in AHI.
Clinical variables such as age, gender and BMI did not significantly add to the predictive
value of oximetry. Further validation of oximetry in larger samples is required to determine
whether our findings can be generalized to other stroke samples.
ACKNOWLEDGEMENTSThe authors thank Brita Daniels and Irene Kos for their support with data collection.
DISCLOSURESNone.
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REFERENCES
1 Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med. 1993;328:1230-1235.
2 Johnson KG, Johnson DC. Frequency of sleep apnea in stroke and TIA patients: A meta-analysis. J Clin Sleep Med.2010;6:131-137.
3 Yaggi HK, Concato J, Kernan WN, Lichtman JH, Brass LM, Mohsenin V. Obstructive sleep apnea as a risk factor for stroke and death. N Engl J Med. 2005;353:2034-2041.
4 Bassetti CL, Milanova M, Gugger M. Sleep-disordered breathing and acute ischemic stroke - diagnosis, risk factors, treatment, evolution,
and long-term clinical outcome. Stroke. 2006;37:967-972.
5 Netzer N, Eliasson AH, Netzer C, Kristo DA. Overnight pulse oximetry for sleep-disordered breathing in adults - A review. Chest. 2001;120:625-633.
6 Verse T, Pirsig W, Junge-Hulsing B, Kroker B. Validation of the POLY-MESAM seven-channel ambulatory recording unit. Chest. 2000;117:1613-1618.
7 Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep. 1991;14:540-545.
43
Aaronson JA, Nachtegaal J, van Bezeij T, Groet E, Hofman WF,
van den Aardweg JG, van Bennekom CAM
Archives of Physical Medicine and Rehabilitation 2014; 95:747-752
CAN A PREDICTION MODEL COMBINING
SELF-REPORTED SYMPTOMS, SOCIODEMOGRAPHIC
AND CLINICAL FEATURES SERVE AS A RELIABLE
FIRST SCREENING METHOD FOR SLEEP APNEA SYNDROME
IN PATIENTS WITH STROKE?
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SLEEP APN
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ABSTRACTObjective To determine whether a prediction model combining self-reported symptoms,
sociodemographic and clinical parameters could serve as a reliable first screening method in
a step-by-step diagnostic approach to sleep apnea syndrome (SAS) in stroke rehabilitation.
Design Retrospective study.
Setting Rehabilitation center.
Participants Consecutive sample of patients with stroke (N = 620) admitted between
May 2007 and July 2012. Of these, 533 patients underwent SAS screening. In total, 438
patients met the inclusion and exclusion criteria.
Main outcome measures We administered an SAS questionnaire consisting of self-
reported symptoms, sociodemographic and clinical parameters. We performed nocturnal
oximetry to determine the oxygen desaturation index (ODI). We classified patients with an
ODI ≥ 15 as having a high likelihood of SAS. We built a prediction model using backward
multivariate logistic regression, and evaluated diagnostic accuracy using receiver operating
characteristic analysis. We calculated sensitivity, specificity and predictive values for
different probability cutoffs.
Results Thirty-one percent of patients had a high likelihood of SAS. The prediction
model consisted of the following variables: sex, age, body mass index, and self-reported
apneas and falling asleep during daytime. The diagnostic accuracy was 0.76. Using a low
probability cutoff (0.1), the model was very sensitive (95%) but not specific (21%). At a
high cutoff (0.6), the specificity increased to 97%, but the sensitivity dropped to 24%. A
cutoff of 0.3 yielded almost equal sensitivity and specificity of 72% and 69%, respectively.
Depending on the cutoff, positive predictive values ranged from 35% to 75%.
Conclusion The prediction model shows acceptable diagnostic accuracy for a high
likelihood SAS. Thus, we conclude that the prediction model can serve as a reasonable
first screening method in a stepped diagnostic approach to SAS in stroke rehabilitation.
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INTRODUCTIONSleep apnea syndrome (SAS) is a highly prevalent sleep disorder in patients with and
is known to contribute to poor functional recovery, increased risk of recurrent stroke,
and post stroke mortality.1-3 Standard evaluation of SAS in stroke rehabilitation settings is
limited, despite its high prevalence and serious consequences.4 When SAS is suspected,
clinical SAS guidelines recommend attended overnight polysomnography or multichannel
polygraphy.5 In stroke rehabilitation, however, these approaches are often not feasible
because of limited access, high costs, and practical constraints. Hence, we propose a step-
by-step diagnostic approach to SAS in which patients with stroke only receive the level of
SAS screening they need, beginning with simple, less costly methods followed by a full
sleep study, if required. This approach asks for easily administered and reliable screening
instruments for the detection of patients a high likelihood of SAS. In this context, we
recently performed a study to evaluate the diagnostic value of nocturnal oximetry
screening in patients with stroke. We found that nocturnal oximetry was highly accurate
for predicting SAS in the stroke population with excellent diagnostic accuracy (93%) and
a positive and negative predictive value of 83% and 100%, respectively.6
Another even more simple screening method is the use of self-report SAS questionnaires.
One of the most frequently administered SAS questionnaires in general sleep clinics is the
Berlin Questionnaire (BQ).7 The BQ asks about known risk factors for sleep apnea, namely
snoring behavior, sleepiness, fatigue and the presence of hypertension and obesity.
Although the BQ is well-validated in general sleep clinics7, recent validation studies in
the stroke population were disappointing; they found that the BQ could not adequately
discriminate between patients with and without SAS.4,8,9 Because the BQ is not able to
accurately predict SAS in patients with stroke and nocturnal oximetry is currently not yet
widely available in stroke rehabilitation settings, it is of interest to develop an adequate
and easily accessible SAS screening tool that could be administered as first step in the
detection of SAS. The BQ contains a selection of SAS-related symptoms, but does not ask
about daytime consequences (e.g., concentration loss, morning headaches, depressed
mood and irritability). Moreover, the BQ does not include important sociodemographic
and clinical risk factors (e.g., age, sex, smoking, comorbidities and medication use).10
The primary aim of the present study was to determine whether a predictive model
based on a combination of self-reported symptoms, sociodemographic variables and clinical
parameters could serve as a reliable screening method for the stepped diagnosis of SAS.
METHODSParticipants
We conducted a retrospective study of a consecutive cohort of 620 patients with stroke
who were admitted to the stroke rehabilitation unit of Heliomare, a rehabilitation center
in the Netherlands, between May 2007 and July 2012. All patients referred to the stroke
rehabilitation unit were able to participate in active multidisciplinary stroke rehabilitation and
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had a good prognosis for return to home. Patients were excluded from screening if they were
< 18 years, or if they had a previous diagnosis and treatment of SAS. In total, 533 patients
with stroke agreed to SAS screening. Patients were included in our study when diagnosis of
stroke was confirmed by a neurologist based on a history of sudden onset of a neurological
deficit lasting more than 24 hours and a brain lesion compatible with the neurological deficit
on computerized tomography or magnetic resonance imaging. Patients were excluded if the
SAS questionnaire was not completed (n = 28) or nocturnal oximetry was not performed
(n = 55) (e.g., because of to severe aphasia, confusion or unwillingness to participate`). In total
438 patients met the inclusion and exclusion criteria and were included in the further analysis.
The ethics committee of the institutional review board approved the study protocol.
Procedure and measurements
Within 4 weeks of admission to the stroke rehabilitation unit, an SAS questionnaire was
administered in a face-to-face interview with a trained nurse. The SAS questionnaire consists
of 3 categories: sociodemographic characteristics, self-reported symptoms, and clinical
characteristics. Questions on sociodemographic characteristics were composed of sex, age,
partner status, and smoking. The self-reported symptoms assessed in the questionnaire are
based on the features of SAS described in the International Classification of Sleep Disorders by
the American Academy of Sleep Medicine11 and include questions on snoring, apneas, daytime
sleepiness, fatigue, falling asleep during the day, not waking up refreshed, concentration loss,
morning headaches, restless legs, mood disturbances, and irritability. For each question, the
patient is asked to report if the symptom was present (yes or no). The nurse extracted clinical data
from the medical records, including stroke subtype, stroke localization, first or recurrent stroke,
comorbidities (hypertension, diabetes, thyroid disease, pulmonary and cardiac comorbidities),
body mass index (BMI) and blood pressure. Within 8 weeks of admission, nocturnal oximetry
was conducted to determine the oxygen desaturation index (ODI). Validation of nocturnal
oximetry against multichannel polygraphy showed that the ODI is a close estimate of the
apnea-hypopnea index (AHI) in patients with stroke, with excellent diagnostic accuracy of 93%
for the detection of SAS in stroke rehabilitation.6 The pulse oximetera was preprogrammed to
collect data between 11 pm and 7 am. Recordings were analyzed automatically. The ODI was
defined as the mean number of desaturations of ≥ 3% from baseline per hour. In accordance
with the guideline for diagnosis of SAS (apnea-hypopnea index ≥ 15), we classified patients
with an ODI of ≥ 15 as having a high likelihood of SAS.
Statistical analysis
We used descriptive statistics to characterize the patient sample. We calculated group
differences between patients with ODI < 15 and ODI ≥ 15 using chi-square or Student t tests.
The prediction model was developed in 2 consecutive steps. Because of the large number of
possible predictive variables in relation to the sample size, the first step was to select potential
predictors for high likelihood of SAS (ODI ≥ 15) using univariable regression. We selected the
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variables that were univariably associated with high likelihood of SAS with a probability value
of < 0.20. This way we reduced the number of variables without discarding potential important
predictors. All variables were checked for collinearity. In the second step, a multivariate analysis
was performed using stepwise manual backward selection. In this stepwise selection, the
variable with the lowest predictive value was manually removed from the model until further
elimination resulted in a statistically significant decrement of the model fit with the likelihood
ratio test (p < 0.05) or the variables in the model were all significantly associated with ODI
≥ 15 (p < 0.10). The probability of a patient having a high likelihood of SAS is determined as
p = (ey) / (1 + ey), where y is a linear combination of explanatory variables.
The area under the receiver-operator characteristic (ROC) curve was used to assess the
accuracy of the prediction model for high likelihood of SAS. In the ROC curve, the true
positive rate (sensitivity) is plotted against the false-positive rate (1 - specificity). An area
under the curve of 0.5 indicates no discrimination above chance, whereas an area under
the curve of 1.0 indicates perfect discrimination. Sensitivity, specificity and predictive value
with 95% confidence intervals (CIs) were calculated for different probability cutoffs of the
model. All analyses were conducted using SPSS version 19.0.b
RESULTSIn total, 438 patients with stroke were included in the study (Table 1). Of these patients,
31% had high likelihood of SAS (ODI ≥ 15). The mean age of the patients was 58.4 years.
Most patients were men, and almost half of the patients was either overweight (BMI ≥ 25)
or obese (BMI ≥ 30). Three quarters of the patients had ischemic stroke. The most prevalent
comorbidity was hypertension, followed by cardiac problems and diabetes. Patients with s
high likelihood of SAS were more likely to be men, be older, and have a higher BMI than
patients without a high likelihood of SAS. Hypertension, diabetes and cardiac comorbidity
were significantly more common in patients with a high likelihood of SAS.
The most frequently reported symptom was snoring followed by fatigue, whereas the
presence of apneas was only reported by a small proportion of patients (Table 2). Patients
with a high likelihood of SAS were more likely to report apneas, snoring, falling asleep
during daytime and depressed mood than patients without a high likelihood of SAS.
Predictive model for a high likelihood of SAS
The variables that showed an univariable association (p ≤ 0.20) with a high likelihood
of SAS were selected for the backward stepwise analysis. Of the sociodemographic
characteristics, sex and age were associated with high likelihood of SAS. In the category
self-reported symptoms, apneas, snoring, concentration loss, falling asleep during
daytime, morning headaches and mood disturbances were related to a high likelihood
of SAS. Clinical variables that were associated with a high likelihood of SAS were BMI,
systolic blood pressure, hypertension, diabetes, and cardiac comorbidity. These variables
were entered into the multivariate logistic regression analysis. The final model for a high
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Table 1. Descriptive statistics of sociodemographic and clinical variables stratified by the ODI
Total (n=438) ODI < 15 (n=301) ODI ≥ 15 (n=137) P-value
Age (y) 58.4 ± 10.1 57.1 ± 10.3 61.1 ± 9.1 <0.001
Sex <0.001
♂ 256 (58.4) 157 (52.2) 99 (72.3)
♀ 182 (41.6) 144 (47.8) 38 (27.7)
Partner (yes) 315 (71.9) 214 (71.1) 101 (73.7) 0.57
Smoking (yes) 131 (29.9) 88 (29.2) 43 (31.4) 0.57
BMI 25.7 ± 4.5 24.8 ± 3.9 27.8 ± 5.0 <0.001
25-29.9 (overweight) 209 (34.0) 91 (30.2) 58 (42.3) <0.001
≥ 30 (obese) 60 (13.7) 28 (9.3) 32 (23.4) <0.001
ODI 13.4 ± 14.5 5.8 ± 4.17 30.0 ± 15.2 <0.001
Blood pressure
Systolic 131.9 ± 18.4 130.7 ± 18.0 134.6 ± 19.0 0.04
Diastolic 79.9 ± 10.5 79.7 ± 10.8 80.3 ± 9.9 0.59
Stroke subtype 0.42
Ischemia 331 (75.5) 223 (74.1) 108 (78.8)
Hemorrhage 79 (18.0) 56 (18.6) 23 (17.0)
Subarachnoid hemorrhage 28 (6.4) 22 (7.3) 6 (4.4)
Stroke location* 0.53
Cortex 308 (75.1) 210 (75.3) 98 (74.8)
Subcortex 40 (9.8) 24 (8.6) 16 (12.2)
Cerebellum 37 (9.0) 27 (9.7) 10 (7.6)
Brain stem 25 (6.1) 18 (6.5) 7 (5.3)
Recurrent stroke 63 (14.4) 44 (14.6) 19 (13.9) 0.86
Hypertension 221 (48.2) 135 (44.9) 76 (55.5) 0.04
Cardiac comorbidity 108 (24.7) 67 (22.3) 41 (29.9) 0.08
Diabetes 52 (11.9) 29 (9.6) 23 (16.8) 0.03
Pulmonary comorbidity 47 (10.7) 30 (10.0) 17 (12.4) 0.44
Thyroid disease 14 (3.2) 8 (2.7) 6 (4.4) 0.34
Values are presented as mean ± SD, n (%), or as otherwise indicated. ODI, oxygen desaturation index; * location is given for ischemia and hemorrhage.
likelihood of SAS consisted of the following predictive variables: sex (men), age, BMI, and
the self-reported symptoms of apneas and falling asleep during daytime. All variables were
significantly associated with a high likelihood of SAS (p < 0.10), and when the variable
with the lowest predictive value was removed (falling asleep during daytime), the likelihood
ratio showed significant decrease of the model fit (p < 0.05). Therefore, all variables were
retained in the model. The parameter estimates are shown in Table 3. According to the
Hosmer-Lemeshow statistic, the reliability of the model was adequate (p = 0.71).
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Table 2. Number and percentage of the subjects reporting each of the symptoms, stratified by the ODI
Symptom Total (n=438) ODI < 15 (n=301) ODI ≥ 15 (n=137) P-value
Snoring 291 (66.4) 188 (62.5) 103 (75.2) 0.09
Fatigue 256 (58.6) 177 (58.8) 79 (58.1) 0.89
Daytime sleepiness 214 (49.1) 142 (47.5) 72 (52.6) 0.33
Concentration loss 196 (44.9) 141 (47.0) 55 (40.1) 0.18
Falling asleep during daytime 161 (36.8) 97 (32.3) 64 (46.7) 0.01
Not waking up refreshed 140 (32.0) 97 (32.3) 43 (31.4) 0.88
Restless legs 123 (28.1) 84 (28.0) 39 (28.5) 0.91
Mood disturbances 92 (21.0) 71 (23.6) 21 (15.3) 0.05
Irritability 87 (19.9) 61 (20.3) 26 (19.1) 0.78
Morning headaches 77 (17.7) 58 (19.3) 19 (14.0) 0.17
Apnea 66 (15.1) 29 (9.6) 37 (27.4) <0.001
ODI, oxygen desaturation index.
Table 3. Multivariable model with predictors of high likelihood of SAS
Predictor Odds ratio 95%CI P-value
Age (in years) 1.04 1.01-1.07 0.002
Sex (♂) 2.32 1.39-3.85 0.001
BMI (per point) 1.17 1.10-1.24 <0.001
Apnea (yes) 2.32 1.23-4.37 0.009
Falling asleep during daytime (yes) 1.81 1.12-2.91 0.015
CI, confidence interval.
The diagnostic accuracy of the prediction model is represented by the ROC curve in
Figure 1. The area under the curve was 0.76 (95% CI, 0.71-0.81), which is considered
acceptable.12 This means that for 2 randomly selected patients, the probability that the
model would correctly classify the patient with a high likelihood of SAS was 76%.
The probability of an individual patient having a high likelihood of SAS was determined
using the following algorithm: p = (ey) / (1 + ey), with y = -8.15 + 0.04 x Age + 0.84 x Sex
+ 0.16 x BMI + 0.84 x Apneas + 0.59 x Falling asleep. The equation parameters are age
(y), sex (0 for women, 1 for men), BMI (points), and self-reported symptoms apneas and
falling asleep (1 for present, 0 for not present).
To illustrate the algorithm, we present a 55-year old man with stroke. The patient
has a BMI of 26 with complaints of falling asleep during daytime, but he has no reported
apneas. The corresponding algorithm is y = -8.15 + 2.2 + 0.84 +4.16 + 0 + 0.59= -0,36;
P = (e-.36) / (1 + e-.36) = 0.41. Therefore, this individual patient has a predicted probability
of 0.41 of having a high likelihood of SAS.
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4Figure 1. The ROC curve for the prediction model for a high likelihood of SAS. The prediction model includes the variables: age, sex, BMI, apneas and falling asleep during daytime. The area under the curve is 0.76 (95% CI, 0.71-0.81).
In Figure 2, the sensitivity and specificity are plotted against selected cutoff points for
the predicted probability of a high likelihood of SAS. If we set the cutoff for predicted
probability of high likelihood of SAS at 0.60, only 24% (95% CI, 17%-33%) of the
patients would be correctly identified (sensitivity), while 97% (95% CI, 93%-98%) would
be correctly excluded (specificity). In this example, the positive predictive value is 75%
(95% CI, 58%-87%). If, on the other hand, we choose a low cutoff score of 0.1 for the
predicted probability of a high likelihood of SAS, the sensitivity would increase to 95%
(95% CI, 89-98%), but the specificity would drop to 21% (95% CI, 17%-27%). The
corresponding positive predictive value is 34% (95% CI, 30%-40%). A cutoff score of 0.3
would lead to almost equal sensitivity and specificity of 72% (95% CI, 63%-79%) and
Figure 2. Sensitivity and specificity for selected cutoff points of the predicted probability of a high likelihood of SAS.
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Table 4. Sensitivity, specificity, PPV, NPV, LR+, and LR- with 95% CI at selected cutoff points for the predicted probability of a high likelihood of SAS
Cutoff Sensitivity Specificity PPV NPV LR+ LR-
0.1 95 (89-98) 21 (17-27) 34 (30-40) 91 (81-96) 1.2 (1.1-1.3) 0.2 (0.1-0.5)
0.3 72 (63-79) 69 (63-74) 50 (43-58) 85 (80-89) 2.3 (1.9-2.8) 0.4 (0.3-0.5)
0.6 24 (17-33) 97 (93-98) 75 (58-87) 74 (70-79) 6.9 (3.5-13.6) 0.8 (0.7-0.9)
LR+, positive likelihood ratio; LR -, negative likelihood ratio; NPV, negative predictive value; PPV, positive predictive value.
69% (95% CI, 63%-74%), respectively, and a positive predictive value of 50% (95% CI,
43-58%). In Table 4, the sensitivity, specificity, predictive values, and likelihood ratios for
the three selected cutoff points are presented.
DISCUSSIONThe primary aim of this study was to investigate whether a prediction model combining
self-reported symptoms, sociodemographic variables and clinical parameters could
serve as a reliable first screening method in the step-by-step diagnosis of SAS in stroke
rehabilitation. Our final prediction model included the parameters sex, age, BMI, and
self-reported symptoms apneas and falling asleep during daytime. The overall diagnostic
accuracy of the model was considered acceptable (76%), making our prediction model a
reasonable first screening tool in stepped detection of SAS.
Our prediction model gives physicians the opportunity to choose a cutoff they
find acceptable. For example, when a low probability cutoff (0.1) is chosen, the
model is very sensitive without being specific, whereas a high cutoff (0.6) yields good
specificity but low sensitivity. At a chosen cutoff of 0.3 the model has equal sensitivity
and specificity of around 70%. When a cutoff is set, patients that are classified with
a high likelihood of SAS by our prediction model should be further examined with
nocturnal oximetry as part of the stepped diagnostic approach. Subsequently, patients
with suspicion of SAS based on nocturnal oximetry should be assessed with a full sleep
examination for true diagnosis of SAS.
Furthermore, we aimed to investigate whether incorporation of sociodemographic
and clinical features into self-report SAS questionnaires improves the screening capability
in patients with stroke. This question was posed in response to 3 recently published studies
reporting disappointing results on the clinical value of the BQ in patients with stroke.4,8,9 In
all three studies, the BQ was found to be only moderately sensitive (56%-77%) and only
one study reported good specificity (86% versus 15%-54%).8 Our model partially overlaps
with the BQ; in both models self-reported apneas and falling asleep during driving (BQ)
or daytime (our model) and BMI are included. In addition, our model also takes the
sociodemographic variables age and sex into account. A direct comparison with the BQ
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and our model is difficult because the BQ was validated against polysomnography and our
prediction model against nocturnal oximetry. Moreover, the BQ has a predefined cutoff,
and our model does not. It is, however, possible to examine the diagnostic accuracy of both
screening instruments measured by the ROC curve. Kotzian et al9 reported a diagnostic
accuracy of the BQ of 58%, which does not significantly differ from chance. In our model,
the diagnostic accuracy was 76%, suggesting that addition of sociodemographic and
clinical parameters improves the predictive value of a self-report questionnaire.
Notably, only 2 of the 11 self-reported symptoms were included in our final predictive
model. There are several possible explanations for this. First, patients with stroke may not be
aware of the presence of SAS-related symptoms. Such lack of awareness or underestimation
of symptoms is called anosognosia and is a frequent result of a stroke.13 Nevertheless, this
does not seem to be the most probable explanation because a number of complaints (e.g.,
fatigue and sleepiness) is reported by most patients. An alternative explanation is that SAS-
and stroke-related symptoms overlap, making it difficult to differentiate between patients
with and without a high likelihood of SAS based on self-reported symptoms. This is best
illustrated by self-reported fatigue. Fatigue is a well-known symptom of SAS,14 but is also
the most common complaint after stroke with a reported prevalence of between 36% and
77%.15 In our study, we found that 58% of the patients with and without a high likelihood
of SAS reported fatigue, which is in line with the review by Lerdal et al.15 Fatigue is not the
only symptom that shows overlap; cognitive impairments, mood disturbances, and irritability
are also often reported as a consequence of both stroke and SAS.16-18
In the general sleep clinic population, the predictive value of sociodemographic and
clinical features for SAS has been the subject of research since the 1990s, and a number
of predictive models have been reported in the literature.19-22 Most of these proposed
models include age, sex and BMI. Clinical variables that were less often included in these
models are pharyngeal examination, alcohol intake, and reported snoring or apneas. The
sensitivity and specificity of these models range widely between studies because of the
different cutoff values used. Two studies also included ROC curves, reporting area’s under
the curve of 0.77 and 0.79.19, 21 The variables in our model are very consistent with these
earlier findings, as is the diagnostic accuracy of our model. These similarities suggest that
the sociodemographic and clinical parameters that are predictive for SAS in patients with
do not differ from patients referred to general sleep clinics.
STUDY LIMITATIONSThe main limitation of this study is that nocturnal oximetry was performed rather than
polysomnography or polygraphy. At least polygraphy is needed for the true diagnosis of
SAS; however, previous studies have shown that nocturnal oximetry gives a highly accurate
estimation of the SAS severity.6, 23 In accordance with these findings, the prevalence
of a 31% high likelihood of SAS found in our study is consistent with findings from
a meta-analysis by Johnson and Johnson.3 They included 6 studies performed in stroke
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rehabilitation units and found prevalence rates for SAS ranging from 25% to 54% (apnea-
hypopnea index ≥ 15). On the other hand, the use of nocturnal oximetry can also be seen
as an advantage of this study. Polysomnography and polygraphy are often perceived as
burdensome and may therefore be rejected by (more seriously affected) patients with
stroke. Nocturnal oximetry, however, is better tolerated because it is measured with a
small instrument composed of only a wristband and finger sensor. In our study, 90% of
the patients with stroke accepted nocturnal oximetry.
A second limitation is that the study was conducted retrospectively; as a result, we
cannot ascertain the individual reasons why 87 of the 620 patients with stroke were
not screened for SAS during their inpatient rehabilitation at Heliomare. Although the
percentage of missed cases is proportionally small, we cannot entirely rule out some
degree of a selection bias.
A third limitation of the study is that the self-reported questionnaire used binary
response choices (yes versus no). The patient could not indicate the severity of
experienced symptoms as is possible on the BQ. We chose this scale because based on
the communication strategies by the National Aphasia Association,24 we expected that
most patients with aphasia would be better able to answer simple ye or no questions than
more complex ones. For future research it would be interesting to investigate whether the
severity of symptoms could add to the predictive value.
CONCLUSIONThe final prediction model for a high likelihood of SAS includes the parameters sex, age,
BMI, and self-reported symptoms apneas and falling asleep during daytime. The model
yields acceptable diagnostic accuracy. Therefore, we conclude that our prediction model
can serve as a reasonable first screening method in a stepped diagnostic approach to SAS
in stroke rehabilitation.
SUPPLIERSa. 3100 WristOx; Nonin Medical Inc, 13700 1st Ave N, Plymouth, MN 55441-5443.
b. SPSS Inc, 233 S Wacker Drive, 11th Fl, Chicago, IL 60606.
ACKNOWLEDGEMENTSWe thank the Respicare team within Heliomare Rehabilitation Center for their assistance
with data collection.
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1 Turkington PM, Allgar V, Bamford J, Wanklyn P, Elliott MW. Effect of upper airway obstruction in acute stroke on functional outcome at 6 months. Thorax 2004;59:367-371.
2 Young T, Finn L, Peppard PE, Szklo-Coxe M, Austin D, Nieto FJ, et al. Sleep disordered breathing and mortality: Eighteen-year follow-up of the Wisconsin sleep cohort. Sleep 2008;31:1071-1078.
3 Johnson KG, Johnson DC. Frequency of sleep apnea in stroke and TIA patients: A meta-analysis. J Clin Sleep Med 2010;6:131-137.
4 Srijithesh PR, Shukla G, Srivastav A, Goyal V, Singh S, Behari M. Validity of the Berlin questionnaire in identifying obstructive sleep apnea syndrome when administered to the informants of stroke patients. J Clin Neurosci 2011;18:340-343.
5 Epstein L, Kristo D, Strollo P, Friedman N, Malhotra A, Patil S, et al. Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. J Clin Sleep Med 2009;5:263-276.
6 Aaronson JA, van Bezeij T, van den Aardweg JG, van Bennekom CAM, Hofman WF. Diagnostic accuracy of nocturnal oximetry for detection of sleep apnea syndrome in stroke rehabilitation. Stroke 2012;43:2491-2493.
7 Netzer NC, Stoohs RA, Netzer CM, Clark K, Strohl KP. Using the Berlin questionnaire to identify patients at risk for the sleep apnea syndrome. Ann Intern Med 1999;131:485-91.
8 ElKholy SH, Amer HA, Nada MM, Nada MAF, Labib A. Sleep-related breathing disorders in cerebrovascular stroke and transient ischemic attacks: A comparative study. J Clin Neurophysiol 2012;29:194-198.
9 Kotzian S, Stanek J, Pinter M, Grossmann W, Saletu M. Subjective evaluation of sleep apnea is not sufficient in stroke rehabilitation. Top Stroke Rehabil 2012;19:45-53.
10 Young T, Skatrud J, Peppard PE. Risk factors for obstructive sleep apnea in adults. JAMA 2004; 291:2013-2016.
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revised: Diagnostic and coding manual. Westchester, IL: American Academy of Sleep Medicine 2001.
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18 Nys G, Van Zandvoort M, De Kort P, Van der Worp H, Jansen B, Algra A, et al. The prognostic value of domain-specific cognitive abilities in acute first-ever stroke. Neurology 2005;64:821-827.
19 Viner S, Szalai JP, Hoffstein V. Are history and physical examination a good screening test for sleep apnea? Ann Intern Med 1991;115:356-359.
20 Hoffstein V, Szalai J. Predictive value of clinical features in diagnosing obstructive sleep apnea. Sleep 1993;16:118-122.
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REFERENCES
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Aaronson JA, van Bennekom CAM, Hofman WF, van Bezeij T, van den Aarweg JG,
Groet E, Kylstra WA, Schmand BA
BMC Neurology 2014; 14:36
THE EFFECT OF OBSTRUCTIVE SLEEP APNEA AND TREATMENT
WITH CONTINUOUS POSITIVE AIRWAY PRESSURE ON STROKE REHABILITATION: RATIONALE,
DESIGN AND METHODS OF THE TOROS STUDY
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ABSTRACTBackground Obstructive sleep apnea is a common sleep disorder in stroke patients.
Obstructive sleep apnea is associated with poor functional outcome and increased
mortality. Continuous positive airway pressure seems to improve functional recovery
in stroke rehabilitation. To date, the effect of continuous positive airway pressure on
cognitive functioning in stroke patients is not well established. The current study will
investigate the effectiveness of continuous positive airway pressure on both cognitive and
functional outcomes in stroke patients with obstructive sleep apnea.
Methods/Design A randomized controlled trial will be conducted on the
neurorehabilitation unit of Heliomare, a rehabilitation center in the Netherlands. Seventy
stroke patients with obstructive sleep apnea will be randomly allocated to an intervention
or control group (n = 2 × 35). The intervention will consist of four weeks of continuous
positive airway pressure treatment. Patients allocated to the control group will receive four
weeks of treatment as usual. Outcomes will be assessed at baseline, immediately after the
intervention and at two-month follow-up. In a supplementary study, these 70 patients
with obstructive sleep apnea will be compared to 70 stroke patients without obstructive
sleep apnea with respect to cognitive and functional status at rehabilitation admission.
Additionally, the societal participation of both groups will be assessed at six months and
one year after inclusion.
Discussion This study will provide novel information on the effects of obstructive sleep
apnea and its treatment with continuous positive airway pressure on rehabilitation
outcomes after stroke.
Trial registration Trial registration number: Dutch Trial Register NTR3412.
Key words Stroke, Rehabilitation outcome, Obstructive sleep apnea, CPAP, Randomized
controlled trial, Cognition, Functional status
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BACKGROUNDObstructive sleep apnea (OSA) is a sleep disorder characterized by repetitive cessations
of breathing during sleep due to obstruction of the upper airway. The diagnosis of OSA
is based on the mean number of apneas and hypopneas per hour sleep, the apnea-
hypopnea index (AHI). Nighttime consequences of OSA are periodic oxygen desaturations,
increases in blood pressure and sleep fragmentation. OSA is associated with an increased
risk for cardiovascular diseases, such as hypertension, heart disease and stroke.1,2 A recent
meta-analysis reported a prevalence of sleep apnea between 38-72% in stroke patients
compared to 3-28% in the general population.3,4
The most frequently reported daytime consequences of OSA are excessive daytime
sleepiness and fatigue. There is also growing evidence that OSA negatively affects
cognitive functioning and mood. Most studies find impairment in the cognitive domains
of vigilance, attention, executive functioning, memory and motor coordination.5,6 The
etiology of the cognitive impairment in OSA patients is still unclear, but neuroimaging
studies provide evidence that OSA is associated with structural and functional changes in
the brain, particularly in the frontal cortex and hippocampus.7,8
Continuous positive airway pressure (CPAP) is the treatment of choice for OSA. CPAP
improves breathing during sleep, resulting in better blood oxygen saturation and less sleep
fragmentation.9 Consequently, it is expected that CPAP treatment will improve daytime
functioning. However, evidence of the therapeutic effects of CPAP on fatigue, cognitive
functioning and mood is inconsistent. A Cochrane review showed significant reduction
of objective and subjective sleepiness, and depressive symptoms, while in a recent meta-
analysis of cognitive functioning after CPAP we only found slight improvement in the
attention domain.10,11 Notwithstanding this limited evidence for behavioral effects of CPAP
treatment, there are some indications that it results in structural changes in the brain. One
imaging study showed that CPAP treatment was associated with increase of grey matter
in the hippocampal and frontal structures.8
OSA is a common sleep disorder in stroke and has been reported to be associated
with poor functional outcome, recurrent stroke, and increased mortality.2,12-14 In a cross-
sectional study in stroke patients, OSA was found to be associated with delirium, depressed
mood and impaired activities of daily living, but no relationship was seen between OSA and
performance on a cognitive screening instrument (Mini Mental State Examination; MMSE).15
The authors suggested the use of more sensitive neuropsychological tests. We conducted
a pilot study examining the effect of OSA on a set of neuropsychological tests in stroke
patients.16 In this small sample, OSA was found to be associated with lower performance on
tasks of attention, verbal memory and visual scanning, and increased depressive symptoms.
To date, six randomized controlled trials (RCTs) have compared CPAP to treatment as usual
(TAU) or sham CPAP in stroke patients. Four of these studies found improvement in CPAP
compared to TAU in one or more neurological function outcomes, sleepiness or mood17-20,
while the other studies found no benefit of CPAP treatment in these areas compared to TAU
or sham CPAP.20,21 The latter studies had low compliance and insufficient power due to small
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sample size, which may have affected the results. The effect of CPAP on cognitive functioning
was evaluated in three studies. Two studies used the MMSE20,21 and one study19 assessed the
sustained attention to response test (a measure of vigilance) and the digit or spatial span
backward test (a measure of executive function). None of these cognitive measures showed
improvement with CPAP. The effect on more extensive neuropsychological assessment
including sensitive measures of memory and attention has not yet been investigated.
In summary, previous research suggests that OSA is associated with poor functional
outcome after stroke, and that treatment with CPAP improves functional recovery during
stroke rehabilitation. However, the effect of OSA and CPAP treatment on cognitive
functioning in stroke patients is still unclear. The Treatment of OSA and Rehabilitation
Outcome in Stroke (TOROS) study is designed to address these issues.
The TOROS project consists of a randomized controlled trial (RCT), and a supplementary
case-control study. The primary objective of the RCT is to evaluate whether CPAP treatment
improves cognitive and functional outcomes in stroke patients with OSA (AHI ≥ 15), as assessed
by a neuropsychological test battery and by measures of neurological status and functional
dependence. The aim of the supplementary case-control study is to explore the association
between OSA and cognitive and functional status in stroke patients. Our hypothesis is that
stroke patients with OSA have a poorer functional status and greater cognitive impairment
compared to stroke patients without OSA. In both the RCT and the case-control study the
secondary objectives are: 1) to evaluate the effects of OSA (and its treatment) on sleep quality,
fatigue and mood; and 2) to evaluate the long-term effects of OSA on societal participation.
METHODS Design
In the RCT, 70 OSA patients will be randomized to receive four weeks of CPAP treatment
or four weeks of TAU. Patients randomized to TAU will start with CPAP treatment after
the four-week intervention period. Neuropsychological assessment and examination of
functional status, the primary outcome measures, are conducted at baseline and repeated
after the four-week intervention period and at two-month follow-up. The secondary
measures sleep quality, fatigue, and mood are assessed at the same time points as the
primary outcome measures. To ascertain whether OSA improves with CPAP treatment,
direct measurements of OSA, including AHI and level of oxygen desaturation during sleep,
are made at baseline, at the end of the intervention period, and at two-month follow-
up. To assess treatment compliance, the CPAP device registers CPAP usage per night, the
air pressure and the residual AHI. At four weeks, and three, six and twelve months after
enrollment the rehabilitation physician checks the compliance.
To test the hypothesis of the case-control study that OSA is associated with poor functional
status and greater cognitive impairment, 70 stroke patients diagnosed with OSA are compared
to 70 stroke patients without OSA on the primary and secondary outcomes at admission to
the rehabilitation unit (baseline). In a subgroup of the stroke patients without OSA (n = 35)
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the assessment of primary and secondary outcome measures will be repeated after four
weeks and three months. The outcomes will be compared to the recovery of OSA patients. All
patients included in the TOROS study are asked to fill out a questionnaire on participation in
work, leisure and social activities at six months and one year after inclusion. The study design
is illustrated in Figure 1. The Medical Ethical Review Board of the Academic Medical Centre,
Amsterdam, approved the study. The inclusion period started in October 2011. The study is
registered at the Dutch trial register (http://www.trialregister.nl) and identified as NTR3412.
Figure 1. Flowchart of the TOROS study. OSA, obstructive sleep apnea; CPAP, continuous positive airway pressure; TAU, treatment as usual.
Patient sample and procedures
This study is conducted in Heliomare rehabilitation center in the Netherlands. Annually,
approximately 150 stroke patients are admitted to Heliomare. Neurologists or rehabilitation
physicians of hospitals in the surrounding area refer patients. Patients with vascular
dementia or comorbid neurodegenerative diseases (e.g., Alzheimer’s or Parkinson’s disease)
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are excluded from rehabilitation in Heliomare and are referred to the rehabilitation unit
of a nursing home. The majority of the admitted patients are in the post-acute phase,
i.e., on average, two to three weeks post-stroke. The average length of stay in Heliomare
is nine weeks (standard deviation of four weeks). An earlier study showed that the
prevalence of OSA in stroke patients admitted to Heliomare is around 50%.23 At admission
to the rehabilitation unit, stroke severity and functional dependence scales are used to
determine the patient’s functional status. In the first week of hospitalization, patients are
interviewed with a questionnaire to determine the presence of common symptoms of
OSA. Subsequently, their oxygen desaturation index (ODI), defined as the mean number of
desaturations of ≥ 3% from baseline per hour, is assessed with nocturnal pulse oximetry.
Patients with an ODI of five or higher are further tested for OSA by polygraphy. Patients
with an AHI of 15 or higher on polygraphy are diagnosed with OSA and patients with a
normal ODI (< 5)23,24 or AHI below 15 are defined as non-OSA patients. Within four weeks
of admission, a neuropsychological assessment is administered to determine the level of
cognitive functioning. Stroke patients meeting the following inclusion criteria are invited
to participate within six weeks from admission to Heliomare: [1] stroke confirmed by a
neurologist, [2] age between 18 and 85 years, [3] admission to Heliomare between 1 and 16
weeks post-stroke, and [4] able to participate in the OSA screening and neuropsychological
assessment. Exclusion criteria are: [1] severe unstable medical conditions, respiratory failure
or history of severe congestive heart failure, [2] traumatic brain injury, [3] severe aphasia,
confusion or psychiatric comorbidity, [4] central sleep apnea or previously diagnosed OSA.
Patients with severe OSA (AHI >60 and oxygen desaturations below 70%), which could
endanger the patient’s health if treatment is not immediately started, are excluded from
the RCT part of the study. Before inclusion, patients give written informed consent.
Randomization and blinding
Participants with OSA will be randomized immediately after the baseline assessments.
The minimization technique25 will be used to minimize imbalance between the treatment
arms for age, severity of OSA, stroke subtype (ischemic or hemorrhage) and severity of
cognitive impairment. Assessors of cognitive and functional outcome measures will be
blinded to treatment allocation.
Intervention
CPAP treatment is set up and monitored by a specialized ‘Respicare’ team. This team exists
of two rehabilitation physicians, two nurse practitioners and four nurses working on the
neurorehabilitation unit specialized in sleep and breathing disorders. Before treatment
is initiated, a CPAP mask, connecting hose and CPAP device are set up for each patient.
Different masks are used, from small nasal pillows to a full-face mask, depending on the
patient’s preference. Personalized instructions are given by one of the Respicare team
members and a written manual for the CPAP device is provided. If possible, the partner or
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a close relative is also provided with instructions on the use of the CPAP device. Patients
are asked to wear the mask for a short period during the day to become accustomed to
using the CPAP device. Within the first week, the CPAP treatment is evaluated together
with the patient and CPAP titration is performed using pulse oximetry. The pressure is
adjusted until the ODI is reduced to normal (ODI <5). If titration by nocturnal oximetry
fails to adequately reduce the ODI, CPAP is titrated by polygraphy to reduce the AHI to <5
or to the highest pressure tolerated. The CPAP device is provided with a memory card to
evaluate the effectiveness of CPAP therapy over time and to monitor CPAP compliance.
The Respicare team has contact with the patients regularly during the intervention period
to help troubleshoot problems and encourage compliance. Patients who are discharged
during the four-week intervention period are followed-up by telephone.
Primary outcomes
The primary outcome measures of this study are cognition and functional status. The following
nine cognition domains are assessed: vigilance, attention, memory, working memory,
executive functioning, language, visuoperception, psychomotor ability and intelligence. The
neuropsychological test battery of standardized neuropsychological tests26 is assessed by a
trained psychological assistant. For a number of cognitive domains, non-verbal alternative
tests are included for patients with aphasia. A language comprehension test is also included
for aphasic patients. The obtained test scores are transformed into demographically corrected
z-scores. In case of multiple tests within one domain, the average z-score for the domain is
calculated. In the Appendix all tests are summarized by cognitive domain.
Functional status is assessed by measures of neurological status and functional
dependence. The rehabilitation physician administers two scales of neurological status,
the Canadian Neurological Scale27 and the National Institutes of Health Stroke Scale28. The
obtained scores are transformed into z-scores and averaged into one score for neurological
status. The nurse practitioner scores the level of functional dependence on the Utrecht
Scale for Evaluation of Rehabilitation29. This scale can be converted to the Barthel Index30.
Secondary outcomes
Secondary outcome measures of the TOROS study are sleepiness (Stanford Sleepiness Scale),
fatigue (Checklist Individual Strength), mood (Hospital Anxiety and Depression Scale), and
subjective sleep quality (Sleep Quality Scale).31-34 A trained psychological assistant administers
these measures after the neuropsychological assessment. The Utrecht Scale for Evaluation
of Rehabilitation – Participation35 is used to evaluate work, leisure and social activities at six
months and one year after the inclusion. The patients complete this questionnaire at home.
Objective measurements of sleep are made using standardized pulse oximetry
(WristOx®; Nonin Medical, Plymouth, USA) and ambulatory overnight cardiorespiratory
polygraphy (Embletta®; Embla, Ottowa, Canada). The ODI (mean number of oxygen
desaturations of ≥ 3% per hour) is calculated from the pulse oximetry data using
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automated analysis. Trained staff manually scores the polygraph recordings. Apnea is
defined as a reduction of airflow of ≥ 90% for at least 10 seconds and hypopnea is
defined as a reduction of airflow of ≥ 50% for at least 10 seconds followed by an oxygen
desaturation of ≥ 3%. The AHI is defined as the mean number of apneas and hypopneas
per hour in bed. The change of AHI measured by polygraphy and treatment compliance
are used as moderating variables in the RCT.
A self-report questionnaire is administered to identify OSA symptoms (e.g., snoring,
daytime sleepiness, morning headaches). Socio-demographic and clinical characteristics
such as age, sex, education, body mass index (BMI), medication use and life style variables
(e.g., smoking) are obtained from medical records.
Sample size
We conducted two separate power analyses for the RCT; one for the functional outcome
and one for the cognitive outcome. The power calculation for the functional outcome is
based on two previous studies.18,19 These studies found large effects of CPAP treatment on
functional outcome measures in stroke patients. To detect at least one standard deviation
(large effect) on functional outcome with 90% power and an alpha of 0.05 (one-tailed),
a minimum of 34 patients (17 per arm) is required.
No reliable information on the expected effect of CPAP treatment on cognitive measures
is available. Therefore, an effect size of 0.75 standard deviation was used to estimate the
necessary sample size. Taken the same alpha level and 80% power, a minimum of 56 patients
(28 per arm) needs to be included in the RCT. The compliance in this study is expected to
be relatively high (around 67%) due to the relatively short duration of the experimental
treatment phase and the assistance of a specially trained staff. Taking the compliance rate
into account, a total of 70 patients will be the target sample size for the RCT.
For the supplementary case-control study, we set the sample size at 70 OSA patients
and 70 patients without OSA, which allows detection of a medium size effect (0.5 standard
deviation) with 90% power, and p-value set at 0.05.
Statistical analyses
All statistical analyses will be performed with SPSS 19 (IBM; Armonck, USA) or later
versions of this package. Depending on the level of measurement, parametric statistics
(Student’s t-test and analysis of (co)variance) or non-parametric statistics (χ2 and Fisher’s
exact test) will be used. Statistical significance will be set at a p-value of 0.05 or less. Socio-
demographic and clinical characteristics at baseline will be presented using descriptive
statistics. If group differences are observed at baseline on one or more background
variables, those variables will be included as covariates in all further analyses.
For the RCT, the therapy effects on the primary and secondary outcome measures will
be examined using a two factor (group x time) multivariate analysis of variance (MANOVA).
Effect sizes will be calculated for all analyses using standard statistical procedures. To
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control for the multiple comparisons of cognition and functional status, two separate
MANOVA’s will be performed. Given that we have two primary outcomes defined for the
study (i.e., cognitive and functional outcomes), we will subsequently follow Hochberg’s
step-up procedure36 to control the false discovery rate.
Data will be analyzed according to the intention-to-treat principle. Additionally, a
per-protocol analysis will be performed. Depending on the variability of compliance, a
secondary analysis of good versus poor compliance will be conducted. In an exploratory
analysis, we will investigate whether the effectiveness of CPAP therapy varies as a function
of clinical diagnosis, level of cognitive functioning, and demographic background.
For the supplementary case-control part of the study, OSA patients will be compared
with the control group of non-OSA patients on primary and secondary outcome measures
using a MANOVA. As with the RCT, a separate analysis will be performed for cognition
and functional status, and the Hochberg’s step-up procedure will be followed. For the
follow-up of the USER-P questionnaire, within group changes will be assessed using paired
t-tests and between group comparison will be made with two sample t-test.
DISCUSSIONSleep apnea is highly prevalent in stroke patients. The TOROS study is designed primarily
to evaluate the effect of CPAP treatment on improvement of rehabilitation outcome in
stroke patients. In the RCT part of the study, conducted in stroke patients with OSA, CPAP
treatment is compared to TAU on both functional and cognitive outcome. There is growing
evidence that CPAP improves functional status,17-19 but the effect of CPAP on cognitive
functioning in stroke patients with OSA remains unclear. CPAP treatment of OSA patients
who did not suffer stroke has only a limited effect on cognitive functioning.11 However,
stroke patients with OSA have much more to gain with respect to cognitive functioning
than these ‘regular’ OSA patients. Thus, the cognitive effects of CPAP treatment may be
much larger in stroke patients with OSA.
Treatment compliance is a major practical problem with CPAP. A number of earlier
studies investigating the effects of CPAP treatment suffered from low compliance.20-22
To ensure treatment compliance within this project, a specialized Respicare team will
carefully monitor patients receiving CPAP treatment in order to quickly detect and resolve
problems, and motivate patients to continue use of the treatment. We expect that this will
enhance treatment compliance.
The supplementary case-control study will improve our understanding of the association
between OSA and cognitive impairments in the rehabilitation of stroke patients. Previous
studies provide provisional evidence that OSA in stroke patients is associated with poor
functional outcome,13 but the association between OSA and cognitive functioning in
stroke patients is far from clear. Studies conducted in regular sleep clinics found that
OSA negatively affected several cognitive domains.5,6 Therefore, it is expected that OSA in
stroke patients is associated with poor cognitive outcome.
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There are several limitations to the TOROS study that should be noted. Firstly, we
perform polygraphy rather than polysomnography to diagnose OSA. Our experience
is that polygraphy is often better tolerated by stroke patients, but polygraphy has
the disadvantage that it may give an underestimation of the AHI in case of low sleep
efficiency. Other limitations of the study are the relatively small sample size and short
CPAP intervention period. These are practical limitations that cannot be overcome given
both financial and time constraints in carrying out this research.
CONCLUSIONSTo date, there are no guidelines for the screening and treatment of OSA in stroke
rehabilitation. We believe that the TOROS study will add to the understanding of the
clinical implications of OSA in stroke rehabilitation, and the effects of CPAP treatment
on the rehabilitation outcome. This, in turn, will provide rehabilitation physicians with
evidence to help formulate future guidelines for OSA in stroke patients.
COMPETING INTERESTSThe authors declare that they have no competing interests.
AUTHORS’CONTRIBUTIONSCvB, TvB, EG and WK conceived the idea of this study. JA, CvB, WH, TvB, JvdA, EG, WK
contributed to the design of the study. JA coordinates the study under supervision of
BS, CvB and WH. TvB enables recruitment and monitors the intervention treatment. JA
was the primary author for this manuscript. BS helped draft the manuscript. All authors
critically reviewed the manuscript and approved the submitted version.
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1 Peppard PE, Young T, Palta M, Skatrud J: Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med 2000;342:1378–1384.
2 Young T, Finn L, Peppard PE, Szklo-Coxe M, Austin D, Nieto J, Stubbs R, Hla KM: Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep 2008;31:1071–1078.
3 Johnson KG, Johnson DC: Frequency of sleep apnea in stroke and TIA patients: a meta-analysis. J Clin Sleep Med 2010;6:131–137.
4 Young T, Peppard PE, Gottlieb DJ: Epidemiology of obstructive sleep apnea - a population health perspective. Am J Respir Crit Care Med 2002;165:1217–1239.
5 Beebe D, Groesz L, Wells C, Nichols A, McGee K: The neuropsychological effects of obstructive sleep apnea: a meta-analysis of norm-referenced and case-controlled data. Sleep 2003;26:298–307.
6 Aloia MS, Arnedt JT, Davis JD, Riggs RL, Byrd D: Neuropsychological sequelae of obstructive sleep apnea-hypopnea syndrome: a critical review. J Int Neuropsychol Soc 2004;10:772–785.
7 Zimmerman ME, Aloia MS: A review of neuroimaging in obstructive sleep apnea. J Clin Sleep Med 2006;2:461–471.
8 Canessa N, Castronovo V, Cappa SF, Aloia MS, Marelli S, Falini A, Alemanno F, Ferini-Strambi L: Obstructive sleep apnea: Brain structural changes and neurocognitive function before and after treatment. Am J Respir Crit Care Med 2011;183:1419–1426.
9 Lamphere J, Roehrs T, Zorick F, Conway W, Roth T: Recovery of alertness after CPAP in apnea. Chest 1989;96:1364–1367.
10 Giles TL, Lasserson TJ, Smith BJ, White J, Wright J, Cates CJ: Continuous positive airways pressure for obstructive sleep apnoea in adults. Cochrane Database Rev 2006;3: doi:10.1002/14651858.CD001106.pub3. Art. No.:CD001106.
11 Kylstra WA, Aaronson JA, Hofman WF, Schmand BA: Neuropsychological functioning after CPAP treatment in obstructive sleep apnea: a meta-analysis. Sleep Med Rev: in press, available online 23 October 2012.
12 Good DC, Henkle JQ, Gelber D, Welsh J, Verhulst S: Sleep-disordered breathing and poor functional outcome after stroke. Stroke 1996;27:252–259.
13 Kaneko Y, Hajek VE, Zivanovic V, Raboud J, Bradley TD: Relationship of sleep apnea to functional capacity and length of hospitalization following stroke. Sleep 2003;26:293–297.
14 Cherkassky T, Oksenberg A, Froom P, Ring H: Sleep-related breathing disorders and rehabilitation outcome of stroke patients: a prospective study. Am J Phys Med Rehab 2003;83:452–455.
15 Sandberg O, Franklin KA, Bucht G, Gustafson Y: Sleep Apnea, Delirium, depressed mood, cognition, and ADL ability after stroke. J Am Geriatr Soc 2001;49:391–397.
16 Jacobs J, Groet E, Schmand B: De invloed van het slaapapneusyndroom op het cognitieve functioneren bij CVA-patiënten: Een verkennend onderzoek. Tijdschrift voor Neuropsychologie 2008;6:131–137.
17 Bravata DM, Concato J, Fried T, Ranjbar N, Sadarangani T, McClain F, Struve F, Zygmunt L, Knight HJ, Lo A, Richerson JB, Gorman M, Williams LS, Brass LM, Agostini J, Mohsenin V, Roux F, Yaggi K: Continuous positive airway pressure: evaluation of a novel therapy for patients with acute ischemic stroke. Sleep 2011;34:1271–1277.
18 Parra O, Sanchez-Armengol A, Bonnin M: Early treatment of obstructive sleep apnoea and stroke outcome: a randomised controlled trial. Eur Respir J 2011;37:1128–1136.
19 Ryan CM, Bayley M, Green R, Murray BJ, Bradley TD: Influence of continuous positive airway pressure on outcomes of rehabilitation in stroke patients with obstructive sleep apnea. Stroke 2011;42:1062–1067.
20 Sandberg O, Franklin, KA, Bucht G, Eriksson S, Gustafson Y. Nasal continuous positive airway pressure in stroke patients with sleep apnoea: a randomized treatment study. Eur Respir J 2001;18:630-634.
21 Hsu C, Vennelle M, Li H, Engleman HM, Dennis MS, Douglas NJ: Sleep-disordered breathing after stroke: a randomised controlled trial of continuous positive airway pressure. J Neurol Neurosurg Psychiatry 2006;77:1143–1149.
22 Brown DL, Chervin RD, Kalbfleisch JD, Zupancic MJ, Migda EM, Svatikova A, ConCannon M, Martin C, Weatherwax KJ, Morgenstern LB: Sleep apnea treatment after stroke (SASTS) trial: is It feasible? J Stroke Cerebrovas Dis: . in press, Available online 23 July 2011.
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26 Lezak MD, Howieson DB, Digler ED, Tranel D: Neuropsychological Assessment. 5th edition. Cary, USA: Oxford University Press; 2012.
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28 Goldstein LB, Bertels C, Davis JN: Interrater reliability of the NIH stroke scale. Arch Neurol 1989;46:660–662.
29 Post MW, van de Port IG, Kap B, Berdenis van Berlekom SH: Development and validation of the Utrecht scale for evaluation of clinical rehabilitation (USER). Clin Rehabil 2009;23:909–917.
30 Mahoney FI, Barthel D: Functional evaluation: the Barthel index. Md State Med J 1965;14:61–65.
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Groet E, Kylstra WA, Schmand BA
Sleep 2015; 38:1431-1437
OBSTRUCTIVE SLEEP APNEA IS RELATED TO IMPAIRED
COGNITIVE AND FUNCTIONAL STATUS AFTER STROKE
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ABSTRACTStudy objectives Obstructive sleep apnea (OSA) is a common sleep disorder in stroke
patients and is associated with prolonged hospitalization, decreased functional outcome,
and recurrent stroke. Research on the effect of OSA on cognitive functioning following
stroke is scarce. The primary objective of this study was to compare stroke patients with and
without OSA on cognitive and functional status upon admission to inpatient rehabilitation.
Design Case-control study.
Settings and patients 147 stroke patients admitted to a neurorehabilitation unit.
Measurements All patients underwent sleep examination for diagnosis of OSA. We
assessed cognitive status by neuropsychological examination and functional status by two
neurological scales and a measure of functional independence.
Results We included 80 stroke patients with OSA and 67 stroke patients without OSA. OSA
patients were older and had a higher body mass index than patients without OSA. OSA
patients performed worse on tests of attention, executive functioning, visuoperception,
psychomotor ability, and intelligence than those without OSA. No differences were found
for vigilance, memory, and language. OSA patients had a worse neurological status,
lower functional independence scores, and a longer period of hospitalization in the
neurorehabilitation unit than the patients without OSA. OSA status was not associated
with stroke type or classification.
Conclusions OSA is associated with a lower cognitive and functional status in patients admitted
for stroke rehabilitation. This underlines the importance of OSA as a probable prognostic
factor, and calls for well-designed randomized controlled trials to study its treatability.
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INTRODUCTIONStroke is one of the leading causes of death and serious long-term disability worldwide,
and is a source of increased health care costs.1 In recent years efforts have been made to
improve stroke prevention by early recognition and treatment of well-known modifiable
risk factors such as hypertension, diabetes mellitus, and obesity.1 The identification of new,
potentially reversible risk factors has also received increased attention. In this context,
obstructive sleep apnea (OSA) has been suggested.
OSA is an independent risk factor for stroke and it is very common in the stroke
population, with reported prevalence rates between 30% and 70%.2,3 OSA can be
effectively treated with continuous positive airway pressure, but it is often left undiagnosed.
When left untreated, OSA is thought to contribute to decreased recovery from stroke.4
In line with this hypothesis, a number of studies have shown that OSA is associated with
poor functional recovery, prolonged hospitalization and higher mortality rates.4–7
In the general population, OSA has been found to negatively affect cognitive functioning.8,9
Studies on the effect of OSA on cognitive functioning in stroke patients are scarce, and
the results inconsistent.4,5,10 One study found an association between OSA and a cognitive
screening instrument (Mini-Mental State Examination [MMSE]),11 whereas two studies reported
no relationship. However, the MMSE is not designed to detect subtle cognitive changes, and
use of more sensitive neuropsychological tests is recommended.5 We conducted a pilot study
(n = 16) examining the effect of OSA on neuropsychological functioning in stroke patients and
found that OSA was associated with lower performance in the domains of attention, verbal
memory, and visuoperception.12 A larger study in patients with traumatic brain injury showed
similar results, with OSA being associated with more impairment of sustained attention
and memory.13 In the current study we investigated the association between OSA and both
cognitive and functional status in a large sample of stroke patients. We hypothesized that OSA
is associated with a lower cognitive and functional status.
METHODSParticipants
Stroke patients admitted to the neurorehabilitation unit of Heliomare Rehabilitation Center
between September 2011 and August 2014 were invited to participate if they met the
following inclusion criteria: (1) stroke confirmed by a neurologist, (2) age between 18 and
85 y, (3) admission between 1 and 16 w after stroke, (4) able to participate in the sleep
study and neuropsychological assessment, and (5) sufficiently fluent in Dutch. Exclusion
criteria were: (1) severe, unstable medical conditions, respiratory failure, or history of
severe congestive heart failure, (2) traumatic brain injury, (3) severe aphasia, confusion,
or severe psychiatric comorbidity, or (4) central sleep apnea or previously diagnosed OSA.
Inclusion of patients in the control group was ended at n = 100.
This study is part of the prospective Treatment of OSA and Rehabilitation Outcome in
Stroke (TOROS) study (Dutch Trial Register NTR3412).14 The institutional review board of
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the Academic Medical Centre in Amsterdam approved the study and all subjects provided
written consent before participation.
Sleep Studies
Within the first weeks of hospitalization, patients underwent a sleep examination
using standardized pulse oximetry (WristOx; Nonin Medical, Plymouth, MN, USA) and
ambulatory overnight cardiorespiratory polygraphy (Embletta; Embla, Ottawa, Canada).
The oxygen desaturation index (ODI) was calculated from pulse oximetry using automated
analyses. The ODI was defined as the mean number of oxygen desaturations of ≥ 3% per
hour. Patients with an ODI of five or higher were further tested for OSA by polygraphy.
Polygraphy included recordings of airflow by oronasal thermistor, oxygen saturation
and heart rate by pulse oximetry, and respiratory effort by abdominal wall and thoracic
wall motion recording. The data were recorded with a multichannel digital polygraphic
system. Trained staff manually scored the polygraph recordings for apnea and hypopnea
events. Apnea was defined as a reduction of airflow of ≥ 90% for at least 10 sec and
hypopnea was defined as a reduction of airflow of ≥ 50% for at least 10 sec followed by
an oxygen desaturation of ≥ 3%. Apneas with thoracic motion, without thoracic motion,
or with initial lack of motion followed by respiratory effort, were classified as obstructive,
central, or mixed, respectively. The apnea-hypopnea index (AHI) was defined as the mean
number of apneas and hypopneas per hour in bed. In patients with an AHI of 15 or higher
on polygraphy, the diagnosis of sleep apnea (moderate to severe) was made. OSA was
diagnosed when at least 50% of the respiratory events were of the obstructive type,
whereas central sleep apnea was diagnosed when more than 50% of respiratory events
were of the central type. Patients with a normal ODI (< 5) or AHI < 15 were enrolled in the
control group of this study. In the current analyses, however, we excluded patients with
mild OSA (AHI between 5-15) to reduce possible misclassifications. Patients with ODI (< 5)
or AHI < 5 are further referred to as non-OSA patients.
Outcome Measurements
The primary outcome measures of this study were cognition and functional status. For
cognition the following nine domains were assessed: vigilance, attention, memory,
working memory, executive functioning, language, visuoperception, psychomotor ability,
and intelligence. A trained psychology technician administered the battery of standardized
neuropsychological tests within 4 weeks of admission. The assessment battery consisted of
the following tests: (1) Psychomotor Vigilance Task to test of vigilance and reaction time, (2)
D-KEFS Trail Making Test for visual attention and mental flexibility, (3) d2 Test of Attention,
a measure of sustained and selective attention; (4) Rey Auditory Verbal Learning Test for
verbal memory; (5) WAIS-III Letter-Number Sequencing to test working memory, (6) Tower
of London for the assessment of executive functioning, (7) Category Fluency to assess
verbal fluency, (8) Bells Test, a test for visuoperception and visual neglect, (9) Finger Tapping
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Test to assess psychomotor ability and motor speed, and (10) WAIS-III Matrix Reasoning, a
measure for nonverbal abstract reasoning. For a number of cognitive domains, nonverbal
alternative tests were administered to for patients with aphasia: Color Trails Test for visual
attention and mental flexibility, Location Learning Test to test visual-spatial memory, and
WMS-IV Symbol Span for visual working memory. Categorization of tests into cognitive
domains was based on conventional classification as described in the standard textbook
of neuropsychological assessment.15 The classification of neuropsychological tests per
cognitive domain and a short description of the tests is given in the Appendix.
The obtained test scores were transformed into demographically corrected z-scores using
reference data. All tests were corrected for age and the Color Trails Test, Location Learning
Test, and Rey Auditory Verbal Learning Test were corrected for both age and education. For
three tests (Psychomotor Vigilance Task, Bells Test, and Finger Tapping Test) no reference data
were available and age-adjusted z-scores were calculated using a linear regression based
approach including age as independent variable. We calculated the regression weights for
the non-OSA group and subsequently applied them to all patients. If patients were unable
to complete a task, the overall lowest obtained z-score for that test was given. In case of
multiple tests within one domain, the average z-score for the domain was calculated. All
nine domain scores were averaged into one overall cognition score (Cronbach α = 0.82).
Functional status was assessed by measures of neurological status and functional
independence. At hospital admission, a rehabilitation physician administered two scales of
neurological status, the Canadian Neurological Scale (CNS)16 and the National Institutes
of Health Stroke Scale (NIHSS).17 The obtained scores were transformed into z-scores and
averaged into one score for neurological status. Within the first week of admission, a trained
nurse practitioner scored the level of functional independence on the physical functioning
subscales (mobility and self-care) of the Utrecht Scale for Evaluation of Rehabilitation
(USER).18 The obtained scale scores were transformed into z-scores and averaged into one
score for functional independence. The score for overall functional status was calculated by
averaging the neurological status and functional independence score (Cronbach α = 0.84).
Secondary outcome measures were sleepiness (Stanford Sleepiness Scale),19 fatigue
(Checklist Individual Strength),20 anxiety and depression (Hospital Anxiety and Depression
Scale),21 and subjective sleep quality (Sleep Quality Scale).22 A trained psychological
technician administered these measures at the time of the neuropsychological assessment.
Demographic, clinical, and neurological data (age, sex, education level, body mass
index [BMI], stroke type, stroke localization, stroke classification, time after onset of
stroke, single versus recurrent stroke) were obtained from the medical files. The level of
education was classified into seven categories according to the International Standard
Classification of Education.23 Stroke classification at the time of hospital presentation was
scored according to the Oxfordshire Community Stroke Project criteria.24 A full description
of the assessment procedures has been published previously.13
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Statistical Analysis
Data analyses were performed using SPSS (version 19.0, IBM, Armonk, NY). We used
descriptive statistics to characterize the sample in terms of demographics, clinical, sleep,
and stroke variables. To compare values between groups we used parametric (Student t
test) and nonparametric tests (chi-square test or Mann-Whitney U test) as appropriate.
To evaluate differences between groups on cognitive and functional status, we
performed a multivariate analysis of covariance (MANCOVA) using the mean z-scores
of cognitive and functional status as dependent variables and recurrent stroke and
level of education as covariates. Next, we performed univariate analyses of covariance
(ANCOVAs) for the individual cognitive and functional domains with Benjamini-Hochberg
correction for multiple comparisons.25 Effect sizes were calculated with Cohen d statistic.
An effect size of 0.2 was considered small, 0.5 moderate and 0.8 large. We replaced
missing values by the group mean of each domain (1 data point was missing for functional
independence, 5 for psychomotor ability, 3 for intelligence, 1 for visuoperception). In
the cognitive domain of vigilance 19 data points (13%) were missing. We imputed the
estimated values using a linear regression equation based on the overall cognition score
without the vigilance domain. We performed the main analyses with and without the
imputed values and this did not change the results. For secondary outcome measures
Student t tests were performed. In case of missing values we imputed the group mean of
each measure (7 data points were missing for sleepiness and sleep quality, 5 for fatigue,
anxiety and depression). For all statistical tests, significance was set at p < 0.05.
RESULTSCharacteristics of the Participants
The flow of participants through the study is illustrated in Figure 1. In total, we screened
654 patients and found 449 patients eligible. One hundred twenty-one patients were
discharged before they could be included and 122 patients declined participation. We
removed one patient from the sample analyses due to changes in AHI after prescription
of sleep medication, and six patients were removed because of technical problems with
the pulse oximetry data (more detailed information is available upon request). Our initial
sample comprised 199 patients, of whom 80 (40%) received a diagnosis of moderate to
severe OSA (AHI ≥ 15). Nineteen patients were excluded from the control group because
they were referred after the target sample size of 100 controls was reached (May 2014).
Additionally, we excluded 33 patients with mild OSA (AHI 5-15) from the control group.
Thus, our final sample comprised 147 patients. Eighty-three patients (57%) were
male and the mean age was 57.8 y (± 10.1; range, 24-76). Fifty-one patients (35%) were
overweight (BMI 25-30) and 22 patients (15 %) were obese (BMI ≥ 30). The majority of
patients had a first-ever stroke (83%). Seventy-one percent of the patients had an ischemic
stroke, 22% had a hemorrhagic stroke, and 7% a subarachnoid hemorrhage. Cortical
strokes were predominant (88%) and the majority of strokes were classified as partial
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Figure 1. Patient flow chart.
anterior circulation stroke (60%). On average, patients were admitted to the rehabilitation
unit 17.2 days (± 16.0; range 3-98) after the stroke and were discharged after 69.0 days of
inpatient rehabilitation (± 32.6; range 13-169). There was no significant difference between
stroke types (ischemia, hemorrhage, or subarachnoid hemorrhage) on the primary outcome
measures cognitive status (F(2, 144) = 0.79, p = 0.46) and functional status (F(2, 144) = 0.99,
p = 0.37), which justifies combining these stroke subtypes in our further analyses.
Background characteristics of the OSA and non-OSA groups are presented in Table 1.
The mean ODI and AHI in the OSA group were 29.4 and 33.0, respectively, compared
to 5.4 and 2.8, respectively, in the non-OSA group (AHI was only available for 18
non-OSA patients because only a subgroup underwent polygraphy). The OSA group was
significantly older and had a higher BMI than the non-OSA group. The groups did not
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differ on sex, education level, or any of the stroke characteristics. There was no difference
in time between stroke onset and admission to the rehabilitation center. The OSA patients,
however, spent a significantly longer period (average of 11 days) in inpatient rehabilitation
than the patients without OSA. This difference in time spent in inpatient rehabilitation
was not related to the age of the patients (F(2, 144) = 0.28, p = 0.60).
We included recurrent stroke and level of education as covariates in our main analyses
for cognitive and functional status, because there were group differences on these
measures, albeit not significant. We did not include age and BMI, because the cognitive
measures were already adjusted for age and we did not find significant correlations between
Table 1. Patient characteristics of the groups
Characteristics OSA (n=80) Non-OSA (n=67) P-value
Age (y) 60.4 (8.5) 54.9 (11.0) 0.001a
Sex (males) 50 (62.5) 33 (49.0) 0.10b
Education level, median (range) 4 (1-6) 4 (1-6) 0.16c
BMI 27.1 (5.0) 23.9 (3.6) <0.001a
25.0-29.9 (overweight) 35 (43.7) 16 (23.8) 0.01b
≥30.0 (obese) 17 (21.3) 5 (7.5) 0.02b
Stroke type 0.25b
Ischemia 60 (75.0) 45 (67.2)
Hemorrhage 17 (21.2) 15 (22.3)
Subarachnoid hemorrhage 3 (3.8) 7 (10.4)
Stroke location 0.42b
Cortex 72 (90) 57 (85.1)
Cerebellum 3 (3.8) 6 (8.9)
Brain stem 5 (6.2) 4 (5.9)
Stroke classification 0.19b
PACS 49 (61.2) 39 (58.2)
LACS 12 (15.0) 8 (11.9)
TACS 7 (8.8) 2 (2.9)
POCS 12 (15.0) 18 (26.8)
Recurrent stroke 16 (20.0) 9 (13.4) 0.29b
Days between onset and admission 18.0 (17.8) 18.4 (13.7) 0.88a
Days admitted to rehabilitation unit 75.0 (33.5) 63.8 (30.8) 0.04a
Oxygen desaturation index 29.4 (14.5) 5.9 (4.2) >0.001a
Apnea-hypopnea index 33.0 (15.3) 2.8 (1.1)d >0.001a
Values are presented as mean (standard deviation) or n (%). a Student t test. b Chi-square test. c Mann-Whitney U test. d Based on 18 non-OSA patients who underwent polygraphy. BMI, body mass index; OSA, obstructive sleep apnea; PACS, partial anterior circulation stroke; LACS, lacunar stroke; TACS, total anterior circulation stroke; POCS, posterior circulation stroke.
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cognitive and functional status, and age or BMI (functional: age r = 0.08, p = 0.32; BMI
r = -0.02, p = 0.79; cognitive: age r = -0.01, p = 0.90; BMI r = 0.05, p = 0.55).
Primary Outcomes
We found a significant difference between the OSA and the non-OSA group on both
cognitive and functional status (MANCOVA; Pillai’s trace = 0.08, F(2, 142), p = 0.001).
OSA patients had a lower overall cognitive status than patients without OSA (Table 2).
ANCOVAs showed that OSA patients performed significantly worse in the domains of
attention, executive functioning, visuoperception, psychomotor ability, and intelligence.
The performance in the domains of vigilance, memory, working memory, and language
did not differ between groups. For all domains with significant group differences, Cohen
d was small to moderate (0.35-0.48).
We found that OSA patients had a significantly lower functional status than non-OSA
patients (Table 3). ANCOVAs showed that OSA patients had a significantly worse
neurological status and significantly lower functional independence, both on self-care
and mobility. The effect sizes for the functional domains ranged from small to large (0.24-
0.92), with the largest effect sizes for measures of functional independence.
Secondary Outcomes
We found no significant differences between the OSA patients and non-OSA patients
on sleepiness, fatigue, or sleep quality (Table 4). Moreover, the OSA group did not differ
significantly from the non-OSA group on reported symptoms of anxiety or depression.
Table 2. Cognitive outcomes
DomainOSA
(n=80)Non-OSA
(n=67)P-value
(one-tailed)Effect size(Cohen d)
Cognitive status -0.98 (0.65) -0.68 (0.64) 0.01 0.47
Vigilance -0.23 (1.00) 0.07 (0.93) 0.08 0.31
Attention -1.39 (1.00) -0.87 (1.17) <0.01 0.48
Memory -0.80 (1.11) -0.83 (1.31) 0.39 0.03
Working memory -0.84 (1.28) -0.49 (1.20) 0.10 0.28
Executive functioning -1.29 (1.10) -0.80 (1.28) 0.02 0.42
Language -1.26 (1.01) -1.20 (0.80) 0.41 0.07
Visuoperception -0.57 (1.23) -0.19 (0.89) 0.03 0.35
Psychomotor ability -0.29 (0.62) 0.03 (0.58) <0.01 0.43
Intelligence -0.90 (0.92) -0.45 (1.14) 0.01 0.44
Values are presented as mean z-score ± standard deviation per cognitive domain. Scores are based on demographically corrected z-scores. OSA, obstructive sleep apnea. Significant after Benjamini-Hochberg correction.
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Table 3. Functional outcomes
DomainOSA
(n=80)Non-OSA
(n=67)P-value
(one-tailed)Effect size(Cohen d)
Functional status1 -0.22 (0.91) 0.25 (0.82) <0.001 0.54
Neurological status1 -0.11 (1.01) 0.13 (0.97) 0.04 0.24
NIHSS2a 6.31 (4.14) 5.35 (3.98) 0.08 0.23
CNS2 8.46 (2.64) 9.28 (2.21) 0.03 0.34
Functional independence1 -0.31 (0.93) 0.36 (0.86) <0.001 0.75
USER mobility2 14.86 (10.35) 21.88 (10.53) <0.001 0.68
USER self-care2 20.08 (10.49) 28.92 (8.47) <0.001 0.92
Values are presented as mean z-score¹ or scale score² ± SD. a Higher score is lower performance. CNS, Canadian Neurological Scale; NIHSS, National Institutes of Health Stroke Scale; OSA, obstructive sleep apnea; USER, Utrecht Scale for Evaluation of Rehabilitation. Significant after Benjamini-Hochberg correction.
Table 4. Secondary outcomes
Measure OSA (n=80) Non-OSA (n=67) P-value (one-tailed)
Sleepiness (SSS) 2.1 (1.0) 2.1 (1.1) 0.46
Fatigue (CIS-20r) 73.9 (21.0) 72.6 (22.0) 0.36
Sleep quality (SQS) 9.5 (3.6) 9.4 (3.5) 0.43
Anxiety (HADS-A) 6.0 (4.3) 5.7 (4.2) 0.32
Depression (HADS-B) 5.7 (3.5) 5.5 (4.3) 0.42
Values are presented as mean scale score ± standard deviation. CIS, Checklist Individual Strength; HADS, Hospital Anxiety and Depression Scale; OSA, obstructive sleep apnea; SQS, Sleep Quality Scale; SSS, Stanford Sleepiness Scale.
DISCUSSIONWe found that OSA patients had significantly more cognitive and functional impairments
than stroke patients without OSA. They were more impaired in attention, executive
functioning, visuoperception, psychomotor ability and intelligence, and had poorer
neurological status and a lower level of functional independence upon admission. We
also found that OSA patients on average had an 11-day longer hospitalization in the
neurorehabilitation than patients without OSA. OSA patients did not differ from the
stroke patients without OSA on reported levels of sleepiness, fatigue, and sleep quality or
on reported symptoms of depression and anxiety.
We observed a relatively high prevalence of OSA in our sample (40% of the initial
sample of 199), which is consistent with previous findings. Johnson and Johnson
performed a meta-analysis on the prevalence of sleep apnea in stroke patients.3 They
included six studies conducted on stroke rehabilitation units and found prevalence rates
between 22–61% (for AHI > 10 and AHI > 20). However, it should be noted that the
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prevalence rate in our study may not be representative for our neurorehabilitation unit
as a whole, as only a selective group of patients participated in the study. The observed
lack of association between stroke characteristics and OSA is also consistent with earlier
studies.5,10 In our study, OSA was associated significantly with older age and higher BMI.
This is not surprising as both are well-known risk factors for OSA.26
One of the main findings of our study is that, at the time of admission to the
neurorehabilitation unit, OSA is associated with a poorer neurological status and higher
functional dependence. Our findings are supported by a number of earlier studies that found
that OSA was associated with worse functional outcome at discharge, and at 3, 6, and 12 mo
after stroke onset.4,5,7 The findings on functional status upon admission to stroke rehabilitation
are less consistent.5,6,10 Kaneko et al. 5 and Sandberg et al.10 found higher functional dependence
in OSA patients upon admission, whereas Cherkassky et al.6 did not find a difference in
functional dependence. In contrast to our findings, Kaneko et al. 5 found that the neurological
status measured with the CNS upon admission to the stroke rehabilitation unit did not differ
between OSA and non-OSA patients. In their study OSA patients did score slightly lower on
the CNS than the OSA patients, although the difference was not significant. This may be
explained by the small size of the non-OSA group in their study (n = 17). Furthermore, in
accordance with Kaneko et al., we also found that OSA patients spent a longer period of time
hospitalized in the neurorehabilitation unit than patients without OSA.
To our knowledge, this is the first study that compared cognitive functioning of stroke
patients with and without OSA using a comprehensive neuropsychological assessment.
We found that OSA patients are significantly more impaired in a number of cognitive
domains. Our results are, to a large extent, comparable to cognitive effects found in
the otherwise healthy OSA population, and confirm our earlier pilot study results.12,8 In
accordance with our findings, substantial effects on attention and to a lesser degree on
executive functioning, visuoperception and motor function are often reported in the
general OSA population, whereas language is often spared. However, in contrast to
our results, vigilance and memory are also often reported to be affected by OSA in the
healthy population.27,28 In our study we did observe a trend for worse vigilance in the OSA
group, but we did not find any effects of OSA on memory. In our pilot study,12 we did
observe a significant correlation between OSA and verbal memory. This discrepancy in
findings is most probably related to methodological differences between the two studies.
In the current study we compared two groups (OSA and non-OSA), whereas in the pilot
study cognitive measures were correlated to measures of OSA (AHI and ODI). Last, we
found that the intelligence domain was more impaired in OSA patients, whereas the
more general finding is that intellectual functioning is spared in otherwise healthy OSA
patients.8,9 This discrepancy may be explained by the fact that we only used one cognitive
test of performance intelligence to measure the intelligence domain (WAIS-III Matrix
Reasoning) instead of a complete intelligence quotation (IQ) assessment, as there are
some indications in the general population that performance IQ is lower in OSA patients,
whereas verbal IQ and total IQ are spared.9
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Contrary to general expectations, we did not find higher levels of fatigue or sleepiness
in OSA patients. The first could be explained by the fact that fatigue is not only a common
sign of OSA, but also the most reported complaint after stroke.29 This explanation is
supported by the high levels of fatigue reported by both groups. The lack of difference
between patients with and without OSA on reported sleepiness may be caused by the
difficulty to differentiate between the concepts of sleepiness and fatigue or alternatively by
a lack of awareness or underestimation of their sleepiness as result of the stroke. Although
these findings or not in line with the generally held view, they are less surprising in the light
of an earlier study we performed on predictive value of self-reported complaints for OSA in
stroke patients.30 In this study we found that self-reported symptoms such as fatigue and
sleepiness could not adequately predict a high likelihood of OSA in the stroke population.
Several limitations of our study should be noted. First, our findings are only correlational
and do not imply causality. It could therefore be argued that the lower cognitive and
functional status of OSA patients are not attributable to OSA, but rather to stroke severity.
However, we did not observe any significant differences between the OSA and non-OSA
groups in stroke type, location, or classification (a measure of stroke severity) at time of
admission. Moreover, we included recurrent stroke as covariate in our main analyses.
Thus, this suggests that the excess of cognitive and functional impairment observed in the
OSA patients is not just a marker of more severe stroke. Nor can the lower performance of
OSA patients be attributed to age, educational level, or BMI, as we corrected the cognitive
data for age, included education as a covariate in our analyses, and demonstrated that
both age and BMI were not correlated to functional status.
Second, we performed a two-tiered screening procedure for the diagnosis of OSA. In
our study patients were first examined by standardized pulse oximetry, and patients only
underwent a full polygraphy when the ODI was elevated (ODI ≥ 5). We chose to use this two-
tiered method, because earlier studies showed that an elevated ODI on pulse oximetry is highly
sensitive for the diagnosis of mild and moderate to severe sleep apnea (AHI ≥ 5: 86% sensitivity;
AHI ≥ 15: 96–100% sensitivity, respectively),31,32 and pulse oximetry is less burdensome and
costly than polygraphy. This method, however, may have led to misclassification of subjects.
Additionally, the use of polygraphy without electroencephalography may have led to some
degree of misclassification, because total recording time was used to calculate AHI, instead
of total sleep time, which may have resulted in an artificially lower AHI. Earlier studies have
shown that the AHI based on total sleep time is on average 2 to 8 points lower in patients
with moderate and severe OSA respectively.33
Third, our patient sample may not have been representative of stroke patients in general.
We only included patients admitted to the neurorehabilitation unit. These patients had a level
of disability that precluded them from being discharged immediately after hospitalization,
but they were not so severely disabled that they did not have rehabilitation potentials. In
addition, more than half of the eligible patients did not participate because of early discharge
from the rehabilitation unit or declining to undergo sleep examination or neuropsychological
assessment. Therefore, we cannot rule out the possibility of a selection bias.
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Our study also had a number of important strengths, including the large sample size, the
use of both cognitive and functional measures, and the administration of a comprehensive
neuropsychological assessment battery for the evaluation of cognitive functioning.
CONCLUSIONSIn summary, our results indicate that OSA is associated significantly with a lower cognitive
and functional status in stroke patients admitted for stroke rehabilitation. Our findings
underline the importance of OSA as a probable prognostic factor, and call for well-
designed randomized clinical trials to investigate whether treatment of OSA could improve
cognitive and functional outcome of stroke patients.
ACKNOWLEDGEMENTSThe authors thank Brita Daniels, Irene Kos, and Mario van Lieshout for their support with
data collection.
DISCLOSURES This was not an industry supported study. The authors have indicated no financial conflicts
of interest.
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18 Post MW, van de Port IG, Kap B, Berdenis van Berlekom SH: Development and validation of the Utrecht scale for evaluation of clinical rehabilitation (USER). Clin Rehabil 2009;23:909–017.
19 Herscovitch J, Broughton R: Sensitivity of the Stanford sleepiness scale to the effects of cumulative partial sleep deprivation and recovery oversleeping. Sleep 1981;4:83–91.
20 Vercoulen JH, Swanink C, Fennis JF, Galama J, van der Meer JW, Bleijenberg G: Dimensional assessment of chronic fatigue syndrome. J Psychosom Res 1994;38:383–392.
21 Zigmond AS, Snaith R: The hospital anxiety and depression scale. Acta Psychiatr Scand 1983;67:361–370.
22 Visser P, Hofman WF, Kumar A, et al. Sleep and mood: measuring the sleep quality. In: Priest RG, Pletscher A, Ward J, eds. Sleep research. Baltimore, MD: University Park Press; 1979:135–145.
23 UNESCO (2006 [1997]). International Standard Classification of Education: ISCED 1997 (re-edition),Montreal: UNESCO Institute for Statistics.
24 Bamford J, Sandercock P, Dennis M, Burn J, Warlow C. Classification and natural history of clinically identifiable subtypes of cerebral infarction. Lancet 1991;337:1521–1526.
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25 Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc Ser B 1995;57:289–300.
26 Young T, Skatrud J, Peppard P. Risk factors for obstructive sleep apnea in adults. JAMA 2004; 291:2013–2016.
27 Beebe D, Groesz L, Wells C, Nichols A, McGee K. The neuropsychological effects of obstructive sleep apnea: a meta-analysis of norm-referenced and case-controlled data. Sleep 2003;26:298–307.
28 Aloia MS, Arnedt JT, Davis JD, Riggs RL, Byrd D. Neuropsychological sequelae of obstructive sleep apnea-hypopnea syndrome: a critical review. J Int Neuropsychol Soc 2004;10:772–785.
29 Lerdal A, Bakken LN, Kouwenhoven SE, Pedersen G, Kirkevold M, Finset A, Kim . Poststroke fatigue—a review. J Pain Symptom Manage 2009; 38:928-949.
30 Aaronson JA, Nachtegaal J, van Bezeij T, Groet E, Hofman WH, van den Aardweg JG, et al. Can a prediction model combining self-reported symptoms, sociodemographic and clinical features serve as a reliable first screening method for sleep apnea syndrome in patients with stroke? Arch Phys Med Rehabil 2014; 95:747–752.
31 Aaronson JA, van Bezeij T, van den Aardweg JG, van Bennekom CAM, Hofman WF. Diagnostic accuracy of nocturnal oximetry for detection of sleep apnea syndrome in stroke rehabilitation. Stroke 2012;43:2491–2493.
32 Vázquez JC, Tsai WH, Flemons WW, et al. Automated analysis of digital oximetry in the diagnosis of obstructive sleep apnoea. Thorax 2000;55:302–307.
33 Dingli K, Coleman EL, Vennelle M, et al. Evaluation of a portable device for diagnosing the sleep apnoea/hypopnoea syndrome. Eur Resp J 2003;21:253–259.
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Aaronson JA, Hofman WF, van Bennekom CAM, van Bezeij T, van den Aardweg JG,
Groet E, Kylstra WA, Schmand BA
Submitted
EFFECTS OF CONTINUOUS POSITIVE AIRWAY PRESSURE
ON COGNITIVE AND FUNCTIONAL OUTCOME OF STROKE PATIENTS
WITH OBSTRUCTIVE SLEEP APNEA: A RANDOMIZED
CONTROLLED TRIAL
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ABSTRACTStudy objectives Obstructive sleep apnea (OSA) in stroke patients is associated with
worse functional and cognitive status during inpatient rehabilitation. We hypothesized
that a four-week period of continuous positive airway pressure (CPAP) treatment would
improve cognitive and functional outcomes.
Methods We performed a randomized controlled trial in stroke patients admitted to
a neurorehabilitation unit. Patients were assigned to rehabilitation treatment as usual
(control group) or to CPAP treatment (CPAP group). Primary outcomes were cognitive
status measured by neuropsychological examination, and functional status measured
by two neurological scales and a measure of activities of daily living (ADL). Secondary
measures included sleepiness, sleep quality, fatigue and mood. Tests were performed at
baseline and after the four-week intervention period.
Results We randomly assigned 20 patients to the CPAP group and 16 patients to the
control group. The average CPAP compliance was 2.5 hours per night. Patients in the CPAP
group showed significantly greater improvement in the cognitive domains of attention and
executive functioning than the control group. CPAP compliance was associated with greater
improvement in cognitive functioning. CPAP did not result in measurable improvement on
measures of neurological status or ADL, or on any of the secondary measures.
Conclusions CPAP treatment improves cognitive functioning of stroke patients with OSA.
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INTRODUCTIONThe estimated prevalence of obstructive sleep apnea (OSA) in stroke patients is as high as
38-70%.1 Despite its high prevalence in the stroke population, OSA is often left undiagnosed
and untreated.2,3 Main causes of under-diagnosis are lack of awareness of health care
professionals, lack of complaints by patients, and difficult access to sleep laboratory-based
testing.4 Untreated OSA is associated with worse cognitive and functional status, higher
risk of recurrent stroke and mortality.5-8 Treatment of choice for OSA is continuous positive
airway pressure treatment (CPAP). In otherwise healthy OSA patients, CPAP effectively treats
OSA by improving the breathing pattern during sleep, leading to improvement of daytime
sleepiness, health status and depressive symptoms.9 Consequently, it seems plausible that
CPAP treatment could improve stroke outcome. The randomized controlled trials (RCTs)
that have been published, however, show mixed results.10-15
In a recent review of CPAP treatment in stroke patients, Tomfohr and colleagues
conclude that none of the studies have found improvements in activities of daily living (ADL)
or cognitive functioning, and that studies on the beneficial effect of CPAP on neurological
recovery, daytime sleepiness and depressive symptoms are still inconclusive. 16 Three studies
found improvement after CPAP on neurological status (Canadian Neurological Scale or
National Institute of Health Stroke scale),10-12 while two others did not.13,14 The two latter
studies suffered from low compliance and insufficient power, which might have influenced
the findings. Three studies investigated the effect on cognitive functioning. However, two
of the studies used only a brief mental status examination to investigate cognition, and the
other study only assessed specific neuropsychological measures of vigilance and executive
functioning.12,14,15 Tomfohr et al. argue that the lack of findings on cognitive measures may
be explained by the differing levels of sensitivity of the tests. They suggest that more nuanced
neuropsychological tests may show specific cognitive domains that are affected by CPAP.16
The primary aim of our study was to evaluate the effectiveness of CPAP treatment
in stroke patients during inpatient rehabilitation using a comprehensive battery of
neuropsychological tests and neurological and ADL scales. We hypothesized that CPAP
treatment would improve cognitive and functional outcomes over a four-week period.
Furthermore, we hypothesized that CPAP would also positively affect sleepiness, sleep
quality, fatigue and mood during inpatient rehabilitation.
METHODSStudy design
We performed a randomized controlled trial (RCT) of CPAP treatment in stroke patients
with blind assessment of outcome measures. Patients were randomized to receive four
weeks of CPAP treatment or treatment as usual (control group). The minimization method
was used to balance the groups for age (< 50, 50-59, 60-69, ≥ 70 years), severity of OSA
(apnea/hypopnea index [AHI, see below]: 15-29, 30-59, ≥ 60), stroke subtype (ischemic
or hemorrhage) and severity of cognitive impairment (cognitive status; very severe = <
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-3, severe = -3 to -2, moderate = -2 to -1, mild = > -1 standard deviation below the
norm; see below). After the four-week intervention period, patients in the control group
were offered CPAP treatment. Assessments were performed at baseline (T0) and after the
four-week intervention period (T1). In addition to the RCT, we included assessments at
a two-month follow-up (T2). We also included a group of non-OSA patients to compare
their recovery on the outcome measures with that of the OSA groups. This study is part
of the prospective TOROS study (Dutch Trial Register NTR3412). The institutional review
board of the Academic Medical Centre in Amsterdam approved the study. A detailed
description of the trial design has been published elsewhere.17
Subjects
We recruited patients from the neurorehabilitation unit of Heliomare rehabilitation center.
Subjects were eligible if they had a stroke confirmed by a neurologist, were admitted in
Heliomare between 1 and 16 weeks post-stroke, were between 18 and 85 years of age, and
were able to participate in the sleep study and in a neuropsychological assessment in Dutch.
Patients were excluded if they had prior diagnosis of sleep apnea, diagnosis of central
sleep apnea, or severe OSA (AHI > 60 and oxygen desaturations below 70%), which could
endanger the patient’s health if treatment was not started immediately. Other exclusion
criteria were severe, unstable medical conditions, respiratory failure, history of severe
congestive heart failure, traumatic brain injury, severe aphasia, severe confusion or severe
psychiatric comorbidity. All subjects provided written informed consent before participation.
Sleep studies
The overnight recordings consisted of standardized pulse oximetry (WristOx®; Nonin Medical,
Plymouth, USA) and ambulatory overnight cardiorespiratory polygraphy (Embletta®; Embla,
Ottawa, Canada). The oxygen desaturation index (ODI) was calculated from pulse oximetry
data using automated analyses. The ODI was defined as the mean number of oxygen
desaturations of ≥ 3% per hour. Patients with an ODI of five or higher were further tested
for OSA by polygraphy.
Polygraphy included recordings of airflow by oronasal thermistor, oxygen saturation
and heart rate by pulse oximetry, and respiratory effort by abdominal wall and thoracic wall
motion recording. The data were recorded with a multichannel digital polygraphic system.
Trained staff manually scored the polygraph recordings for apnea and hypopnea events.
Apnea was defined as a reduction of airflow of ≥ 90% for at least 10 seconds and
hypopnea was defined as a reduction of airflow of ≥ 50% for at least 10 seconds followed
by an oxygen desaturation of ≥ 3%. Apneas with thoracic motion, without thoracic motion
or with initial lack of motion followed by respiratory effort, were classified as obstructive,
central or mixed, respectively. The apnea-hypopnea index (AHI) was defined as the mean
number of apneas and hypopneas per hour in bed. Patients with an AHI of 15 or higher
on polygraphy were diagnosed with sleep apnea (moderate to severe). OSA was diagnosed
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when at least 50% of the respiratory events were of the obstructive type, while central
sleep apnea was diagnosed when more than 50% of respiratory events were of the central
type. Patients with a normal ODI (< 5) or AHI below 15 were classified as non-OSA patients.
Intervention
CPAP treatment was set up and monitored by a specialized ‘Respicare’ team. This team
consists of two rehabilitation physicians, two nurse practitioners and four nurses working
on the neurorehabilitation unit specialized in sleep and breathing disorders. Before CPAP
treatment was initiated, personalized instructions were given and a written manual for
the CPAP device was provided. If possible, a partner or a close relative was also provided
with instructions on the use of the CPAP device. CPAP treatment was evaluated together
with the patient, and CPAP titration was performed using pulse oximetry. The pressure
was adjusted until the ODI was reduced to normal (ODI < 5). If titration by nocturnal
oximetry failed to adequately reduce the ODI, CPAP was titrated by polygraphy to reduce
the AHI to < 5 or to the highest pressure tolerated. The CPAP device was provided with a
memory card to evaluate the effectiveness of CPAP therapy over time and to monitor CPAP
compliance. In our study, patients were considered to be compliant if they used CPAP for
a minimum of one hour per night. Good compliance was, in accordance with general
consensus, defined as more than 4 hours CPAP usage for five or more nights per week.18
The Respicare team had contact with the patients regularly during the intervention period
to help troubleshoot problems and encourage compliance. Patients who were discharged
during the four-week intervention period were followed-up by telephone interview and
invited for an outpatient consultation.
Outcome measures
The primary outcome measures were cognitive and functional status. For cognitive status
the following nine domains were assessed: vigilance, attention, memory, working memory,
executive functioning, language, visuoperception, psychomotor ability and intelligence. A
trained psychological technician administered the battery of standardized neuropsychological
tests. Categorization of tests into cognitive domains was based on conventional classification
as described in a standard textbook of neuropsychological assessment.19 The classification of
neuropsychological tests per cognitive domain is given in the Appendix.
The obtained test scores were transformed into demographically corrected z-scores using
reference data. All tests were corrected for age (and education, where possible). If reference
data were not available, age-adjusted z-scores were calculated.7 If patients were unable
to complete a task, the overall lowest obtained z-score for that test was given. For most
domains, multiple tests were used; the average z-score for each domain was calculated.
Functional status was assessed by measures of neurological status and functional
independence. The rehabilitation physician administered two scales of neurological status,
the Canadian Neurological Scale (CNS)20 and the National Institutes of Health Stroke Scale
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(NIHSS).21 The obtained scores were transformed into z-scores using the mean and standard
deviation of the patient group at T0, and averaged into one score for neurological status.
A trained nurse-practitioner scored the level of functional independence on the physical
functioning subscales (mobility and self-care) of the Utrecht Scale for Evaluation of Rehabilitation
(USER).22 The obtained scale scores were transformed into z-scores and averaged into one
score for functional independence in the same way as the neurological status.
Secondary outcome measures were sleepiness (Stanford Sleepiness Scale)23, fatigue
(Checklist Individual Strength)24, anxiety and depression (Hospital Anxiety and Depression
Scale)25, and subjective sleep quality (Sleep Quality Scale)26.
Demographic, clinical and neurological data (age, sex, education level, body mass
index [BMI], stroke type, stroke localization, stroke classification, time since onset of stroke,
single versus recurrent stroke) were obtained from the medical files. The level of education
was classified into seven categories according to the UNESCO International Standard
Classification of Education.27 Stroke classification at the time of hospital presentation was
scored according to the Oxfordshire Community Stroke Project criteria.28 We categorized
lacunar strokes (LACS) as mild, partial anterior circulation stroke (PACS) and posterior
circulation stroke (POCS) as moderate, and total anterior circulation stroke (TACS) as
severe. A full description of the assessment procedures has been published previously.17
Statistical analysis
Data analyses were performed using SPSS (version 19.0). We compared baseline data of the
two groups using Student’s t-test, chi-square test or Mann-Whitney U test, as appropriate.
To compare differences between the control and intervention group over time, we
calculated difference scores for our outcome measures for T1-T0. The groups were
compared on primary outcomes with a multivariate analysis of covariance (MANCOVA)
with age, severity of OSA and stroke severity as covariates. We included these variables,
because we expected that they would influence the recovery rate of stroke patients, with
younger age and more severe OSA ameliorating recovery, and greater stroke severity
impeding recovery. Two separate MANCOVA’s for cognition and functional status were
performed, followed by a Benjamini-Hochberg correction for multiple comparisons.29 In
case of missing outcomes, the last observation carried forward method was used.
Secondary outcomes were compared with separate analyses of covariance (ANCOVA’s),
again including age and stroke severity as covariates. Effect sizes were calculated with partial
eta squared. An effect size of 0.01 was considered small, 0.06 moderate and 0.14 large.
All analyses were conducted on an intention-to-treat (ITT) basis. Additionally, we performed
per-protocol analyses for T1 on patients who completed the assessment and met our criteria
for minimal CPAP compliance (> 1 hour per night). For all statistical tests, significance was set
at a p-value ≤ 0.05. We hypothesized that the CPAP group would show greater improvement
at T1 than the control group and thus tested this hypothesis one-tailed.
In addition to the RCT analyses, we compared differences in recovery between the CPAP
and control group at T2 (T2-T1) applying the same statistical analyses as described for T1. The
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USER scores were only available for a small group (14 patients) and were therefore excluded
from the T2 analyses. As treatment was offered in both groups, we did not have a hypothesis
for the outcome at T2 and therefore used two-tailed p-values. The results of the T2 analyses
are presented in Tables I-III in Supplement I and are not further discussed in the paper.
Finally, we compared the recovery rate of the two groups of OSA patients (CPAP
and control) to a group of non-OSA stroke patients. For this comparison we conducted
two MANCOVA’s for the primary outcome measures, with age and stroke severity as
covariates. To adjust for multiple comparisons a Bonferroni correction was performed. We
hypothesized that the OSA control group would show less improvement than the CPAP
group and non-OSA group.
RESULTSSubjects
Between October 2011 and September 2014 we screened 654 patients, of whom 449
patients were eligible (Figure 1). Of these 449 patients, 206 agreed to participate in
the sleep study. We diagnosed 80 patients (39%) with OSA (AHI ≥ 15). Two patients
were excluded because immediate treatment was indicated and 42 patients declined to
participate in the RCT. The remaining 36 patients were randomly assigned to the CPAP
group (n = 20) or control group (n = 16). Three patients in the CPAP group and two in the
control group withdrew from the study before the four-week assessment. Five patients
were lost to follow-up. We also included 44 non-OSA patients as a comparison group.
Patients with OSA had a mean age of 59.1 years (± 8.6; range, 42-74) and average BMI
of 27.1 (± 5.8; range, 18-46). The mean AHI was 34.2 (± 14.8; range, 15-83). The majority of
patients had a first-ever ischemic stroke (56%), and were admitted to Heliomare, on average,
16.8 days (± 15.0; range, 3-71) after the stroke. The CPAP and control group showed similar
baseline characteristics (Table 1), and did not differ significantly at baseline on measures of
cognitive or functional status, or any of the secondary outcome measures (Table 2-4).
Sleep measures
The mean compliance in the CPAP group was 2.5 hours per night at T1 (± 2.8; range,
0-9). Nine patients used CPAP for less than one hour per night and were considered
non-compliant to CPAP treatment. The mean CPAP use by the 11 compliant patients
was 4.4 hours per night (±2.5; range, 1.3-9.0), with seven patients showing good CPAP
compliance (> 4 hours per day, ≥ 5 days a week). At two-month follow-up, eight patients
in the treatment group were still using CPAP, with an average compliance of 4.9 (± 2.9)
hours per night. Ten of the 13 patients still participating in the control group at T1 started
CPAP treatment after the four-week intervention period. The mean compliance during the
follow-up period for this group was 3.2 hours per night (± 2.5; range, 0.3-7.8).
After the four-week intervention period, 14 patients from the CPAP group and 12
from the control group agreed to polygraphy. Compared to the control group, the CPAP
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Table 1. Patient characteristics of the groups minimized for age, severity of OSA, stroke subtype and severity of cognitive impairment
Characteristics CPAP (n=20) Control (n=16) P-value
Age (years) 61.1 (8.2) 56.7 (8.8) 0.13a
Sex (males) 12 (60.0) 10 (62.5) 0.88b
Education level, median (range) 4 (1-5) 4 (2-6) 0.48c
BMI 28.1 (6.4) 25.8 (4.7) 0.24a
Stroke type 0.89b
Ischemia 14 (70.0) 10 (63.0)
Hemorrhage 5 (25.0) 5 (31.3)
Subarachnoid hemorrhage 1 (5.0) 1 (6.3)
Stroke severity 0.09c
Mild (LACS) 6 (30.0) 0 (0.0)
Moderate (PACS/POCS) 13 (65.0) 14 (87.5)
Severe (TACS) 1 (5.0) 2 (12.5)
Severity of cognitive impairment1 0.58c
Mild (≥ -1) 11 (55%) 7 (44%)
Moderate (< -1 ≥ -2) 7 (35%) 7 (44%)
Severe (< -2 ≥ -3) 2 (10%) 2 (13%)
Recurrent stroke 3 (15.0) 3 (18.8) 0.76b
Days between onset and admission 14.0 (18.0) 20.0 (11.8) 0.25a
Days between onset and NPA 35.2 (15.8) 36.9 (21.2) 0.77a
Days admitted to rehabilitation unit 71.1 (29.2) 74.2 (33.9) 0.77a
Apnea-hypopnea index 38.1 (17.3) 29.4 (9.2) 0.08a
Oxygen desaturation index 34.2 (16.4) 25.9 (9.9) 0.09a
Values are presented as mean (standard deviation) or n (%). PACS, partial anterior circulation stroke; LACS, lacunar stroke; TACS, total anterior circulation stroke; POCS, posterior circulation stroke; NPA, neuropsychological assessment. 1 defined as the number of standard deviations below the norm; a Student’s t-test; b Chi-square test; c Mann-Witney U test.
compliant patients (n = 10) showed a significant reduction in AHI (CPAP -25.9 versus control
-7.6, p = 0.02). The mean AHI in the treatment group fell below the OSA cut-off of 15 (11.0
± 11.0), while mean AHI in the control group was still above (21.9 ± 12.6). At follow-up only
12 patients agreed to polygraphy. In the patients who were compliant to CPAP (n = 8) the
AHI was 8.2 ± 6.2 compared to 32.8 ± 21.2 in the patients without CPAP use.
Primary outcomes
The results of IIT analyses showed that the CPAP group experienced significantly greater
improvements in cognitive status compared to the control group after the four-week
intervention period (F(9, 23) = 2.38, p = 0.02). Specifically, the CPAP group showed
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greater improvement in the domains of attention (p = 0.048) and executive functioning
(p = 0.001), but not in the other cognitive domains (Table 2). The effects were moderate
to large, partial η2 = 0.09 and 0.26, respectively. The profile of cognitive improvement
of the CPAP and control group is visualized in Figure 2a. The CPAP group did not show
significantly greater improvement in functional status after the four-week intervention
period (F(2, 30) = 1.08, p = 0.18; Table 3).
Figure 2. Profiles of cognitive change.(a) Profile of mean z-scores of difference between T1 and T0 for the nine cognitive domains in CPAP group and control group. *significant difference between the CPAP and control group. (b) Profile of mean z-scores of difference between T1 and T0 for the nine cognitive domains for the non-OSA group compared to the CPAP group and OSA control group. *significant difference between the OSA control group and both the non-OSA group and CPAP group.
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Table 2. Cognitive outcomes
CPAP (n=20) Control (n=16) P-value ΔT1*
ES ΔT1*T0 ΔT1 T0 ΔT1
Vigilance -0.33 (1.18) 0.19 (0.64) -0.33 (1.18) 0.13 (0.49) 0.34 <0.01
Attention -1.34 (1.01) 0.49 (0.66) -1.48 (1.18) 0.22 (0.46) 0.05 0.09
Memory -0.78 (1.11) 0.43 (0.82) -0.79 (1.02) 0.06 (0.87) 0.32 <0.01
Working memory -0.64 (1.30) -0.09 (0.85) -0.71 (1.44) 0.06 (0.61) 0.16 0.03
Executive functioning -1.16 (1.05) 0.37 (0.69) -0.97 (1.29) -0.17 (0.86) <0.01 0.26
Language -1.33 (0.77) 0.30 (0.59) -1.16 (1.18) 0.22 (0.60) 0.11 0.05
Visuoperception -0.34 (1.16) 0.23 (0.77) -0.87 (1.31) 0.20 (0.72) 0.38 <0.01
Psychomotor ability -0.30 (0.62) 0.00 (0.32) -0.32 (0.76) 0.00 (0.28) 0.45 <0.01
Intelligence -1.00 (1.03) 0.40 (0.58) -1.00 (1.07) 0.38 (0.81) 0.33 <0.01
Values are presented as mean z-score (standard deviation). ΔT1, difference score between T1-T0; P-value, one-tailed; ES, effect size (partial eta squared). *difference between groups by multivariate analysis of covariance adjusted for age and stroke severity.
Secondary outcomes
Based on the ITT analyses, no significant between-group differences were observed in
improvement in sleep quality, levels of sleepiness and fatigue, or symptoms of depression
and anxiety after the four-week intervention period.
Per-protocol analyses
In the per-protocol analyses, we excluded two patients from the control group and three
patients from the CPAP group, because they withdrew consent before T1 assessment.
Table 3. Functional outcomes
CPAP (n=20) Control (n=16) P-valueΔT1*
ES ΔT1*T0 ΔT1 T0 ΔT1
Neurological status1 -0.23 (1.00) 0.72 (0.63) 0.01 (0.89) 0.51 (0.68) 0.08 0.06
NIHSS2º 6.70 (4.37) -3.50 (3.28) 5.81 (3.87) -2.19 (2.71)
CNS2 8.10 (2.66) 0.98 (1.52) 8.75 (2.44) 0.69 (1.92)
ADL1 -0.33 (0.92) 1.17 (0.85) -0.31 (0.98) 1.03 (0.88) 0.11 0.05
USER mobility2 13.95 (9.73) 12.65 (9.03) 14.19 (10.14) 12.38 (10.16)
USER self care2 19.25 (10.89) 11.85 (10.00) 19.56 (10.89) 9.00 (8.48)
Values are presented as mean z-score1 or scale score2 (standard deviation). NIHSS, National Institutes of Health Stroke Scale; CNS, Canadian Neurological Scale; ADL, activities of daily living; USER, Utrecht Scale for Evaluation of Rehabilitation. º higher score is lower performance. ΔT1, difference score between T1-T0; P-value, one-tailed; ES, effect size (partial eta squared). *difference between groups by (multivariate) analysis of covariance adjusted for age and stroke severity.
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Table 4. Secondary outcomes
CPAP (n=20) Control (n=16) P-valueΔT1*T0 ΔT1 T0 ΔT1
Sleepiness (SSS) 2.05 (0.95) 0.05 (1.39) 1.81 (0.98) 0.13 (1.45) 0.41
Sleep quality (SQS) 9.10 (4.03) 1.25 (2.67) 11.19 (3.31) -1.56 (3.71) 0.07
Fatigue (CIS-20r) 73.40 (24.5) -1. 30 (22.18) 69.27 (21.27) -5.7 (21.28) 0.12
Anxiety (HADS-A) 5.15 (3.48) -0.75 (2.90) 4.06 (3.28) -0.94 (2.35) 0.45
Depression (HADS-B) 4.80 (3.17) 0.45 (3.03) 4.00 (3.20) 0.13 (4.16) 0.33
Values are presented as mean z-score (standard deviation). ΔT1, difference score between T1-T0; P-value, one-tailed. *difference between groups by multivariate analysis of covariance adjusted for age and stroke severity.
Another six patients were excluded because they were not compliant to CPAP (usage < 1
hour per night).
In total, 25 patients (11 in the CPAP group and 14 in the control group) were included
in the per protocol analyses. There were no significant differences between the groups on
baseline characteristics. The results of the per-protocol analyses were in line with those
of the ITT analyses, with the CPAP group showing significantly greater improvements in
cognitive status (F(9, 12) = 2.35, p = 0.04); specifically for the domains of attention (p =
0.044) and executive functioning (p = 0.006). The effect sizes were large, partial η2 = 0.14
and 0.33, respectively. As in the ITT analyses, no significant between-group differences
were observed in the degree of improvement in functional status (F(2, 19) = 1.04, p =
0.19) or any of the secondary outcome measures.
Comparison to non-OSA stroke patients
Background characteristics of the non-OSA group compared to the OSA group are presented
in Table IV. In comparison to the OSA patients, the non-OSA patients were significantly
younger (54 years ± 10.4), had a lower BMI (24.4 ± 3.6) and had a slightly higher level of
education. To control for the background variability, we added education level as covariate
to the analysis of cognitive functioning, and BMI to the analysis of functional status.
We found a significant difference in the improvement of cognitive functioning
between the three groups (F(18, 134)=1.55, p = 0.042), with the CPAP and non-OSA
group showing greater improvement in the domain of executive functioning than the OSA
control group, respectively (p=0.02 and p = 0.01). The effect size was moderate, partial η2
= 0.10. The cognitive profile of the three groups is visualized in Figure 2b. For functional
status, we did not find a significant difference in improvement between the three groups
((F(4, 148)=1.70, p = 0.08). Additional data on the non-OSA group in comparison to the
OSA groups are presented Supplement I, Table V-VII.
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DISCUSSIONIn this randomized trial in stroke patients with OSA, we found that four weeks of
CPAP treatment was associated with significant improvement in cognitive status during
inpatient rehabilitation. Specifically, patients in the CPAP group showed significantly more
improvement in the cognitive domains of attention and executive functioning than the
control group. We did not find significant CPAP-associated improvements on measures of
functional status, including neurological status and ADL, nor on secondary measures of
sleepiness, sleep quality, fatigue or mood. Even though the effect of CPAP on functional
status was not significant, the effect on the functional measures of neurological status
and ADL were both in the expected direction, with a trend for neurological status (p =
0.08) and a near trend for ADL (p = 0.11). The same applies to the secondary measure of
sleep quality, for which we found a trend in favor of the CPAP group (p = 0.07).
Our findings that CPAP has a beneficial effect of in the cognitive domains of attention
and executive functioning are in contrast to earlier studies that showed no improvement
after CPAP.12,14,15. This may be explained by the fact that we administered a full
neuropsychological battery as opposed to a short cognitive assessment as was the case in
these earlier studies. Moreover, our results are in line with the effect of CPAP in the general
OSA population.30,31 Based on a meta-analysis of 13 RCT’s, Kylstra et al. found a beneficial
effect of CPAP on attention.30 In that meta-analysis, executive functioning was not assessed
as a separate domain. Olaithe and Bucks, on the other hand, performed a meta-analysis
that specifically focused on executive functioning.31 They found that CPAP improved all
sub-domains of executive functioning. These latter results should be interpreted with
caution, because only a small number of the included studies had an RCT design.
Other, more indirect support for our findings comes from baseline data of the TOROS
study.7 In this TOROS sub-study, we looked at the cognitive profile of stroke patients with
OSA and compared this to stroke patients without OSA. We found that OSA patients
showed greater impairments in a number of cognitive domains, including attention and
executive functioning. These results suggest that OSA worsens the cognitive impairments
of stroke patients, which might imply that these impairments are at least partially
reversible with adequate CPAP treatment. We did not observe a significant improvement
in functional status (neurological functioning nor ADL) as a result of CPAP. Although the
results on ADL are in accordance with previous studies,11,15 our findings on neurological
functioning are not. That is, the majority of earlier studies found that CPAP improved
neurological status.11-13 There are a number of possible explanations for the lack of effect
of CPAP on functional status in our study. First, inclusion of patients was independent
of their functional status at admission. This resulted in inclusion of patients who already
had a maximum neurological status score at baseline (up to 24% of patients depending
on the applied scale); for these patients further improvement was not possible. Second,
the functional outcome measures showed a strong ceiling effect at the assessment after
the four-week intervention period. Approximately one-third of the patients (29%) had
a maximum score on the NIHSS scale, while almost half (45%) had a maximum score
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on the CNS. We observed even stronger ceiling effects for the ADL measures, with
maximum scores in 39% and 68% of the patients on the USER mobility and USER self-care
measures, respectively. Third, a number of more general limitations of our study, including
the relatively small sample size and low CPAP compliance may have affected the results.
These limitations will be discussed in more detail below.
In the stroke population, the effects of CPAP on sleepiness, quality of life and mood are
not as clear-cut as in the general population.9 Some studies found small positive effects on
measures of sleepiness and mood,12,15 while others did not.11,13,14 Our results correspond to
the latter studies, as we did not find a significant effect of CPAP on our secondary measures
of sleep quality, sleepiness, fatigue and mood. The lack of improvement may be explained
by the fact that stroke patients with OSA do not report lower levels of sleep quality or higher
levels of sleepiness, fatigue and depressed mood than stroke patients without OSA. 7
In addition to the RCT, we compared the two OSA groups (CPAP and control) to a
group of non-OSA stroke patients. We found that both the non-OSA patients and the
OSA patients in the CPAP group showed larger improvement in the cognitive domain
of executive functioning than the OSA control group, with no difference between the
non-OSA and CPAP group. Although these results should be interpreted with caution
as we were not able to match the groups, they do seem to support the hypothesis that
untreated OSA negatively affects the recovery of cognitive functioning in stroke patients,
and that adequate CPAP treatment of OSA in stroke patients can, at least partially, invert
the OSA-associated cognitive impairments.
The present study has a number of limitations that should be noted. First, the sample
size of the study was small, despite screening of a large number of patients (n = 654;
see Figure 1). Around 30% of patients were excluded based on our criteria, and another
40% were discharged early or declined to undergo a sleep examination. Of the 206
patients who underwent a sleep study, 80 patients were diagnosed with OSA. Ultimately,
just under half of these OSA patients agreed to participate in the RCT. Although these
numbers seem very low, they are representative for research in this field. In a review of 17
studies on CPAP treatment in stroke patients, Tomfohr and colleagues reported that, of
over 3400 possible participants, only 4.8% were randomized to CPAP.16
Second, we were not able to perform RCT analyses for the two-month follow-up, as
the majority of patients in the control group received CPAP treatment after the four-week
intervention period. Although, from a purely methodological perspective it would have
been preferable to delay offering CPAP treatment to the control group until the two-month
follow-up had been completed, this was not considered to be ethically acceptable.
Third, the compliance with CPAP in this study was low, despite the support of our
experienced Respicare team and involvement and education of primary caregivers. Nine
of 20 patients were rated as being non-compliant, and only seven patients had good
CPAP compliance ( > 4 hours per night for ≥ 5 days a week). Given these compliance
problems, the beneficial effects of CPAP on cognitive functioning obtained on the basis
of the intention-to treat analyses are all the more promising. Moreover, the results based
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on the per-protocol analysis suggest that, if CPAP compliance can be increased, even
greater improvement may be expected. Further exploratory analysis showed that the
seven patients with high compliance reported relatively large improvement in fatigue, as
compared to the other patients (data available on request).
The results of our study once more underscore the need to improve CPAP compliance.
Future research in this population should therefore focus on the development of methods
that augment the CPAP compliance of stroke patients. At present, the beneficial effects
of CPAP on stroke outcome found in our study, as well as in a number of earlier studies,
offer a preliminary evidence base for the use of this treatment as part of a rehabilitation
program for stroke patients.16
In conclusion, this study indicates that CPAP treatment improves cognitive functioning
of stroke patients with OSA during inpatient rehabilitation.
ACKNOWLEDGEMENTSThe authors thank Brita Daniels, Irene Kos and Mario van Lieshout for their support with
data collection.
DISCLOSURESNone
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SUPPLEMENT I
Tab
le I.
Cog
niti
ve o
utco
mes
CPA
P (n
=20
)C
on
tro
l (n
=16
)P-
valu
eΔ
T1*1
ES Δ
T1*
P-va
lue
ΔT2
*2T0
ΔT1
ΔT2
T0Δ
T1Δ
T2
Vig
ilanc
e-0
.33
(1.1
8)0.
19 (
0.64
)0.
00 (
0.44
)-0
.33
(1.1
8)0.
13 (
0.49
)-0
.17
(0.5
7)0.
34<
0.01
0.54
Att
enti
on-1
.34
(1.0
1)0.
49 (
0.66
)0.
13 (
0.34
)-1
.48
(1.1
8)0.
22 (
0.46
)0.
29 (
0.44
)0.
050.
090.
37
Mem
ory
-0.7
8 (1
.11)
0.43
(0.
82)
0.20
(0.
66)
-0.7
9 (1
.02)
0.06
(0.
87)
0.28
(0.
65)
0.32
<0.
010.
94
Wor
king
mem
ory
-0.6
4 (1
.30)
-0.0
9 (0
.85)
0.12
(0.
38)
-0.7
1 (1
.44)
0.06
(0.
61)
0.04
(0.
54)
0.16
0.03
0.28
Exec
utiv
e fu
nctio
ning
-1.1
6 (1
.05)
0.37
(0.
69)
0.50
(0.
57)
-0.9
7 (1
.29)
-0.1
7 (0
.86)
0.56
(0.
66)
<0.
010.
260.
23
Lang
uage
-1.3
3 (0
.77)
0.30
(0.
59)
0.16
(0.
54)
-1.1
6 (1
.18)
0.22
(0.
60)
0.08
(0.
52)
0.11
0.05
0.84
Vis
uope
rcep
tion
-0.3
4 (1
.16)
0.23
(0.
77)
0.16
(0.
66)
-0.8
7 (1
.31)
0.20
(0.
72)
0.22
(0.
76)
0.38
<0.
010.
98
Psyc
hom
otor
abi
lity
-0.3
0 (0
.62)
0.00
(0.
32)
-0.0
4 (0
.12)
-0.3
2 (0
.76)
0.00
(0.
28)
-0.1
2 (0
.43)
0.45
<0.
010.
53
Inte
llige
nce
-1.0
0 (1
.03)
0.40
(0.
58)
-0.0
3 (0
.40)
-1.0
0 (1
.07)
0.38
(0.
81)
0.17
(0.
47)
0.33
<0.
010.
05
Valu
es a
re p
rese
nted
as
mea
n z-
scor
e (s
tand
ard
devi
atio
n).
ΔT1
, di
ffer
ence
sco
re b
etw
een
T1-T
0; Δ
T2,
diff
eren
ce s
core
bet
wee
n T2
-T1;
ES,
eff
ect
size
(pa
rtia
l et
a sq
uare
d). *
diff
eren
ce b
etw
een
grou
ps b
y m
ultiv
aria
te a
naly
sis
of c
ovar
ianc
e ad
just
ed f
or a
ge a
nd s
trok
e se
verit
y. 1 P
-val
ue, o
ne-t
aile
d; 2 P
-val
ue,
two-
taile
d.
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Tab
le II
. Fun
ctio
nal o
utco
mes
CPA
P (n
=20
)C
on
tro
l (n
=16
)p
-val
ue
ΔT1
*3
ESΔ
T1*
P-va
lue
ΔT2
*4T0
ΔT1
ΔT2
T0Δ
T1Δ
T2
Neu
rolo
gica
l sta
tus1
-0.2
3 (1
.00)
0.72
(0.
63)
0.07
(0.
38)
0.01
(0.
89)
0.51
(0.
68)
0.16
(0.
28)
0.08
0.06
0.21
NIH
SS2 º
6.
70 (
4.37
)-3
.50
(3.2
8)-0
.18
(1.9
0)5.
81 (
3.87
)-2
.19
(2.7
1)-0
.50
(1.6
7)
CN
S28.
10 (
2.66
)0.
98 (
1.52
)0.
25 (
1.18
)8.
75 (
2.44
)0.
69 (
1.92
)0.
49 (
0.76
)
AD
L1-0
.33
(0.9
2)1.
17 (
0.85
)-
-0.3
1 (0
.98)
1.03
(0.
88)
-0.
110.
05-
USE
R m
obili
ty2
13.9
5 (9
.73)
12.6
5 (9
.03)
-14
.19
(10.
14)
12.3
8 (1
0.16
)-
USE
R se
lf c
are2
19.2
5 (1
0.89
)11
.85
(10.
00)
-19
.56
(10.
89)
9.00
(8.
48)
-
Valu
es a
re p
rese
nted
as
mea
n z-
scor
e1 or
sca
le s
core
2 (s
tand
ard
devi
atio
n).
NIH
SS,
Nat
iona
l In
stit
utes
of
Hea
lth
Stro
ke S
cale
; C
NS,
Can
adia
n N
euro
logi
cal
Scal
e; A
DL,
act
ivit
ies
of d
aily
livi
ng; U
SER,
Utr
echt
Sca
le f
or E
valu
atio
n of
Reh
abili
tati
on. º
hig
her
scor
e is
low
er p
erfo
rman
ce. Δ
T1, d
iffer
ence
sco
re b
etw
een
T1-T
0; Δ
T2,
diff
eren
ce s
core
bet
wee
n T2
-T1;
ES,
eff
ect
size
(pa
rtia
l et
a sq
uare
d).
*diff
eren
ce b
etw
een
grou
ps b
y (m
ulti
vari
ate)
ana
lysi
s of
cov
aria
nce
adju
sted
for
age
and
str
oke
seve
rity
. 3 P-v
alue
, on
e-ta
iled;
4 P-v
alue
, tw
o-ta
iled.
Tab
le II
I. Se
cond
ary
outc
omes
CPA
P (n
=20
)C
on
tro
l (n
=16
)p
-val
ue
ΔT1
*1
ESΔ
T1*
p-v
alu
eΔ
T2*2
T0Δ
T1Δ
T2T0
ΔT1
ΔT2
Slee
pine
ss (
SSS)
2.05
(0.
95)
0.05
(1.
39)
-0.0
5 (0
.82)
1.81
(0.
98)
0.13
(1.
45)
-0.1
9 (0
.83)
0.41
<0.
010.
93
Slee
p qu
alit
y (S
QS)
9.10
(4.
03)
1.25
(2.
67)
0.95
(2.
44)
11.1
9 (3
.31)
-1.5
6 (3
.71)
0.25
(4.
05)
0.07
0.07
0.33
Fati
gue
(CIS
-20r
)73
.40
(24.
5)-1
.30
(22.
18)
1.50
(13
.06)
69.2
7 (2
1.27
)-5
.7 (
21.2
8)2.
50 (
10.1
2)0.
120.
040.
30
Anx
iety
(H
AD
S-A
)5.
15 (
3.48
)-0
.75
(2.9
0)1.
45 (
3.24
)4.
06 (
3.28
)-0
.94
(2.3
5)1.
00 (
2.71
)0.
45<
0.01
0.21
Dep
ress
ion
(HA
DS-
B)4.
80 (
3.17
)0.
45 (
3.03
)1.
20 (
2.82
)4.
00 (
3.20
)0.
13 (
4.16
)0.
19 (
2.04
)0.
33 <
0.01
0.29
Valu
es a
re p
rese
nted
as
mea
n sc
ale
scor
es (
stan
dard
dev
iati
on).
ΔT1
, di
ffer
ence
sco
re b
etw
een
T1-T
0; Δ
T2,
diff
eren
ce s
core
bet
wee
n T2
-T1;
ES,
eff
ect
size
(p
arti
al e
ta s
quar
ed).
*di
ffer
ence
bet
wee
n gr
oups
ana
lysi
s of
cov
aria
nce
adju
sted
for
age
and
str
oke
seve
rity
. 1 P-v
alue
, on
e-ta
iled;
2 P-v
alue
, tw
o-ta
iled.
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7
Table IV. Patient characteristics of the non-OSA and OSA group
Characteristics Non-OSA (n=44) OSA (n=36) p-value
Age (years) 54.0 (10.4) 59.1 (8.6) 0.02a
Sex (males) 23 (52.3) 22 (61.1) 0.43b
Education level, median (range) 4 (1-6) 4 (1-6) 0.03c
BMI 24.4 (3.6) 27.1 (5.8) 0.01a
Stroke type 0.97b
Ischemia 29 (65.9) 24 (66.7)
Hemorrhage 12 (27.3) 10 (27.8)
Subarachnoid hemorrhage 3 (6.8) 2 (5.6)
Stroke severity 0.94c
Mild (LACS) 8 (18.2) 6 (16.7)
Moderate (PACS/POCS) 32 (72.7) 27 (75.0)
Severe (TACS) 4 (9.1) 3 (8.3)
Recurrent stroke 6 (13.6) 6 (16.7) 0.71b
Days between onset and admission 17.6 (13.7) 16.8 (15.0) 0.80a
Days between onset and NPA 38.6 (14.7) 35.9. (18.2) 0.47a
Days admitted to rehabilitation unit 71.1 (29.2) 74.7 (33.9) 0.77a
Apnea-hypopnea index 6.6 (4.3)1 34.2 (14.8) <0.001a
Oxygen desaturation index 8.6 (6.8) 30.5 (14.3) <0.001a
Values are presented as mean (SD) or n (%).¹ based on 23 non-OSA patients that underwent polygraphy. PACS, partial anterior circulation stroke; LACS, lacunar stroke; TACS, total anterior circulation stroke; POCS, posterior circulation stroke.; NPA, neuropsychological assessment. a Student’s t-test; b Chi-square test; c Mann-Witney U test.
108
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E: EFFECT O
N R
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7
Tab
le V
. Cog
niti
ve o
utco
mes
for
non
-OSA
gro
up c
ompa
red
to O
SA g
roup
s
No
n-O
SA (
n=
44)
CPA
P (n
=20
)C
on
tro
l (n
=16
)
T0Δ
T1Δ
T2 (
n=
31)
ES Δ
T1ES
* Δ
T2ES
ΔT1
ES Δ
T2ES
ΔT1
ES Δ
T2
Vig
ilanc
e0.
14 (
0.84
)-0
.06
(0.9
0)0.
20 (
0.63
)-0
.07
0.24
0.16
0.00
0.11
-0.1
4
Att
enti
on-0
.79
(1.0
9)0.
49 (
0.76
)0.
27 (
0.57
)0.
410.
220.
440.
110.
180.
22
Mem
ory
-0.6
8 (1
.34)
0.51
(0.
92)
0.39
(0.
78)
0.41
0.31
0.37
0.15
0.06
0.27
Wor
king
mem
ory
-0.3
3 (1
.39)
0.52
(0.
80)
0.02
(0.
63)
0.40
0.02
-0.0
70.
080.
040.
04
Exec
utiv
e fu
ncti
onin
g-0
.70
(1.1
8)0.
37 (
0.61
)0.
25 (
0.65
)0.
310.
220.
350.
45-0
.12
0.34
Lang
uage
-0.9
8 (0
.78)
0.29
(0.
71)
0.10
(0.
52)
0.32
0.11
0.33
0.14
0.21
0.08
Vis
uope
rcep
tion
-0.1
4 (0
.78)
0.03
(0.
90)
0.44
(0.
90)
0.04
0.57
0.20
0.14
0.14
0.17
Psyc
hom
otor
abi
lity
0.03
(0.
52)
-0.0
6 (0
.37)
0.07
(0.
26)
-0.1
20.
140.
00-0
.06
0.00
-0.1
3
Inte
llige
nce
-0.3
6 (1
.24)
0.15
(0.
90)
0.47
(0.
81)
0.13
0.56
0.39
-0.0
30.
330.
14
Valu
es a
re p
rese
nted
as
mea
n z-
scor
e (s
tand
ard
devi
atio
n).
ΔT1
, di
ffer
ence
sco
re b
etw
een
T1-T
0; Δ
T2,
diff
eren
ce s
core
bet
wee
n T2
-T1;
ES,
eff
ect
size
(C
ohen
’s d
); N
egat
ive
ES,
decr
ease
of
cogn
itiv
e pe
rfor
man
ce.*
base
d on
n =
31.
109
CPA
P IN STR
OK
E: EFFECT O
N R
EHA
BILITA
TION
OU
TCO
ME
7Ta
ble
VII.
Sec
onda
ry o
utco
mes
for
non
-OSA
gro
up c
ompa
red
to O
SA g
roup
s
No
n-O
SA (
n=
44)
CPA
P (n
=20
)C
on
tro
l (n
=16
)
T0Δ
T1Δ
T2 (
n=
31)
ES Δ
T1ES
* Δ
T2ES
ΔT1
ES Δ
T2ES
ΔT1
ES Δ
T2
Slee
pine
ss (
SSS)
2.
09 (
1.16
)0.
25 (
1.29
)-0
.45
(1.0
6)-0
.21
0.43
-0.0
50.
05-0
.13
0.19
Slee
p qu
alit
y (S
QS)
9.36
(3.
76)
0.50
(4.3
3)0.
35 (
3.80
)0.
130.
060.
350.
34-0
.46
0.12
Fati
gue
(CIS
-20r
)73
.63
(21.
56)
-0.3
0 (2
1.84
)-0
.84
(14.
6)0.
030.
070.
06-0
.06
0.27
-0.1
1
Anx
iety
(H
AD
S-A
)6.
16 (
4.11
)-0
.68
(3.2
1)-0
.71
(3.0
1)0.
180.
160.
21-0
.42
0.31
-0.3
6
Dep
ress
ion
(HA
DS-
B)6.
30 (
4.32
)-0
.09
(4.2
5)-0
.39
(2.9
7)0.
020.
20-0
.13
-0.3
7-0
.03
-0.0
4
Valu
es a
re p
rese
nted
as
mea
n z-
scor
e1 or
sca
le s
core
2 (S
D).
ΔT1
, di
ffer
ence
sco
re b
etw
een
T1-T
0; Δ
T2;
diff
eren
ce s
core
bet
wee
n T2
-T1;
ES,
eff
ect
size
(C
ohen
’s d
); N
egat
ive
ES,
incr
ease
of
com
plai
nts.
*bas
ed o
n n
= 3
1.
Tab
le V
I. Fu
ncti
onal
out
com
es f
or n
on-O
SA g
roup
com
pare
d to
OSA
gro
ups
No
n-O
SA (
n=
44)
CPA
P (n
=20
)C
on
tro
l (n
=16
)
T0Δ
T1Δ
T2 (
n=
31)
ES Δ
T1ES
* Δ
T2ES
ΔT1
ES Δ
T2ES
ΔT1
ES Δ
T2
Neu
rolo
gica
l sta
tus1
0.10
(0.
99)
0.70
(0.
49)
0.11
(0.
25)
0.90
0.19
0.82
0.10
0.62
0.21
NIH
SS2 º
5.64
(4.
48)
-3.1
8 (2
.57)
-0.4
8 (1
.15)
CN
S29.
1 (2
.48)
1.00
(1.
30)
0.22
(0.
73)
Act
iviti
es o
f da
ily li
ving
10.
27 (
0.91
)0.
88 (
0.74
)-
1.26
-1.
49-
1.09
-
USE
R m
obili
ty2
20.0
5 (1
0.07
)9.
98 (
8.28
)-
--
-
USE
R se
lf c
are2
25.5
9 (1
0.02
) 8.
45 (
8.94
)-
--
-
Valu
es a
re p
rese
nted
as
mea
n z-
scor
e1 or
sca
le s
core
2 (s
tand
ard
devi
atio
n). N
IHSS
, Nat
iona
l Ins
titu
tes
of H
ealt
h St
roke
Sca
le; C
NS,
Can
adia
n N
euro
logi
cal S
cale
; USE
R,
Utr
echt
Sca
le f
or E
valu
atio
n of
Reh
abili
tati
on.
º hi
gher
sco
re is
low
er p
erfo
rman
ce.
ΔT1
, di
ffer
ence
sco
re b
etw
een
T1-T
0; Δ
T2,
diff
eren
ce s
core
bet
wee
n T2
-T1;
ES,
ef
fect
siz
e (C
ohen
’s d
). *
base
d on
n =
31
110
SLEEP APNEA IN STROKE: SUMMARY AND
GENERAL DISCUSSION
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SUMMARYThe overall objective of this thesis was to improve our understanding of the effects of
obstructive sleep apnea (OSA) and its treatment with continuous positive airway pressure
(CPAP) on the recovery of stroke patients. Specifically, the aims of our studies were to 1)
improve early recognition of sleep apnea in stroke patients during inpatient rehabilitation,
2) determine the effects of OSA on daily functioning, more specific on cognitive and
functional status of stroke patients at admission to stroke rehabilitation, and 3) investigate
whether CPAP treatment can improve the recovery of cognitive and functional outcome
after stroke. In this chapter we will summarize and discuss the results of the studies
described in this thesis. We will address methodological considerations of our studies
and discuss the clinical implications of this thesis. Finally, we will conclude with some
recommendations for future research.
Chapter 1 provided a general introduction to the topic of this thesis, and started with
the definition, incidence, consequences and treatment options of both stroke and sleep
apnea. Subsequently, the relationship between stroke and sleep apnea was outlined and
the current literature on this subject was briefly summarized.
CPAP treatment in the general OSA population
In preparation of our study on the effect of CPAP treatment on cognitive functioning in
stroke patients, we first carried out a meta-analysis of CPAP treatment in the general OSA
population. Earlier reviews suggested that CPAP would improve cognitive functioning in
several domains such as vigilance, attention and memory, but due to the qualitative nature
of these reviews the magnitude of these effects was unclear.1-3 In Chapter 2 the results
of our meta-analysis of thirteen randomized controlled trials (RCT’s) on the effect of CPAP
treatment on neuropsychological functioning were presented. The main finding of this
meta-analysis was a small, positive effect of CPAP in the domain of attention, while no
significant improvement was seen in the other cognitive domains. More specifically, the
improvement was observed on tasks for divided attention, with the more demanding tasks
revealing larger improvements. Additionally, in our selected studies, we found improvement
on measures of sleepiness and mood, which is in line with prior research.4 Although this
meta-analysis indicated that, contrary to earlier views, only slight improvement of cognitive
functioning after CPAP treatment can be expected, additional RCT’s are needed to further
quantify the influence of OSA severity, treatment duration and compliance on this effect.
Early recognition of sleep apnea in stroke patients during inpatient rehabilitation
Sleep apnea is a highly prevalent sleep disorder in stroke patients with reported prevalence
ranging from 38% to 78% compared to 3-17% in the general population.5,6 Moreover, sleep
apnea has been found to contribute to poor functional recovery, increased risk of recurrent
stroke, and post-stroke mortality.7-10 Despite its high prevalence and serious consequences,
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standard evaluation of sleep apnea in stroke rehabilitation settings is limited.11 The standard
diagnostic test for sleep apnea is overnight polysomnography or multichannel polygraphy.12
In stroke rehabilitation, however, these approaches are often not feasible due to limited
access, high costs, and practical constraints. To overcome these limitations and to improve
sleep apnea screening in stroke rehabilitation, we proposed a step-by-step diagnostic
approach to sleep apnea in which stroke patients only receive the level of sleep apnea
screening they need. This approach asks for easily administered and reliable screening
instruments. In Chapter 3 and Chapter 4 we therefore investigated the validity of two
different screening methods for the detection of sleep apnea in stroke patients.
The aim of the study described in Chapter 3 was to validate the use of nocturnal
oximetry for screening of sleep apnea and to identify possible clinical predictors of sleep
apnea in stroke rehabilitation. Oximetry is a method that monitors the number of oxygen
desaturations per hour (ODI) and the heart rate through an infrared sensor usually placed
on the fingertip. Although the use of oximetry as a screening instrument for sleep apnea
in sleep laboratories is widespread, the sensitivity and specificity of the instrument in
the stroke population had not yet been subject of investigation.13 In our study 56 stroke
patients underwent both nocturnal polygraphy and oximetry. We found that forty-six
percent of the patients were diagnosed with sleep apnea (AHI≥15). The majority of stroke
patients with sleep apnea was male, older and had a higher body mass index than the
stroke patients without sleep apnea. Nocturnal oximetry accurately discriminated between
patients with and without a sleep apnea, with a 93% diagnostic accuracy. The sensitivity
of nocturnal oximetry (ODI ≥ 15) was 77% with a specificity of 100%. Given the high
prevalence of sleep apnea in the studied population, a positive oximetry result increased
the likelihood of sleep apnea to 100% (positive predictive value), while a negative result
lowered the probability to 17% (negative predictive value of 83%). Other clinical variables
did not add to the predictive value of oximetry. Our findings showed that nocturnal
oximetry is an adequate screening instrument for sleep apnea in the stroke population.
In continuation of this study we wanted to investigate, as part of the stepped
diagnostic approach, if even easier to administer screening instruments such as self-report
questionnaires would be useful in the detection of sleep apnea in the stroke population.
A number of previous studies had found that the well-validated sleep apnea screening
questionnaire, the Berlin questionnaire, could not adequately discriminate between stroke
patients with and without sleep apnea.11,14,15 Thus, in Chapter 4 we sought to develop
a predictive model based on a sleep apnea questionnaire consisting of self-reported
symptoms, socio-demographic variables and clinical parameters that could serve as a
first reliable screening method for sleep apnea in the stepped diagnostic approach. We
administered our self-designed sleep apnea questionnaire to 438 stroke patients and
used nocturnal oximetry to identify the patients with a high likelihood of sleep apnea
(ODI ≥ 15). The following variables of the sleep questionnaire were included in our final
prediction model: sex, age, BMI, and self-reported symptoms of apneas and falling asleep
during daytime. We found that this prediction model had an acceptable diagnostic
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accuracy of 76%. Using a moderate probability cut-off, the sensitivity and specificity were
72% and 69%, respectively. The corresponding positive predictive value was 50%, while
the negative predictive value was 83%. We therefore concluded that a questionnaire
including the variables of our prediction model seems to be a reasonable first screening
tool in stepped detection of sleep apnea in stroke patients, as it significantly reduces the
number of patients who need more elaborate sleep apnea screening.
The effect of OSA and CPAP treatment on rehabilitation outcome
As has been mentioned above, OSA is a common sleep disorder in stroke patients and
associated with poor functional outcome and increased mortality.7-10 However, research on
the effect of OSA on cognitive functioning following stroke is scarce. Moreover, a number
of studies suggest that CPAP improves functional recovery of stroke patients, but the effect
on cognitive functioning has not been thoroughly investigated.16 Chapter 5 provided a
detailed description of the rationale, design and methods of our study protocol “Treatment of
Obstructive sleep apnea on Rehabilitation Outcome in Stroke” (TOROS). The primary objective
of the TOROS study was to investigate the effectiveness of CPAP on both cognitive and
functional outcomes in stroke patients with OSA with a RCT. Supplementary to this RCT, the
protocol encompassed a case-control study that aimed to examine the association between
OSA and cognitive and functional status in stroke patients. With the publication of the study
protocol, including the main clinical endpoints before the results were known, we aimed to
augment the transparency of our research.
In Chapter 6 the results of this case-control study are presented. In total, 80 stroke
patients with OSA (AHI ≥ 15) and 67 stroke patients without OSA (AHI < 5) participated. All
these patients were compared on cognitive and functional status during early admission
to the inpatient stroke rehabilitation unit of Heliomare. Our main finding was that OSA
patients were significantly more impaired on both cognitive and functional status than
stroke patients without OSA. More specifically, OSA patients were more impaired in the
cognitive domains of attention, executive functioning, visuoperception, psychomotor
ability and intelligence, and had poorer neurological status and a lower level of functional
independence. Further, OSA patients spent on average eleven days longer on the inpatient
stroke rehabilitation unit than patients without OSA. Finally, contrary to findings in the
general OSA population, we did not find that stroke patients with OSA differed from
patients without OSA on reported levels of sleepiness, fatigue, and sleep quality or on
reported symptoms of depression and anxiety.
The focus of Chapter 7 was on the effect of CPAP treatment on the rehabilitation
outcome of stroke patients with OSA. Sixteen stroke patients with OSA (AHI ≥ 15) were
assigned to four weeks of rehabilitation treatment as usual, while twenty patients received
four weeks of CPAP treatment. After this four-week intervention period patients receiving
CPAP treatment showed greater improvement of cognitive functioning in the domains of
attention and executive functioning than patients who received treatment as usual. These
effects were moderate to large, implying that they were not only statistically significant,
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but also clinically relevant. CPAP was not associated with improvement of functional
status, or of sleepiness, fatigue, sleep quality or mood. Even though no significant effect
on functional status was found, our findings did indicate a trend in the expected direction.
The average CPAP compliance was 2.5 hours per night, which is considered low. The low
compliance may have led to an underestimation of the true effect of CPAP on stroke
rehabilitation. This hypothesis is supported by the results of a subsequent per-protocol
analysis, which suggested that if CPAP compliance could be increased, even greater
improvement may be expected. We concluded that the beneficial effects of CPAP on
stroke outcome found in our study offer a preliminary evidence base for the use of this
treatment as part of a rehabilitation program for stroke patients.
DISCUSSIONDespite the growing evidence that sleep apnea is an independent risk factor for stroke and
increases the risk of new cardiovascular incidents in stroke patients, sleep apnea still often
remains unrecognized and untreated in the stroke rehabilitation.11 When left untreated,
sleep apnea can lead to less optimal functional recovery and prolonged hospitalization.7-10
One of the main aims of our studies was therefore to improve early recognition of sleep
apnea in stroke patients. The findings of the first part of the thesis provided a stepped
diagnostic approach to sleep apnea. This approach has a number of important advantages.
First, one of our studies showed that with a limited number of questions an adequate first
selection can be made. Second, the prediction model based on these questions gives
physicians the opportunity to choose a cutoff with sensitivity and specificity trade-off, that
they find acceptable. Third, our research indicates that nocturnal oximetry, the second
step in the proposed stepped diagnostic approach, is well tolerated by stroke patients, as
opposed to poly(somno)graphic sleep studies. Moreover, it appeared to be highly accurate
for predicting sleep apnea in the stroke population. In addition, both measures are far
less expensive than a poly(somno)graphic sleep study. Even though a full sleep study in a
selection of patients is still required, the stepped diagnostic approach seems to be a useful
method to efficiently employ the limited resources in stroke rehabilitation centers.
The aim of the TOROS study, described in the second part of the thesis, was twofold.
Herewith, we aimed to investigate the effect of OSA on cognitive and functional status
of stroke patients, and to examine whether CPAP treatment could improve the recovery
of cognitive and functional outcome of stroke patients with OSA. Taking the findings
of the TOROS study as a whole, we can formulate two main conclusions. First, OSA is
associated with considerable impairments in daily functioning of stroke patients in a
number of cognitive and functional domains. Second, CPAP treatment has a beneficial
effect on cognitive functioning, while it does not seem to improve other domains of daily
functioning. These findings suggest that the OSA related impairments in stroke patients
only partially improve with CPAP treatment. This hypothesis is supported by our meta-
analysis on the effect of CPAP treatment on cognition in the general population, in which
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we concluded that only slight improvement of cognitive functioning can be expected
after treatment.17 Functional measures, however, were not included in this meta-analysis
of otherwise healthy adults, making it difficult to draw a comparison with stroke patients.
Moreover, it should be noted that the CPAP compliance in our study was in general
low (< 4 hours per night), which may have limited the effect of CPAP treatment. The low
compliance may explain why, contrary to a number of earlier studies, we did not find
significant CPAP-associated improvements on measures of functional status.18-20 The fact
that we did observe a trend in the expected direction, seems to support this hypothesis.
In a review by Thomfor and colleagues on the effect of CPAP treatment in stroke patients,
they describe three studies that found improvement on neurological status and two
studies that did not.16 The studies in which CPAP improved neurological functioning in
general had good compliance (average of ≥ 4 hours per night) and relatively large samples,
while the studies in which no improvement was seen both studied a small sample and
suffered from compliance problems. These findings imply that if patients are compliant
to CPAP treatment modest improvement in daily functioning can be expected. However,
these contradictory results also emphasize the need of further research on OSA and its
treatment in the stroke rehabilitation. More specific recommendations for future research
are discussed at the end of this chapter. In the next paragraph the methodological issues
that may further explain the contradictory findings will be discussed in more detail.
Methodological considerations
To the best of our knowledge, the studies presented in this thesis were the first to
investigate the effect of OSA and its treatment on cognitive functioning using an elaborate
neuropsychological assessment. Previous studies used cognitive screening instruments
or limited test batteries targeting one specific cognitive domain, which did not show
beneficial effects of CPAP treatment.20-22 Extensive neuropsychological testing has a
number of advantages. First, we were able to assess the domain-specific effects of OSA.
Second, these neuropsychological tests have good psychometric properties and are more
sensitive to change than cognitive screening instruments. Third, we were able to compare
our findings in the stroke population to the extensive literature in the general OSA
population. In addition, we included measures of neurological status, activities of daily
living and subjective wellbeing such as sleepiness, fatigue and mood. The broad scope of
our studies gave us the opportunity to gain insight in the effect of OSA on the different
aspects of stroke rehabilitation and daily functioning of stroke patients. Furthermore, we
were able to compare our findings to earlier studies that primarily aimed at functional
outcomes of stroke patients, and thus to contribute to the limited research in this field.
Another strength of our research was that we were able to screen over 400 stroke
patients on sleep apnea. With this large database, we were able to build a prediction
model with acceptable diagnostic accuracy that provides physicians with a reasonable first
screening tool in the stepped detection of sleep apnea in stroke rehabilitation. In comparison,
previous studies that investigated the clinical value of screening questionnaires included
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relatively small samples (< 100 stroke patients) and found that these questionnaires were
not able to adequately discriminate between patients with and without sleep apnea.11,14,15
Several methodological limitations of the research presented in this thesis should also
be addressed. Specific limitations of each study were discussed in the relevant chapters, but
some have more general implications and are therefore noted here. First, we performed a
two-tiered screening procedure for the diagnosis of sleep apnea using nocturnal oximetry
and polygraphy instead of the golden standard, full overnight polysomnography. We chose
this method because it is less burdensome and costly than polysomnography. However, this
method may have led to misclassification of patients as it can give an underestimation of
the severity of sleep apnea in case of low sleep efficiency. Second, our patient sample may
not have been representative for stroke rehabilitation. In the TOROS study around one third
of the patients were excluded based on our criteria, and approximately half of the eligible
patients did not participate because of early discharge from the rehabilitation unit or because
they declined to undergo testing. Therefore, there is a possibility of a selection bias in our
study. Third, the research on the effect of CPAP treatment suffered from small sample size
and low CPAP compliance. Despite the support of our experienced team of physicians and
nurses and the involvement and education of primary caregivers, only half of the patients
were adherent to CPAP during the four-week intervention period. Our results therefore
warrant replication, preferably in larger multi-center studies. Another limitation concerns
the classification of neuropsychological tests into cognitive domains. In our research we
categorized the neuropsychological tests into cognitive domains based on conventional
classification as described in a standard textbook of neuropsychological assessment.23 This is
to some degree arbitrary, because neuropsychological tests almost never tap into one single
cognitive domain. Moreover, cognitive domains are not isolated entities, but rather related
and somewhat overlapping constructs. At the same time, classification has its own advantages
as it reduces the number of outcome measures and allows easier comparison between studies.
Clinical implications
The findings of this thesis confirm the high prevalence of sleep apnea in stroke rehabilitation.
Moreover, the result that OSA is associated with worse cognitive and functional status of
stroke patients underline the need for sleep apnea screening and treatment within the
stroke rehabilitation. Standard evaluation of sleep apnea during stroke rehabilitation still
seems to be limited in clinical practice. In a survey we performed in 2012, we found
that of the nine participating rehabilitation centers in the Netherlands only two centers
performed standardized screening of sleep apnea. The lack of standardized screening
seemed to be at least partly explained by the high costs, practical constraints and lack of
validated screening instruments for OSA in the (stroke) rehabilitation. This thesis provides
a stepped diagnostic approach to sleep apnea in which stroke patients receive only the
level of screening they need, beginning with simple, less costly methods followed by a
full sleep study, if required. Although full sleep examination is still needed for the true
diagnosis of sleep apnea, the first part of our thesis provides physicians with guidelines to
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introduce standard evaluation in stroke rehabilitation. These guidelines may increase the
awareness of sleep apnea, without the need to acquire expensive polygraphic systems or
to make a large number of unnecessary referrals to specialized sleep centers.
Furthermore, the results from our treatment study indicate that CPAP treatment leads
to clinically relevant improvement of functioning in the cognitive domains of attention
and executive functioning of stroke patients. Although the benefit of CPAP in our study
was limited to these specific domains of functioning, it underscores the promise of this
intervention. However, this must be placed in the context that CPAP compliance remains
a challenge. Matthias and colleagues investigated both the challenges and motivating
factors related to CPAP treatment in stroke patients.24 They found that one of the main
impeding factors was that most patients were asymptomatic with respect to OSA. They
were therefore less accepting of the diagnosis and less motivated to initiate treatment.
Also they often did not perceive the benefits of CPAP. In addition to being ‘invisible’, OSA
was also perceived as a harmless condition by many stroke patients. On the other hand,
Matthias and colleagues found that the only significant motivating factor was reducing
risk of another stroke. Thus, emphasizing the potential of CPAP treatment to avoid a
subsequent stroke may be a useful motivator for clinicians and for their stroke patients
with OSA. Additionally, a good understanding of technical difficulties of CPAP treatment
and quick troubleshooting may help to maintain compliance over time. Moreover,
alternative treatment methods could be considered to improve treatment compliance of
stroke patients with OSA. For patients, who primarily suffer from apneas in the supine
position (positional OSA) less invasive treatment options are available. These alternatives
further stress the importance of adequate OSA screening. In the section below specific
treatment options for positional OSA are described in more detail.
Recommendations for future research
Considering the high prevalence and clinical implications of sleep apnea in stroke
rehabilitation, there is a need for better recognition of sleep apnea in stroke patients.
In this thesis we proposed a stepped-diagnostic approach to sleep apnea. Our findings
suggest that this approach can be considered an adequate and feasible screening method
for sleep apnea during stroke rehabilitation. However, further validation of this approach
in a similar stroke rehabilitation setting is still required. Moreover, future research should
focus on the cost-effectiveness of this method. Also, it would be interesting to investigate
whether this method is applicable in the broader stroke population, such as stroke patients
in the acute or chronic phase, because the under-recognition and impeding effect of sleep
apnea is not limited to stroke rehabilitation.
Second, there is a need for more studies on the association between OSA and
cognitive functioning of stroke patients. As we have shown, OSA is related to impairments
in different aspects of cognitive functioning during early rehabilitation. Future studies
should aim to see whether these findings can be replicated and should also investigate the
course of OSA related cognitive impairments over a longer period of time.
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Third, the beneficial effect of CPAP on stroke rehabilitation remains largely unclear.
The small number of earlier RCT’s show mixed results, with some studies indicating that
CPAP improves neurological recovery, daytime sleepiness and depressive symptoms.16
In our study we were not able to replicate these findings, but we did find that CPAP
ameliorates cognitive functioning. To date, most studies included small sample sizes
drawn from one rehabilitation center. Furthermore, these studies often only investigated
functional outcomes and scarcely included measures of cognitive functioning. Thus, we
recommend that future research should aim to assess the effect of CPAP on a combination
of functional and cognitive outcome measures in larger multi-center studies. However,
it might be more valuable to focus on methods to augment CPAP compliance within
the stroke population first, before developing large and expensive multi-center studies.
Additionally, future studies should aim to investigate other treatment methods, such as
positional therapy techniques or oral appliance therapy. Positional training is a treatment
method for OSA patients who mostly suffer from apneas in the supine sleep position,
which is the case in around half of the OSA patients.25 For a long time positional training
consisted of a tennis ball fastened to the back with a belt or strap to make sleeping in supine
position uncomfortable, and thus prevent positional apneas. In an attempt to decrease the
discomfort and to improve compliance, new treatment devices have been developed that
vibrate when patients turn to the supine position, stimulating them to return to a side
position.26 Another treatment option is an oral appliance, this is a mouthpiece that can
either increase the size of the upper airway or help prevent the collapse of the tongue
and soft tissues leading to a decrease of sleep apnea.27 Both therapy methods are often
perceived as less invasive and could therefore be a good alternative to CPAP treatment in
the stroke population, especially in patients who do not tolerate CPAP treatment.26,28
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1 Aloia MS, Arnedt JT, Davis JD, Riggs RL, Byrd D. Neuropsychological sequelae of obstructive sleep apnea-hypopnea syndrome: A critical review. J Int Neuropsychol Soc 2004;10:772-785.
2 Weaver TE, Chasens ER. Continuous positive airway pressure treatment for sleep apnea in older adults. Sleep Med Rev 2007;11:99-111.
3 Sánchez AI, Martínez P, Miró E, Bardwell WA, Buela-Casal G. CPAP and behavioral therapies in patients with obstructive sleep apnea: Effects on daytime sleepiness, mood, and cognitive function. Sleep Med Rev 2009;13:223-233.
4 Giles TL, Lasserson TJ, Smith BH, White J, Wright J, Cates CJ. Continuous positive airways pressure for obstructive sleep apnoea in adults. The Cochrane Library 2006;4:1-107.
5 Johnson KG, Johnson DC. Frequency of sleep apnea in stroke and TIA patients: A meta-analysis. J Clin Sleep Med. 2010;6:131–137.
6 Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. Increased prevalence of sleep-disordered breathing in adults. AM J Epidemiol 2013;177:1006-1014.
7 Good DC, Henkle JQ, Gelber D, Welsh J, Verhulst S. Sleep-disordered breathing and poor functional outcome after stroke. Stroke 1996;27:252–259.
8 Kaneko Y, Hajek VE, Zivanovic V, Raboud J, Bradley TD. Relationship of sleep apnea to functional capacity and length of hospitalization following stroke. Sleep 2003;26:293–297.
9 Cherkassky T, Oksenberg A, Froom P, Ring H. Sleep-related breathing disorders and rehabilitation outcome of stroke patients: a prospective study. Am J Phys Med Rehab 2003;83:452–455.
10 Turkington PM, Allgar V, Bamford J, Wanklyn P, Elliott MW. Effect of upper airway obstruction in acute stroke on functional outcome at 6 months. Thorax 2004;59:367–371.
11 Srijithesh PR, Shukla G, Srivastav A, Goyal V, Singh S, Behari M. Validity of the Berlin questionnaire in identifying obstructive sleep apnea syndrome when administered to the informants of stroke patients. J Clin Neurosci 2011;18:340-343.
12 Epstein L, Kristo D, Strollo P, Friedman N, Malhotra A, Patil S, et al. Clinical guideline for the evaluation, management and long-term
care of obstructive sleep apnea in adults. J Clin Sleep Med 2009;5:263-276.
13 Netzer N, Eliasson AH, Netzer C, Kristo DA. Overnight pulse oximetry for sleep-disordered breathing in adults - A review. Chest. 2001; 120:625-633.
14 ElKholy SH, Amer HA, Nada MM, Nada MAF, Labib A. Sleep-related breathing disorders in cerebrovascular stroke and transient ischemic attacks: A comparative study. J Clin Neurophysiol 2012;29:194-198.
15 Kotzian S, Stanek J, Pinter M, Grossmann W, Saletu M. Subjective evaluation of sleep apnea is not sufficient in stroke rehabilitation. Top Stroke Rehabil 2012;19:45-53.
16 Tomfohr LM, Hemmen T, Natarajan L, Ancoli-Israel S, Loredo JS, Heaton RK, Bardwell W, et al. Continuous Positive Airway Pressure for Treatment of Obstructive Sleep Apnea in Stroke Survivors What Do We Really Know? Stroke 2012;43:3118-3123.
17 Kylstra WA, Aaronson JA, Hofman WF, Schmand BA. Neuropsychological functioning after CPAP treatment in obstructive sleep apnea: a meta-analysis. Sleep Med Rev. 2013;17:341-347.
18 Bravata DM, Concato J, Fried T, Ranjbar N, Sadarangani T, McClain F, Struve F, et al. Continuous positive airway pressure: Evaluation of a novel therapy for patients with acute ischemic stroke. Sleep. 2011;34:1271-1277.
19 Parra O, Sanchez-Armengol A, Bonnin M. Early treatment of obstructive sleep apnoea and stroke outcome: a randomised controlled trial. Eur Respir J. 2011;37:1128-1136.
20 Ryan CM, Bayley M, Green R, Murray BJ, Bradley TD. Influence of continuous positive airway pressure on outcomes of rehabilitation in stroke patients with obstructive sleep apnea. Stroke. 2011;42:1062-1067.
21 Brown DL, Chervin RD, Kalbfleisch JD, Zupancic MJ, Migda EM, Svatikova A, ConCannon M, Martin C, Weatherwax KJ, Morgenstern LB. Sleep apnea treatment after stroke (SASTS) trial: Is It feasible? J Stroke Cerebrovas Dis. 2013;22:1216-1224.
22 Sandberg O, Franklin KA, Bucht G, Eriksson S, Gustafson, Y. Nasal continuous positive airway pressure in stroke patients with sleep apnoea: a randomized treatment study. Eur Respir J. 2001;18: 630-634.
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23 Lezak MD, Howieson DB, Digler ED, Tranel D: Neuropsychological Assessment. 5th edition. Cary, USA: Oxford University Press; 2012.
24 Matthias MS, Chumbler NR, Bravata DM, Yaggi HK, Ferguson J, Austin C, et al. Challenges and motivating factors related to positive airway pressure therapy for post-TIA and stroke patients. Behavioral sleep medicine. 2014;12:143-157.
25 Oksenberg A, Silverberg DS, Arons E, Radwan H. Positional vs nonpositional obstructive sleep apnea patients. Chest 1997;112:629-639.
26 van Maanen JP, Meester KA, Dun LN, Koutsourelakis I, Witte BI, Laman DM et al.
The sleep position trainer: a new treatment for positional obstructive sleep apnoea. Sleep and Breathing 2013;17:771-779.
27 Ng AT, Gotsopoulos H, Qian J, Cistulli PA. Effect of oral appliance therapy on upper airway collapsibility in obstructive sleep apnea. Am J Respir Crit Care Med 2003;168:238–241.
28 Marklund M, Verbraecken J, Randerath W. Non-CPAP therapies in obstructive sleep apnoea mandibular advancement device therapy. Eur Respir J 2012;39:1241–1247.
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APPENDIX
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NEUROPSYCHOLOGICAL TESTS BY COGNITIVE DOMAIN
Vigilance
Psychomotor Vigilance Task1 The test measures the speed with which subjects respond to a visual stimulus on a computer screen by pressing the space bar during a 10-min period. Scores are mean reaction time and number of lapses (> 500 ms).
Attention
D-KEFS Trail Making Test (TMT),2 condition 2-3a Color Trails Test,3 condition 1Subjects are asked to connect numbers in ascending order (TMT condition 2, Color Trails Test condition 1) and connect letters in alphabetical order (TMT condition 3) by drawing lines between them with a pencil. Scores are time to completion in seconds.
d2 Test of Attention4
Subjects are asked to cross out the target stimuli with a pencil (d’s with two accent marks), while ignoring the distracting stimuli. The test consists of 14 lines of stimuli for which the subjects have 20 sec per line. Scores are number of correctly crossed out targets minus number of incorrectly crossed out distracting stimuli.
Memory
Rey Auditory Verbal Learning Test (Dutch version)5,6
A series of 15 unrelated one-syllable words is read over five trials. After each trial the subjects are asked to recall the words they remember. Twenty minutes after the last trial the delayed recall is tested. Scores are number of words recalled on the five immediate trials and the score on the delayed recall trial.
a Location Learning Test7 The test consists of a 5 × 5 matrix with 10 pictures. Subjects are presented this matrix for 30 sec and are subsequently asked to correctly place the pictures on the matrix. Subjects are given five learning trials and a delayed recall trial after 15 min. Scores are total number of displacements after five trials and total number of displacements on the delayed recall trial.
Working Memory
WAIS-III Letter-Number Sequencing8
A series of letter and number combinations, from two to nine letter-number combinations are read by the examiner. Subjects are instructed to repeat each series by first, repeating the numbers in ascending order and then the letters in alphabetical order. Scores are number of correct items.
a WMS-IV Symbol Span9 Subjects are briefly shown a series of abstract symbols on a screen and then asked to select the symbols from an array of symbols, in the same order they were presented on the previous screen. Scores are number of correct items.
Executive functioning (mental flexibility, planning and organization)
D-KEFS TMT,2 condition 4a Color Trails Test,3 condition 2Subjects are asked to connect numbers alternating with letters (TMT condition 4) or to connect yellow numbers alternating with pink numbers (Color Trails Test condition 2). Scores are time to completion in seconds.
Tower of London10
The test consists of two boards with three pegs and three beads with different colors. Subjects are asked to copy different patterns on the examiner’s board in as few moves as possible without breaking the imposed rules. Scores are number of moves to completion.
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Language
Category Fluency11
The test consists of naming animals and occupations for 1 min each. Scores are the number of correct words per category.
Visuoperception
D-KEFS TMT,2 condition 1 Subjects are asked to cross out the number ‘3’ between distracting numbers and letters. The page is placed at the subjects’ midline. Scores are time to completion in seconds.
Bells Test12 In this test subjects are asked to circle all 35 bells embedded within a large number distractors (houses, horses, etc.) The page is placed at the subjects’ midline. Scores are the number of circled bells.
Psychomotor ability
D-KEFS TMT,2 condition 5 Subjects are asked to trace a line connecting a number of circles as fast as possible without missing any of the circles. Scores are time to completion in seconds.
Finger Tapping Test13 Subjects are instructed to tap their index finger on the space bar of the test computer as quickly as possible for 10 sec. Subjects start with a practice trial after which the trial is repeated five times. This procedure is repeated with the nondominant hand. Scores are number of taps per hand.
Intelligence
WAIS-III Matrix Reasoning8 Subjects are shown incomplete matrices or series and are instructed to complete the matrices or series by selecting the correct response option. Score are number of items correct.
a Nonverbal alternative for patients with aphasia.
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1 Dinges DF, Powell JW. Microcomputer analysis of performance on a portable, simple visual RT task during sustained operations. Behav Res Met Ins C. 1985;17:652–5.
2 Delis DC, Kaplan E, Kramer JH. Delis–Kaplan Executive Function System. San Antonio, TX: Psychological Corporation; 2001.
3 D’Elia LF, Satz P, Uchiyama CL, White T. Color Trails Test 1 & 2. Odessa, FL: Psychological Assessment Resources; 1994.
4 Brickenkamp R, Zillmer, E. The d2 Test of Attention. Seattle, WA: Hogrefe & Huber Publishers; 1998.
5 Rey A. L’examen clinique en psychologie. Paris: Presses Universitaires de France; 1964.
6 Schmand B, Houx P, de Koning IM, et al. Normen van psychologische tests voor gebruik in de klinischeneuropsychologie [norms for psychological tests for use in clinical neuropsychology]. Published on the website of the section Neuropsychology of the Dutch Institute of Psychology (Nederlandse Instituut van Psychologen; NIP).
7 Bucks RS, Willison J, Byrne L. Location learning test: Manual. Bury, St Edmunds: Thames Valley Test Company, 2000.
8 Wechsler D. Wechsler Adult Intelligence Scale 3rd edition (WAIS-III): Test manual. New York, NY: Psychological Corporation; 1997.
9 Wechsler D. WMS-IV: Wechsler Memory Scale-Administration and Scoring Manual. New York, NY: Psychological Corporation; 2009.
10 Culbertson CQ, Zillmer EA. Tower of London Drexel University (TOL DX): Technical manual. North Tonawanda, NY: Multi-Health Systems Incorporated (MHS); 2001.
11 Luteijn F, Barelds, DPH. Groningen Intelligence Test 2 (GIT-2): Manual. Amsterdam, The Netherlands: Harcourt Assessment BV; 2004.
12 Gauthier L, Dehaut F, Joanette Y. The Bells test: a quantitative and qualitative test for visual neglect. Int J Clin Neuropsychol 1989;11:49-54.
13 Reitan RM, Wolfson D. The Halstead‐Reitan Neuropsychological Test Battery: theory and clinical interpretation. Tucson, AZ: Neuropsychology Press; 1993.
REFERENCES
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SLEEP APNEA IN STROKE: NEDERLANDSE SAMENVATTINGInleiding
Slaapapneu is een slaapstoornis waarbij er sprake is van het herhaald optreden van
ademstilstanden tijdens de slaap. Deze ademstilstanden worden ook wel apneus genoemd.
Er zijn verschillende vormen van slaapapneu met verschillende oorzaken. De meest
voorkomende vorm is obstructief slaapapneu (OSA), waarbij de apneus worden veroorzaakt
door een afsluiting van de luchtweg. Een andere vorm van slaapapneu is centraal slaapapneu
(CSA). Bij CSA wordt vanuit de hersenen geen prikkel gegeven aan de ademhalingspieren
om adem te halen. Ook is een combinatie van beide vormen mogelijk. De ernst van de
slaapapneu wordt bepaald door het aantal apneus en gedeeltelijke ademstops (hypopneus)
per uur slaap (apneu hypopneu index; AHI), waarbij een onderscheid gemaakt wordt tussen
milde (≥ 5), matige (≥ 15) en ernstige slaapapneu (≥ 30).1 Aangezien OSA verreweg het meest
voorkomt ligt de focus in het proefschrift op deze vorm van slaapapneu. OSA kan gepaard
gaan met slaperigheid overdag, vermoeidheid, vergeetachtigheid, concentratieproblemen
en een sombere of prikkelbare stemming. Daarnaast hebben patiënten met OSA een
verhoogd risico op hart- en vaatziekten en op een beroerte (cerebrovasculair accident;
CVA).2 CVA is een verzamelnaam voor herseninfarcten en hersenbloedingen. Aangezien
OSA het risico op een CVA verhoogt, komt OSA vaker voor bij CVA-patiënten dan in de
algemene bevolking. Meerdere onderzoeken hebben aangetoond dat ongeveer 30-70%
van de CVA-patiënten OSA heeft, terwijl dit in de algemene bevolking geschat wordt
op 3-17%.3,4 Tevens blijkt dat OSA bij CVA-patiënten geassocieerd is met een slechtere
functionele uitkomst, langere opnameduur en een verhoogde kans op mortaliteit.5-8 Het
effect van OSA op andere aspecten van het dagelijks functioneren van CVA-patiënten,
zoals het cognitief functioneren, is nog niet duidelijk. Ondanks dat OSA veel voorkomt bij
CVA-patiënten en een negatieve invloed heeft op het herstel van deze patiënten, blijft OSA
vaak ongediagnosticeerd en onbehandeld.9
De meest gebruikte behandeling voor OSA is continuous positive airway pressure (CPAP).
CPAP verbetert het ademhalingspatroon door via een masker lucht in de neus of de mond
te blazen, waardoor de luchtweg geopend blijft, de apneus verdwijnen en de slaapkwaliteit
verbetert.10 Dit zorgt voor een vermindering van slaperigheid en verbetering van functioneren
overdag in verder gezonde OSA-patiënten.11 Hoewel het onderzoek naar de behandeling met
CPAP bij CVA-patiënten nog schaars is, is de verwachting dat CPAP ook het functioneren van
deze patiënten verbetert. Enkele onderzoeken ondersteunen deze hypothese en vinden dat
CPAP behandeling leidt tot beter neurologisch herstel.12-14 Tot op heden hebben studies echter
nog geen verbetering van het dagelijks functioneren (algemeen dagelijkse levensverrichtingen;
ADL) of cognitief functioneren van CVA-patiënten met OSA kunnen aantonen.15
Doel van het proefschrift
Het doel van de studies opgenomen in dit proefschrift is het verbeteren van de kennis over
de effecten van OSA en de behandeling met CPAP op het herstel van CVA-patiënten tijdens
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hun revalidatie. De specifieke doelen waren 1) het verbeteren van vroege herkenning
van slaapapneu bij CVA-patiënten tijdens hun opname in een revalidatiecentrum, 2) het
onderzoeken van de effecten van OSA op het dagelijks functioneren van CVA-patiënten,
waaronder het cognitief functioneren, tijdens hun opname, en 3) evalueren of behandeling
met CPAP verbetering kan geven op het herstel van CVA-patiënten tijdens hun revalidatie.
Hoofdstuk 1 begint met een algemene inleiding in het onderwerp. Er wordt een korte
achtergrond van slaapapneu en beroerte, en van de relatie tussen beide gegeven. Verder wordt
de relevante literatuur kort besproken en worden de doelen van dit proefschrift geformuleerd.
Resultaten
In voorbereiding op de studie naar het effect van CPAP behandeling op het cognitief
functioneren van CVA-patiënten, zijn de resultaten uit eerdere onderzoeken naar het
effect van CPAP in de algemene bevolking gezamenlijk geanalyseerd in een meta-analyse.
Eerdere reviews suggereerden dat CPAP het cognitief functioneren in verschillende
domeinen, zoals vigilantie (waakzaamheid), aandacht en geheugen, verbetert.16-18 Deze
reviews waren echter kwalitatief van aard, waardoor de grootte van de gevonden effecten
onduidelijk was. In hoofdstuk 2 worden de resultaten van deze meta-analyse van dertien
gerandomiseerde, gecontroleerde onderzoeken (RCT’s) beschreven. De voornaamste
bevinding is dat er een klein, positief effect van CPAP was op de aandachtsfunctie, terwijl
er geen significante verbetering werd gevonden in andere cognitieve domeinen zoals
het geheugen. Meer specifiek werd een verbetering gezien in de verdeelde aandacht,
waarbij taken die meer inspanning vereisten een grotere verbetering gaven. Daarnaast
is in deze geselecteerde groep studies gekeken of er sprake was van een vermindering
van slaperigheid en een verbetering van de stemming. De gevonden verbetering in beide
domeinen kwam overeen met eerder onderzoek.11 Hoewel op basis van onze meta-
analyse slechts minimale verbetering van het cognitief functioneren verwacht kan worden,
is er meer gerandomiseerd onderzoek nodig om de invloed van de ernst van OSA, de
behandelduur en de therapietrouw (compliance) op dit effect vast te stellen.
Hoofdstuk 3 en hoofdstuk 4 zijn gericht op het verbeteren van vroege herkenning van
slaapapneu tijdens de revalidatie van CVA-patiënten. Hoewel slaapapneu veel voorkomt en
negatieve gevolgen heeft voor de revalidatie wordt er in weinig revalidatiecentra standaard
gescreend op deze aandoening. De standaard onderzoeksmethode voor het vaststellen van
slaapapneu is een uitgebreid slaaponderzoek middels poly(somno)grafie.19 In revalidatiecentra
is dit vaak niet haalbaar vanwege beperkte beschikbaarheid en hoge kosten van dit onderzoek.
In dit kader wordt in dit proefschrift een getrapte diagnostische benadering voorgesteld,
waarbij patiënten slechts het onderzoek krijgen dat nodig is. Voor deze benadering zijn
betrouwbare en makkelijk te gebruiken screeningsinstrumenten nodig. Het doel van deze
hoofdstukken was om de predictieve validiteit (voorspellende waarde) en bruikbaarheid van
twee verschillende screeningsmethoden voor het opsporen van slaapapneu te onderzoeken.
In hoofdstuk 3 worden de resultaten van een onderzoek naar de validiteit van
nachtelijke pulse-oximetrie als screeningsinstrument voor slaapapneu weergegeven.
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Pulse-oximetrie is een methode die door middel van een sensor op de vinger het aantal
zuurstofdalingen per uur berekent (oxygen desaturation index = ODI). Deze methode
wordt veel gebruikt in algemene slaapklinieken, maar is nog niet onderzocht in de
CVA populatie. In deze studie kregen 56 CVA-patiënten zowel een polygrafie als pulse-
oximetrie onderzoek. Bij 46% van de patiënten werd de diagnose slaapapneu vastgesteld
(AHI ≥ 15). De meerderheid van de patiënten met slaapapneu was man, ouder en had
een hogere BMI dan de patiënten zonder slaapapneu. Met behulp van pulse-oximetrie
kon met 93% diagnostische nauwkeurigheid onderscheid gemaakt worden tussen de
patiënten met en zonder slaapapneu. De sensitiviteit en specificiteit waren respectievelijk
77% en 100%. Sensitiviteit is een maat voor de gevoeligheid van de test om patiënten
met slaapapneu op te sporen, terwijl specificiteit aangeeft in hoeverre die gestelde
diagnose terecht wordt gesteld. Gezien de hoge prevalentie van slaapapneu in deze groep
CVA-patiënten, gaf een positieve uitkomst een 100% waarschijnlijkheid op slaapapneu
(positief voorspellende waarde), terwijl een negatief resultaat de waarschijnlijkheid op
slaapapneu met 83% verminderde (negatief voorspellende waarde). Er werden geen
klinische variabelen gevonden die een verdere verbetering gaven van de voorspellende
waarde van pulse-oximetrie. Op basis van de hoge voorspellende waarde van pulse-
oximetrie wordt geconcludeerd dat het een goede screeningsmethode is voor slaapapneu
in CVA-patiënten tijdens de revalidatie.
Als vervolg op de bovenstaande studie, wordt in hoofdstuk 4 onderzocht of een nog
simpelere screeningsmethode, een vragenlijst bestaande uit zelf gerapporteerde klachten,
sociodemografische gegevens en klinische variabelen, kan dienen als een bruikbare eerste
stap in de getrapte diagnostische benadering. In deze studie werd bij 438 CVA-patiënten
de vragenlijst afgenomen. Daarnaast werd er met behulp van pulse-oximetrie vastgesteld
welke patiënten een hoge waarschijnlijkheid van slaapapneu hadden (ODI≥15). Er werd
gevonden dat de volgende variabelen uit de vragenlijst voorspellend waren en deze
werden opgenomen in het uiteindelijke predictiemodel: geslacht, leeftijd, BMI, en zelf
gerapporteerde symptomen van ademstops tijdens de slaap en overdag in slaap vallen.
Het predictiemodel had een diagnostische nauwkeurigheid van 76% met een sensitiviteit
en specificiteit van respectievelijk 72% en 69%. De bijbehorende positieve voorspellende
waarde was 50%, terwijl de negatieve voorspellende waarde 83% was. Geconcludeerd
wordt dat de vragen uit het predictiemodel goed gebruikt kunnen worden als eerste
screeningsmethode, omdat deze methode het aantal CVA-patiënten, dat uitgebreidere
slaapapneu screening nodig heeft, significant vermindert.
Zoals eerder beschreven, komt OSA veel voor bij CVA-patiënten en wordt het
geassocieerd met slechtere functionele uitkomsten en verhoogde mortaliteit.3,5-8 Er is echter
nog weinig onderzoek gedaan naar het effect van OSA op het cognitief functioneren van
CVA-patiënten. Daarnaast zijn er een aantal recente studies die vinden dat behandeling
met CPAP het functionele herstel van CVA-patiënten verbetert, terwijl het effect op
cognitief herstel nog nauwelijks onderzocht is. In hoofdstuk 5 worden de rationale, de
opzet en onderzoeksmethoden van het studieprotocol “Treatment of Obstructive sleep
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apnea on Rehabilitation Outcome in Stroke” (TOROS) beschreven. Het doel van de TOROS
studie was om in een RCT de effectiviteit van CPAP behandeling op zowel cognitieve als
functionele uitkomsten van CVA-patiënten met OSA te evalueren. Aanvullend op de RCT,
bevatte het protocol een patiënt-controleonderzoek dat als doel had om de relatie tussen
OSA en cognitieve en functionele status te onderzoeken.
De resultaten van dit patiënt-controleonderzoek worden gepresenteerd in hoofdstuk
6. Aan deze studie deden 80 CVA-patiënten met OSA (AHI ≥ 15) en 67 CVA-patiënten
zonder OSA mee (AHI < 5). Deze patiënten werden vergeleken op cognitieve en
functionele status tijdens de beginfase van de opname in revalidatiecentrum Heliomare. De
belangrijkste bevinding was dat patiënten met OSA significant meer beperkingen hadden
in zowel de cognitieve als de functionele status. Meer specifiek werden er in de groep
OSA patiënten meer beperkingen gevonden in de domeinen van aandacht, executief
functioneren (o.a. planning, structuur aanbrengen, mentale flexibiliteit), visuoperceptie
(visuele herkenning), (psycho)motorische snelheid en intelligentie, en was er sprake van
een lagere neurologische status en een hogere mate van functionele afhankelijkheid.
Daarnaast werd gevonden dat OSA patiënten gemiddeld 11 dagen langer opgenomen
waren in Heliomare dan patiënten zonder slaapapneu. Er werden geen verschillen
gevonden tussen patiënten met en zonder OSA op zelf gerapporteerde slaperigheid,
vermoeidheid, slaapkwaliteit en symptomen van angst- en depressie. Op basis van deze
resultaten wordt geconcludeerd dat OSA geassocieerd is met verminderd functioneren bij
CVA-patiënten tijdens hun revalidatietraject.
Hoofdstuk 7 beschrijft de RCT en richt zich op het effect van CPAP behandeling op de
uitkomst van revalidatie van CVA-patiënten. In deze studie kregen zestien patiënten met
OSA (AHI ≥ 15) vier weken standaard revalidatiebehandeling, terwijl 20 OSA patiënten
tijdens deze vier weken daarnaast CPAP behandeling kregen. Na deze vier weken lieten
de OSA patiënten die CPAP behandeling hadden gekregen meer verbetering zien in
de cognitieve domeinen van aandacht en executief functioneren dan de groep die de
standaard revalidatiebehandeling had ontvangen. De gevonden behandeleffecten waren
medium tot groot, wat impliceert dat deze niet alleen statistisch significant waren, maar
ook klinisch relevant. Er werd geen CPAP geassocieerde verbetering van functionele
status, slaperigheid, vermoeidheid, slaapkwaliteit of stemming gevonden. Hoewel het
effect op functionele status niet significant was, werd er wel een trend gezien in de
verwachte richting. De CPAP - compliance was met gemiddeld 2,5 uur per nacht laag.
Het is mogelijk dat deze lage compliance geleid heeft tot een onderschatting van het
werkelijke effect van CPAP. Geconcludeerd wordt dat het positieve effect van CPAP op het
cognitieve herstel erop wijst dat CPAP behandeling een toegevoegde waarde heeft in de
revalidatiebehandeling van CVA-patiënten.
Conclusies
Het laatste hoofdstuk van dit proefschrift (hoofdstuk 8) bevat een samenvatting en
algemene discussie. In dit hoofdstuk worden tevens de methodologische aspecten van het
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proefschrift besproken en wordt ingegaan op de klinische relevantie van de bevindingen.
Verder worden er aanbevelingen voor toekomstig onderzoek gepresenteerd.
Het eerste doel van het proefschrift was het verbeteren van de vroege herkenning van
slaapapneu bij CVA-patiënten tijdens hun opname in een revalidatiecentrum. De bevindingen
uit het eerste deel van het proefschrift hebben twee screeningsinstrumenten opgeleverd die
ingezet kunnen worden in een getrapte diagnostische benadering. Deze benadering heeft
een drietal voordelen voor het gebruik in de klinische praktijk. Ten eerste is gevonden dat op
basis van een beperkt aantal vragen een eerste selectie gemaakt kan worden van patiënten
die verdere slaapapneu onderzoek nodig hebben. Ten tweede geeft het op deze vragen
gebaseerde predictiemodel artsen de mogelijkheid om een afkappunt met bijbehorende
sensitiviteit en specificiteit te kiezen dat zij acceptabel vinden. Tot slot toont ons onderzoek
aan dat pulse-oximetrie, de tweede diagnostische stap in de getrapte benadering, een zeer
betrouwbare voorspeller is voor slaapapneu in de CVA populatie, die beter geaccepteerd
wordt door patiënten en goedkoper is dan poly(somno)grafie, de gouden standaard.
Het doel van de TOROS studie, beschreven in het tweede deel van het proefschrift, was
tweeledig. Deze studie richtte zich zowel op de relatie tussen OSA op de cognitieve en
functionele status van CVA-patiënten als op het effect van CPAP behandeling op revalidatie-
uitkomsten van CVA-patiënten met OSA. Samenvattend toont deze studie aan dat OSA
geassocieerd is met aanzienlijke beperkingen in het dagelijks functioneren van CVA-patiënten
op zowel cognitief als functioneel vlak. Daarnaast wordt gevonden dat behandeling met
CPAP een positief effect heeft op het cognitief functioneren van CVA-patiënten, terwijl het
geen verbetering geeft in de overige domeinen van dagelijks functioneren.
De klinische implicatie van de bovenstaande resultaten is dat men binnen de
revalidatie bedacht moet zijn op OSA bij CVA-patiënten, omdat OSA veel voorkomt en
een belemmerende rol speelt in het herstel. Daarnaast kan adequate CPAP behandeling
bijdragen aan het herstel van deze patiëntengroep. De lage compliance met CPAP bij CVA-
patiënten blijft echter een punt van zorg. Er wordt daarom aanbevolen om toekomstig
onderzoek enerzijds te richten op het ontwikkelen van methoden om de CPAP compliance
te verbeteren en anderzijds de bruikbaarheid en effectiviteit van alternatieve, minder
belastende behandelvormen te evalueren.
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DANKWOORDHet is zo ver, het einde van mijn promotie is echt in zicht. Nu komt het leukste, maar
misschien wel ook het moeilijkste stuk: het dankwoord. Het is bijna niet mogelijk om
in een aantal pagina’s te vangen hoe blij ik ben met alle steun en bijdragen aan de
totstandkoming van dit proefschrift, maar ik ga mijn best doen!
Allereerst wil ik alle patiënten die deelgenomen hebben aan dit onderzoek bedanken.
Ondanks dat u vaak in een onzekere revalidatieperiode verkeerde was u bereid om alle
extra slaaponderzoeken, tests en vragenlijsten te ondergaan. Zonder u was dit proefschrift
er niet geweest.
Mijn promotoren prof. dr. B. Schmand en prof. dr. C. van Bennekom, en copromotor dr.
W. Hofman. Beste Ben, aan het einde van mijn stage neuropsychologie in het AMC bracht
jij de vacature voor deze promotieplek onder mijn aandacht. Zonder jouw stimulans had
ik nooit gesolliciteerd en was dit proefschrift er dus nooit gekomen. Jouw kennis van de
neuropsychologie en wetenschappelijke methoden, jouw snelle reacties en altijd kritische
blik hebben mij enorm geholpen bij het schrijven van dit proefschrift. Daarnaast hebben
jouw vertrouwen en positieve houding ervoor gezorgd dat ik na elke tegenslag de moed
erin bleef houden. Ook tijdens mijn klinische werkzaamheden heb ik nog steeds veel
profijt van alle kennis en praktische adviezen die jij mij hebt meegegeven. Dank daarvoor!
Beste Coen, tijdens het opzetten van het onderzoek was het heel prettig dat jij zowel
als revalidatiearts als onderzoeker meedacht. Dit maakte dat er steeds een balans gezocht
werd tussen de wetenschappelijke waarde van het onderzoek en de haalbaarheid in
de klinische praktijk. Hierdoor is het (grotendeels) gelukt om het geplande onderzoek
met succes en binnen de tijd te voltooien. Als manager heb jij mij heel vrij gelaten in
het uitvoeren van het onderzoek, maar kon ik altijd met vragen bij je aankloppen en in
moeilijke momenten (denk aan de METC vergadering) op jouw steun rekenen.
Beste Winni, jij was minder direct betrokken bij het project, maar dat maakt jouw
bijdrage aan dit proefschrift zeker niet minder belangrijk. Jouw frisse blik en kennis
van slaapstoornissen en slaaponderzoek hebben mij als neuropsycholoog ontzettend
geholpen. Het feit dat een aantal artikelen in mooie “slaapbladen” is verschenen, is wat
mij betreft hiervan het bewijs. Daarnaast wist jij ook altijd nog een aantal foutjes uit mijn
stukken te halen, hoe vaak ik deze ook rondstuurde.
Overige leden van de promotiecommissie, dr. J.G. van den Aardweg, dr. J.M.
Karemaker, prof. dr. R.P.C. Kessels, prof. dr. J.M.J. Murre, prof. dr. G.M. Ribbers en prof.
dr. Y.W.B.E.M Roos, heel veel dank voor het beoordelen van mijn proefschrift. Beste Joost,
daarnaast heel veel dank voor jouw bijdrage aan het onderzoek.
Het Respicare team was de draaiende motor van het onderzoek. Tijs, zonder jou was dit
avontuur nooit gestart. Ik wil jou bedanken voor het motiveren van zoveel patiënten om
deel te nemen aan het onderzoek. Ik bewonder jouw creativiteit en vooruitziende blik. Er
zijn weinig mensen die het lukt om zoveel van hun ideeën werkelijkheid te maken. Brita,
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Irene en Mario, dank voor het regelen van alle praktische zaken, zodat ik tijd had om mij
met het onderzoek bezig te houden. Jullie inspanning en hart voor de patiënten maakte
dat ik nooit uit het oog verloor waarvoor ik dit onderzoek deed.
Erny, wat heb ik veel van jou mogen leren in de afgelopen jaren! Op het gebied van
onderzoek heb jij de neiging jezelf te onderschatten, maar jouw snelle werkwijze, en
innovatieve en praktische ideeën hebben ervoor gezorgd dat mijn proefschrift ondanks
een aantal tegenslagen toch nog een mooi boekwerk is geworden. Daarnaast wil ik je
enorm bedanken voor alle kansen die jij mij hebt gegeven om mij te ontwikkelen als
psycholoog en voor jouw begeleiding daarbij.
Wytske, in het proefschrift van Anne Rienstra wordt jij haar “meest welbespraakte
collega ever” genoemd. Ik sluit mij daar volledig bij aan, maar wil daaraan meest
weloverwogen toevoegen. De tijd die jij neemt om dingen precies uit te zoeken en het
belang dat jij hecht aan nuances maakt jou een hele fijne sparringpartner. Het samen
schrijven van het meta-analyse artikel was voor mij daarom ook een feest. Ik hoop dat wij
dit in de toekomst nog een keer kunnen doen.
Tineke, Gerda, Indira, Marianne, Andrea, Elisah, Floortje, Dennis en Simone, jullie
hebben voor mijn onderzoek honderden patiënten getest, enorm veel dank daarvoor.
Wilma, Annemiek, Marion en alle andere collega’s van de planning heel veel dank
voor de administratieve en organisatorische ondersteuning. Ik heb het jullie met mijn
dubbelfunctie niet makkelijk gemaakt. Collega’s van afdelingen 1a, 3a en 3b bedankt
voor de fijne samenwerking.
Alle collega’s van R&D, Judith, Janneke, Han, Richard, Linda, Daphne, Trienke, Maaike, Timo
en Elmar, wat was het fijn om met jullie de mooiste ruimte van Heliomare te mogen delen.
Hoewel onze onderzoeken vaak weinig raakvlakken hadden, was het erg prettig dat er altijd
wel iemand was om de tegenslagen en hoogtepunten mee te delen. Ik ga jullie gezelligheid
en de prachtige strandwandelingen zeker missen. Richard, heel erg veel dank voor het
ontwerpen van de voorkant van mijn proefschrift. Jij hebt van een simpel logo een prachtige
kaft gemaakt!
Psychologen van Heliomare, wat was de vakgroep een welkome afwisseling van het
onderzoek! Dank dat ik mocht delen in jullie kennis en gezelligheid. Mechteld en Marijke,
ik heb met heel veel plezier samen met jullie groepsbehandelingen gedraaid. Het was voor
mij als beginneling heel prettig om zulke fijne en vakkundige psychologen naast mij te
hebben om van te leren. Collega’s van de NAH-poli, bedankt voor de leuke samenwerking.
Collega’s van de UvA, ik was maar sporadisch aanwezig, maar toch was er altijd
een plekje voor mij. Renate, jammer genoeg is het nooit gelukt om onze projecten te
combineren, maar gelukkig kunnen we nog wel gezellig buurten.
Collega’s van het Radboud, in het bijzonder Roy, Nelleke, Sandra, Ilja en Marian, ik
ben ontzettend blij dat ik de overstap naar Nijmegen heb gemaakt. De wens om de GZ-
opleiding met onderzoek te combineren is hiermee uitgekomen. Jullie vormen en klein
maar fijn team, waar ik mij vanaf de eerste dag meer dan welkom heb gevoeld. Ik verheug
mij erop om de komende jaren met jullie samen te werken en van jullie te leren.
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Neuropsychologen van het AMC, inmiddels lopen we er allemaal niet meer rond, maar
gelukkig zien we elkaar nog steeds; van congressen, etentjes tot fietstochten. Harriet,
Meike en Mirjam, jullie enthousiasme voor het vak is aanstekelijk. Anne, jij gaf mij het
goede voorbeeld dat promotieonderzoek en klinisch werk te combineren zijn. Ik hoop
dat het lukt om snel weer een reünie te plannen. Hyke en Nina, wat ben ik blij met jullie
vriendschap. Ik vind het heerlijk dat wij op een avond werkperikelen kunnen bespreken
om vervolgens naadloos over te gaan op verhalen over vakanties, familie, liefdes etc.
Natuurlijk was er buiten het werk ook genoeg gezelligheid en afleiding. Lieve vriendinnen,
ik heb het geluk dat ik meeste van jullie al meer dan de helft van mijn leven ken. Dank
voor jullie trouwe en onvoorwaardelijke vriendschap en steun!
Lieve meisjes van het Barlaeus, Kay, Minyou, Gijsje, Fritzi, Joanne en Anke, na de
middelbare school zijn we allemaal onze eigen weg gegaan, maar gelukkig hebben de
vele borrels, heerlijke etentjes en fijne vakanties ervoor gezorgd dat we in elkaars leven
zijn gebleven. Kay en Joanne, ik kan mij de afgelopen jaren niet voorstellen zonder onze
koffiedates; zó fijn dat wij nooit uitgepraat raken.
Lieve Maaike en Menje, wat bijzonder dat we sinds onze tijd bij circus Elleboog altijd
contact hebben gehouden. Maai, ik bewonder jouw kracht en doorzettingsvermogen.
Hoewel de laatste jaren voor jou niet makkelijk zijn geweest, kon ik toch altijd voor een
kopje thee bij jou terecht. Men, recht voor zijn raap en altijd in voor iets nieuws. Zullen we
snel weer naar een voorstelling gaan?
Lieve buurmeisjes, Eva, Diana en Suus, beter een goede buur dan een verre vriend is op
jullie helemaal van toepassing. We zijn inmiddels allang geen “echte” buren meer, maar des
te betere vriendinnen. Eef, ik ben ontzettend trots op hoe jij Issabel opvoedt tot een heerlijk
kind en blij dat wij elkaar weer vaker zien. Di, ik ken niemand die zo enthousiast kan worden
van kleine dingen zoals muziek en lekker eten. Daarnaast ben je altijd oprecht geïnteresseerd,
dat maakt je heel bijzonder. Suus, met niemand heb ik meer lief en leed gedeeld dan met
jou. Hoewel we heel anders zijn, ik de rationele en jij het gevoelsmens, zitten we zó vaak op
één lijn. Je bent er altijd voor mij en dat maakt dat jouw vriendschap mij heel dierbaar is. Ik
ben daarom ook vereerd dat jij mijn paranimf wil zijn! Lieve San, officieel geen buurmeisje,
maar stiekem heb jij je er in de loop der jaren wel tussen gewerkt. Dromerigheid en scherpe
humor lijken elkaars tegenpolen, maar jij weet ze perfect te combineren.
En dan is het tijd voor mijn familie; klein maar fijn! Lieve tantes Kastein, al zijn jullie als
zussen alle drie heel verschillend, niets gezelliger dan jullie samen te zien. Lieve Geerten,
ik zal jouw 60e verjaardag nooit vergeten. Hopelijk lukt het om snel weer met de hele
familie Gerritsen te gaan skiën. Dear aunt Susan, Jenni and Michael, the distance makes
it impossible to see each other as often as we would like, but I’m grateful for the time we
have spent together over the last couple of years.
Lieve Wildemannen, het was voor mij even wennen, zo’n groot gezin waar niemand
op zijn mondje gevallen is. Desondanks heb ik mij vanaf het eerste moment thuis gevoeld.
Jullie zijn voor mij het voorbeeld dat familie er altijd voor elkaar is, door dik en dun.
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Lieve Ineke, ik kan mij geen leukere “stiefmoeder” wensen. Met jou erbij is het altijd
gezellig. Daarnaast heb ik er met jou twee broertjes bijgekregen. Lieve Fabian en Vasco,
zullen we de Timsons overhalen om snel weer een reisje met ons te maken?
Lieve Ilana, mijn kleine zusje die stiekem al jaren boven mij uit torent, ik ben heel blij
dat jij vandaag naast mij staat! Zo gezellig dat jij weer bij mij in de buurt woont. Ik verheug
me nu al op de avondjes in jouw nieuwe huisje. Vito, thank you for taking such good care
of my little sister for the last couple of years. I’m happy that you finally get the chance to
spend more time together.
Lieve papa, geen trotsere vader dan jij. Natuurlijk wil ik jou bedanken voor jouw
bijdrage aan dit proefschrift; van vraagbaak tot editor. Maar veel belangrijker is dat jij
mij altijd gesteund hebt in alles wat ik wilde doen, ook als dit betekende dat ik alleen op
avontuur ging en jij je stiekem veel zorgen maakte. Lieve mama, ik hoef maar te bellen en
jij staat op de stoep. Of het nu om praktische hulp gaat of ik een hart onder de riem nodig
heb, je bent er altijd. Ik kan mij echt geen betere ouders wensen. Dank jullie voor alles!
Lieve Maarten, wat heb ik een geluk met jou! Dank voor al je liefde, vertrouwen en
steun, ook toen ik besloot ons leven nog drukker te maken. Misschien cliché, maar ik heb
heel veel zin in de toekomst samen met jou.
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LIST OF A
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LIST OF ABBREVIATIONSADL Activities of daily living
AHI Apnea-hypopnea index
ANCOVA Analysis of covariance
BMI Body mass index
BQ Berlin questionnaire
CI Confidence interval
CIS Checklist of individual strength
CNS Canadian neurological scale
CPAP Continuous positive airway pressure
CSA Central sleep apnea
ECG Electrocardiogram
EEG Electroencephalogram
EF Executive functioning
EMG Electromyogram
EOG Electrooculogram
ES Effect size
ESS Epworth sleepiness scale
ITT Intention-to-treat
HADS Hospital anxiety and depression scale
LACS Lacunar stroke
LR Likelihood ratio
MANCOVA Multivariate analysis of covariance
MANOVA Multivariate analysis of variance
MeSH Medical subject headings
MMSE Mini mental state examination
MSLT Multiple sleep latency test
MWT Maintenance of wakefulness test
NIHSS National institutes of health stroke scale
NPV Negative predictive value
ODI Oxygen desaturation index
OSA Obstructive sleep apnea
PACS Partial anterior circulation stroke
PASAT Paced auditory serial addition task
PG Polygraphy
PPV Positive predictive value
PSG Polysomnography
POCS Posterior circulation stroke
RCT Randomized controlled trial
RDI Respiratory disturbance index
ROC Receiver-operator characteristic
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SAS Sleep apnea syndrome
SD Standard deviation
SSS Stanford sleepiness scale
SQS Sleep quality scale
TACS Total anterior circulation stroke
TAU Treatment as usual
TMT Trail making test
TOROS Treatment of OSA and rehabilitation outcome in stroke
USER Utrecht scale for evaluation of rehabilitation
WAIS Wechsler adult intelligence scale
WMS Wechsler memory scale
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PRESEN
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LIST OF PUBLICATIONS & PRESENTATIONSInternational peer-reviewed publications
Aaronson JA, Hofman WH, van Bennekom CAM, van Bezeij T, van den Aardweg JG, Groet
E, Kylstra WA, Schmand BA. Effects of continuous positive airway pressure on cognitive
and functional outcome of stroke patients with obstructive sleep apnea: a randomized
controlled trial. Accepted in Journal of Clinical Sleep Medicine.Authors' contributions: All authors contributed to the study design, interpretation of the results and preparation of the manuscript. JA and TvB were involved in the data collection. The data analyses were performed by JA under supervision of BS, WH and CvB.
Aaronson JA, van Bennekom CAM, Hofman WH, van Bezeij T, van den Aardweg JG,
Groet E, Kylstra WA, Schmand BA. Obstructive sleep apnea is related to impaired cognitive
and functional status after stroke. Sleep 2015; 38:1431-1437. Authors' contributions: All authors contributed to the study design, interpretation of the results and preparation of the manuscript. JA and TvB were involved in the data collection. The data analyses were performed by JA under supervision of BS, CvB and WH.
Aaronson JA, van Bennekom CAM, Hofman WH, van Bezeij T, van den Aardweg JG,
Groet E, Kylstra WA, Schmand BA. The effect of obstructive sleep apnea and treatment
with continuous positive airway pressure on stroke rehabilitation: rationale, design and
methods of the TOROS study. BMC Neurology 2014; 14:36.Authors' contributions: All authors contributed to the study design, and preparation of the manuscript. JA coordinated the study under supervision of BS, CvB and WH.
Aaronson JA, Nachtegaal J, Groet E, van Bezeij T, Hofman WH, van den Aardweg JG, van
Bennekom CAM. Can a prediction model combining self-reported symptoms, socio-demographic
and clinical features serve as a reliable first screening method for sleep apnea syndrome in stroke
patients? Archives of Physical Medicine and Rehabilitation 2014; 95:747-752.Authors' contributions: All authors contributed to the study design, interpretation of the results and preparation of the manuscript. JA, JN and TvB were involved in the data collection. The data analyses were performed by JN, JA and EG under supervision of CvB.
Kylstra WA*, Aaronson JA*, Hofman WF, Schmand BA. Neuropsychological functioning
after CPAP treatment in obstructive sleep apnea: a meta-analysis. Sleep Medicine Reviews
2013; 17:341-347.Authors' contributions: All authors contributed to the study design, interpretation of the results and preparation of the manuscript. WK and JA were involved in the data collection. The data analyses were performed by WK and JA under supervision of BS.
* WK and JA contributed equally to this manuscript.
Aaronson JA, van Bezeij T, van den Aardweg JG, van Bennekom CAM, Hofman WF.
Diagnostic accuracy of nocturnal oximetry for detection of sleep apnea syndrome in stroke
rehabilitation. Stroke 2012; 43:2491-2493.Authors' contributions: All authors contributed to the study design, interpretation of the results and preparation of the manuscript. TvB was involved in the data collection. The data analyses were performed by JA and TvB under supervision of WH and CvB.
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Oral presentations
Van Bezeij T, Aaronson JA. Obstructive sleep apnea in stroke: effects on cognitive and
functional status. European Stroke Conference. Vienna, Austria, May 2015.
Aaronson JA, WF Hofman, van Bennekom CAM, Schmand BA. Sleep apnea in stroke:
effects on cognition and rehabilitation. Joint meeting of the British Neuropsychological
Society and Dutch Neuropsychological Society (NVN), London, England, March 2015.
Aaronson JA, van Bennekom CAM, Hofman WH, van Bezeij T, Groet E, van den Aardweg
JG, Kylstra WA, Schmand BA. The impact of obstructive sleep apnea on cognitive and
functional status in stroke patients. Clinical rehabilitation 2015; 29:306-311, abstracts of
the Dutch Congress of Rehabilitation Medicine (DCRM) meeting, Rotterdam, Netherlands,
November 2014.
Aaronson JA. Sleep disorders after stroke. Workshop ‘Sleep in rehabilitation medicine’.
Rijnlands Rehabilitation Center, Leiden, Netherlands, February 2014.
Aaronson JA. Sleep apnea and other sleep disorders in the rehabilitation. Masterclass
‘Treatment of fatigue in rehabilitation setting’. Reade Amsterdam, Netherlands, September
2013.
Aaronson JA. Sleep apnea and fatigue in stroke patients. Symposium ‘Het venijn zit in de
straat IV’. Ede, Netherlands, April 2013.
Aaronson JA. Sleep apnea in patients with stroke – prevalence, consequences and
treatment. Hersenletselcongres, Axon leertrajecten and Hersenstichting Nederland. Ede,
Netherlands, November 2011.
Poster presentations
Aaronson JA, WF Hofman, van Bennekom CAM, van Bezeij T, van den Aardweg JG,
Groet E, Kylstra WA, Schmand BA. The cognitive and functional status of stroke patients
are negatively affected by obstructive sleep apnoea. Congress of the European Sleep
Research Society. Tallinn, Estonia, September 2014.
Aaronson JA, Groet E, van Bennekom CAM, WF Hofman, van Bezeij T, van den Aardweg JG,
Kylstra WA, Schmand BA. Obstructive sleep apnea affects functional and cognitive status of
stroke patients. Neuropsychological Rehabilitation SIG Conference. Limasol, Cyprus, July 2014.
Aaronson JA, van Bennekom CAM, WF Hofman, van Bezeij T, van den Aardweg JG, Groet
E, Kylstra WA, Schmand BA. Obstructive sleep apnea affects functional and cognitive
status after stroke. World Congress of Neurorehabilitation. Istanbul, Turkey, April 2014.
Aaronson JA, van Bennekom CAM, WF Hofman, van Bezeij T, van den Aardweg JG, Groet
E, Kylstra WA, Schmand BA. Obstructive sleep apnea affects functional and cognitive status
after stroke. IBIA World Congress for Brain Injury. San Francisco, United States, March 2014.
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Aaronson JA, Nachtegaal J, Groet E, van Bezeij T, van Bennekom CAM. Is a questionnaire
a useful method for sleep apnea in stroke? Sleep and Breathing conference ERS & ESRS.
Berlin, Germany, April 2013.
Aaronson JA, Nachtegaal J, Groet E, van Bezeij T, van Bennekom CAM. Is a questionnaire
a useful method for sleep apnea in stroke? Clinical rehabilitation 2014; 28:397-402,
proceedings of the VRA. Noordwijkerhout, Netherlands, October 2013.
Aaronson JA, Nachtegaal J, Groet E, van Bezeij T, van Bennekom CAM. The diagnostic
value of self-reported symptoms for the detection of sleep apnea syndrome in stroke
patients. ERS annual congress. Vienna, Austria, September 2012.
Kylstra WA, Aaronson JA, Hofman WF, Schmand BA. Neuropsychological functioning
after CAP in OSA: a meta-analysis. ERS annual congress. Vienna, Austria, September 2012.
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CURRICULUM VITAEJustine Aaronson was born on November 1st, 1984 in
Amsterdam, the Netherlands. She graduated from high school
in 2003 at the Barlaeus Gymnasium in Amsterdam. After a
year of traveling and learning Spanish, she started studying
Psychology at the University of Amsterdam, specializing in
Clinical Neuropsychology. In 2007, she went abroad for six
months to study at the Universidad de Autonóma in Madrid as
part of the Erasmus exchange programme. She obtained her
bachelor’s degree in 2008. In 2009, she had a clinical internship
at the Department of Neurology at the Academic Medical
Center in Amsterdam. During this period she also conducted research for her master
thesis. She graduated cum laude in 2010. In the same year, she started her PhD project at
Heliomare Research and Development in collaboration with the Department of Brain and
Cognition of the University of Amsterdam, under supervision of prof. dr. B.A. Schmand,
prof. dr. C.A.M. van Bennekom and dr. W.F. Hofman. In addition to her PhD project she
worked as a neuropsychologist at the outpatient clinic for patients with acquired brain
injury at Heliomare Rehabilitation Center. As of January 2015, she works as a psychologist
at the Department of Medical Psychology of the Radboud University Medical Center in
Nijmegen, where she started her clinical training as general health care psychologist.
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