Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive cessation or reduction in airflow during sleep. Stroke patients have a higher risk of OSA, which can worsen their cognitive and functional disabilities, prolong their hospitalization, and increase their mortality rates.
Methods
We conducted a comprehensive literature search in the databases of PubMed, CINAHL, Embase, PsycINFO, Cochrane Library, and CNKI, using a combination of keywords and MeSH words in both English and Chinese. Studies published up to March 1, 2022, which reported the development and/or validation of clinical prediction models for OSA diagnosis in stroke patients.
Results
We identified 11 studies that met our inclusion criteria. Most of the studies used logistic regression models and machine learning approaches to predict the incidence of OSA in stroke patients. The most frequently selected predictors included body mass index, sex, neck circumference, snoring, and blood pressure. However, the predictive performance of these models ranged from poor to moderate, with the area under the receiver operating characteristic curve varying from 0.55 to 0.82. All the studies have a high overall risk of bias, mainly due to the small sample size and lack of external validation.
Conclusion
Although clinical prediction models have shown the potential for diagnosing OSA in stroke patients, their limited accuracy and high risk of bias restrict their implications. Future studies should focus on developing advanced algorithms that incorporate more predictors from larger and representative samples and externally validating their performance to enhance their clinical applicability and accuracy.
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Abkürzungen
OSA
Obstructive sleep apnea
PSG
Polysomnography
BQ
Berlin Questionnaire
PRISMA
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
HSAT
Home sleep apnea test
CHARMS
Critical appraisal and data extraction for systematic reviews of prediction modeling studies
AHI
Apnea hypopnea index
BMI
Body mass index
NIHSS
National Institutes of Health Stroke Scale
ESS
Epworth Sleepiness Scale
AUC
Area under the curve
Background
Obstructive sleep apnea (OSA) is the most common sleep disorder, characterized by recurrent interruptions in breathing during sleep. Individuals with OSA often present clinical symptoms such as sleepiness, fatigue, and headache [1]. The incidence of OSA in stroke patients increased from 61% in 2011 to 75% in 2019 [2‐4], a rate significantly higher than the 35% found in the general population [5]. Previous studies found that OSA was associated with prolonged hospital stay, increased recurrence of stroke, and elevated mortality rates among stroke patients [6‐9]. Both the American Heart Association and the American Stroke Association recommend that the diagnosis and treatment of OSA should be part of secondary prevention programs for stroke [10]. Therefore, it is important to ensure that patients with OSA receive timely and effective diagnosis and treatment.
The polysomnography (PSG), conducted in a sleep laboratory by a trained physician, is widely recognized as the gold standard for OSA diagnosis [11]. Patients experiencing an average of at least 15 apnea events per hour are typically diagnosed with OSA [12]. However, due to high costs and significant manpower required for routine PSG screenings in clinical settings, the actual incidence of OSA is seriously underreported [13, 14]. A cross-sectional survey in the USA showed that only 5% of stroke patients took PSG examinations for OSA diagnosis [13]. Home sleep apnea testing (HSAT) is also recommended as an alternative diagnostic method, although it has slightly lower sensitivity than PSG [12]. Therefore, PSG is necessary for OSA diagnosis, particularly in patients who have negative HSAT results but present clinical symptoms of OSA [15]. Hence, studies have been conducted to develop convenient and accurate prediction models based on demographic and clinical characteristics for early identification of high-risk OSA [16].
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Numerous screening tools for identifying the risk of OSA in stroke patients have been developed and validated, including the Berlin Questionnaire (BQ), Epworth Sleepiness Scale, four‐variable screening tool, and Sleep Apnea Clinical Score [17]. In this study, we conducted a systematic review of the performance of these prediction models and evaluated the feasibility of adopting these models for predicting OSA risk in stroke patients.
Methods
This review was conducted following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [18].
The inclusion criteria were as follows: (1) studies involving adults aged 18 years or above who were admitted for stroke, (2) studies focusing on prediction models for the early diagnosis of OSA, (3) studies on the development of a new prediction model for incident OSA with internal and/or external validations, and (4) studies that adopted the PSG or HSAT as the gold standard for OSA diagnosis for model internal and/or external validations. The studies were limited to those published in English and Chinese. There was no time restriction for the literature search. Secondary sources such as reviews or meta-analyses were excluded. No other exclusion criteria were applied in this review.
Search strategy
We conducted the literature search on March 1, 2022, in the English database CINAHL, Embase, PsycINFO, and PubMed, as well as in the Chinese literature database CNKI.
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Only articles published in English and Chinese were included. In addition, PhD dissertations and related articles were searched by using the Google Scholar. The reference lists of all selected studies were manually searched for additional literature. The MeSH terms and keywords used in the electronic search were {“obstructive sleep apnea” OR “obstructive sleep apnea syndrome” OR “sleep apnea hypopnea syndrome” OR "sleep apnea, obstructive" OR "sleep disordered breathing"} AND}“stroke” OR “cerebrovascular accident” OR “brain vascular accident” OR “acute stroke”} AND {“prediction” OR “predictor” OR "screening” OR “assess” OR “identify” OR “predictive value of test” OR “risk assessment” OR “risk factors” OR “questionnaire”}. Detailed English keywords and corresponding Chinese keywords are shown in Appendix 1.
Study selection and screening
Two reviewers (H. Y. and S. L.) screened the title and abstracts of searched articles for relevancy. The methodological quality and risk of bias of each selected article were independently evaluated by the two reviewers using the Prediction model Risk-of-Bias Assessment Tool (PROBAST) [19]. This tool was used to identify the potential risk of the model on the basis of four domains: participant, predictor, outcome, and analysis. Each domain had two or more signal questions. If the response to one or more signal question was “no,” this domain was considered as high risk. If no information to answer to the question was available, this domain was considered as “unclear.” The third reviewer (L. Y.) participated in the discussion in the case of discrepancies to reach a consensus.
Data extraction
A standardized form was used to tabulate the included articles and retrieved related information in accordance with the CHARMS checklist [20]. Two reviewers extracted relevant information from the selected literature independently by using the standardized data extraction form. A third reviewer was involved in discussions in case of discrepancies in extraction. The extracted study information included authors, years of publication, study design, participant characteristics (age, sample size, recruitment method, study period, settings, and stroke stage), outcome measured (method and time point of measurement), predictor (candidate predictor and final model predictor), method for handling missing data, model development (type of model, methods for selecting predictors, and model format), and model performance (calibration and discrimination).
Results
Characteristics of the included studies
A total of 2874 records were identified through electronic databases and keyword searches. A total of 1931 articles remained after removing duplicates and screening titles and abstracts for eligibility. The guidelines of the critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) checklist were used for assessment of the abstracts of identified articles [20]. Two reviewers independently screened the full texts of the remaining 101 articles for eligibility. Eleven studies were selected for this review (Fig. 1).
Fig. 1
PRISMA flow chart of study selection
×
The characteristics of the 2837participants in these 11 studies are summarized in Table 1. The average age of the participants was 60.7 years [21‐31]. The majority of participants (71.3%) were in the acute stage of stroke (less than 7 days), while 22.8% were in both the acute and subacute stages (less than 6 months). Almost 85% participants were from hospitals [21‐23, 25‐28, 30, 31], including neurology, stroke unit, or emergency units, while 15% were from stroke clinics [24, 29]. The studies were conducted in seven different countries: the USA, Canada, China, Brazil, Slovakia, India, and Italy. Most studies (72.7%) adopted a cross-sectional study design [21, 22, 24, 25, 28‐31], while two studies were retrospective cohort studies [26, 27].
Table 1
Participant characteristics in the selected studies
All the studies adopted diagnosed OSA as the outcome for prediction models. The diagnosis was based on either PSG or HSAT tests in sleep laboratories or at home, but the criteria varied among studies. Six studies defined the OSA as apnea hypopnea index (AHI) ≥ 10 times per hour [22, 24, 25, 27‐29], four used the definition of AHI ≥ 5 times per hour [23, 26, 30], and one study adopted an AHI of ≥ 15 times per hour [29]. The time between the PSG test and stroke onset ranged from 1 day to 1 year. Most studies used PSG to test for OSA [21‐23, 26‐31], while others used HSAT [24, 25], and one study used both [29]. Five studies developed or updated a new model to predict the risk of OSA in stroke patients [21, 23, 25, 28, 31], three validated exiting models [22, 27, 29], and three developed and validated the same model [24, 26, 30].
The candidate predictors considered in these studies included demographics (age, gender, and race), clinical data (medical history, body mass index (BMI), blood pressure, waist circumference, and neck circumference, and disease severity measured by the National Institutes of Health Stroke Scale (NIHSS)), laboratory data (C-reactive protein, hemoglobin HbAlc, homocysteine, echocardiography, and oximetry), lifestyle factors (smoking, cocaine used, and alcohol consumption), sleep-related data (snoring, tiredness observed, Berlin Questionnaire (BQ), and Epworth Sleepiness Scale (ESS)), and wake-up stroke. The most commonly selected predictors in the models were BMI (n = 6), followed by sex (n = 5), neck circumference (n = 4), and snoring (n = 4). The other significant predictors included blood pressure, age, ESS, NIHSS, BQ, heart failure, and oximetry. Most predictors were obtained from medical records, such as demographic data, laboratory data, and anthropometric data, upon hospital admission. The interview time for sleep screening in four studies ranged from 1 night to 7 days after stroke onset [22, 24, 25, 29, 31], while the others did not specify a time point (Table 2).
Table 2
Outcome variables and predictors in the selected studies
STOP (snoring, tired, observed, high blood pressure), BMI, age, race, neck circumference, married status, sex, history of coronary artery disease, sleep time, smoking
PSG polysomnography, HSAT home sleep apnea test, BMI body mass index, ESS Epworth Sleepiness Scale, NIHSS National Institute of Health stroke scale, PHQ-8 Patient Health Questionnaire, CHD coronary heart disease, TIA transient ischemic attack, BQ Berlin Questionnaire, SACS sleep apnea clinical score, CT brain computed tomography, SOS sleep obstructive apnea score, AF atrial fibrillation, CRP C-reactive protein, 4 V the four-variable screening tool
Model development and performance
Five studies developed or updated a new model to predict the risk of OSA in stroke patients, while three studies only validated the exiting models in different settings. Five of the developed models were logistic regression models [23, 24, 27, 29, 31], two adopted machine learning approaches such as random forest and convolutional neural network [21, 25], and one study simply combined variables from two existing instruments to validate its performance [30]. Four studies reported the predictor selection process, including backward selection [23], stepwise selection [25, 31], and bootstrapping [27], but none reported the model’s goodness of fit or calibration. The logistic regression models developed in these studies had low to moderate performance, with the area under the curve (AUC) ranging from 0.68 to 0.83 and specificity from 28 to 71.9% [22‐24, 26, 27, 29‐31] (Table 3). Four studies conducted internal validation [24, 27, 29, 31], but only one performed both internal and external validation [23].
Table 3
Modeling strategies adopted in the selected studies
D developed, V validated, C c-index, AUC area under the curve, SN sensitivity, SP specificity, NPV negative predictive value, PPV positive predictive value,
SB STOP-BANG (snoring, tired, observed, high blood pressure, BMI, age, neck circumference, gender), 4 V the four-variable screening tool, STOP-BAG snoring, tired, observed, high blood pressure, BMI, age, gender, BQ Berlin Questionnaire, SOS sleep obstructive apnea score, ESS Epworth Sleepiness Scale
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Quality assessment
Based on the PROBAST criteria, the participant selection domain was rated as low risk of bias, as all studies adopted appropriate study design and inclusion/exclusion criteria (Table 4). However, the application of this domain was judged as high concern, as four studies also included patients diagnosed with transient ischemic attack [23, 24, 26, 29]. The predictor domain was assessed as high risk of bias, because three studies did not clearly state whether predictors were measured in the same way [22, 23, 29]. The application of this domain was judged as high concern due to inconsistent predictor assessment times and unclear predictor measurement methods. All studies defined OSA diagnosis based on the PSG test; hence, the risk of bias and application concern in the outcome domain were low. The analysis domain was rated as high risk of bias, as all the included studies assessed the models’ discrimination or classification performance, but none described model calibration. Only three studies used appropriate methods to handle missing data [23, 27, 29]. The methods used to handle of missing data by other models were unclear. The overall risk of bias and application concern were high in these studies (Table 5).
Table 4
Quality assessment of the selected studies by Prediction model Risk-of-Bias Assessment Tool (PROBAST)
Quality assessment of the selected studies by Prediction model Risk-of-Bias Assessment Tool (PROBAST) (detailed)
Items
Author/year
Sico/2017
Brown/2020
Boulos/2019
Katzan/2016
Bernardini/2021
Zhang/2019
Boulos/2016
Petrie/2021
Šiarnik/2020
Camilo/2014
Srijithesh/2011
Participants
Were appropriate data sources used, e.g., cohort, randomized controlled trial, or nested case–control study data?
+
+
+
+
+
+
+
+
+
+
+
Were all inclusions and exclusions of participants appropriate?
+
+
+
+
+
+
+
+
+
+
+
Predictors
Were predictors defined and assessed in a similar way for all participants?
+
+
?
+
+
?
+
+
+
+
+
Were predictor assessments made without knowledge of outcome data?
?
+
?
+
+
?
+
+
+
+
+
Are all predictors available at the time the model is intended to be used?
+
+
+
+
+
+
+
+
+
+
+
Outcome
Was the outcome determined appropriately?
+
+
+
+
+
+
+
+
+
+
+
Was a prespecified or standard outcome definition used?
+
+
+
+
+
+
+
+
+
+
+
Were predictors excluded from the outcome definition?
+
+
+
+
+
+
+
+
+
+
+
Was the outcome defined and determined in a similar way for all participants?
+
+
+
+
+
+
+
+
+
+
+
Was the outcome determined without knowledge of predictor information?
+
+
+
+
+
+
+
+
+
+
+
Was the time interval between predictor assessment and outcome determination appropriate?
+
+
+
+
+
+
+
+
+
+
+
Analysis
Were there a reasonable number of participants with the outcome?
-
-
+
+
-
-
-
+
+
-
-
Were continuous and categorical predictors handled appropriately?
+
+
+
+
+
+
+
+
+
+
+
Were all enrolled participants included in the analysis?
+
+
+
+
+
+
+
+
+
+
+
Were participants with missing data handled appropriately?
+
?
?
+
?
+
?
?
?
?
?
Was selection of predictors based on univariable analysis avoided? (Model development studies only)
+
+
+
+
+
+
+
+
+
+
+
Were complexities in the data (e.g., censoring, competing risks, sampling of control participants) accounted for appropriately?
+
+
+
+
+
+
+
+
+
+
+
Were relevant model performance measures evaluated appropriately?
-
-
-
-
-
-
-
-
-
-
-
Were model overfitting and optimism in model performance accounted for? (Model development studies only)
-
-
-
?
-
-
-
-
-
-
-
Do predictors and their assigned weights in the final model correspond to the results from the reported multivariable analysis? (Model development studies only)?
-
-
-
+
-
-
-
-
-
-
-
+ Low risk of bias
− High risk of bias
?Unclear
Discussion
Several models have been developed to assess the risk of OSA in the general population [32] or in patients with specific diseases such as spinal cord injury [33], pulmonary arterial hypertension [34], and diabetes [35]. However, these models may not be suitable for stroke patients. To identify stroke patients at high risk of OSA, 11 studies have been conducted. Only five of these studies proposed new models, while the rest either modified or validated existing models that were originally developed for the general population.
The models developed for predicting OSA in stroke patients exhibited low to moderate performance, with a high risk of bias observed during quality assessment. Developing an accurate prediction model for OSA in stroke patients is challenging. Common predictors like waist and neck circumference may be difficult to obtain in acute stroke patients, and some predictors adopted for the general population, such as observed tiredness, may not be applicable to stroke patients due to their similarity to stroke symptoms. Moreover, most studies had small sample sizes, particularly in acute patients who need emergency care, and the risk of OSA was often overlooked in this group. However, early diagnosis of OSA in acute stroke patients is crucial for their full recovery [36]. Therefore, there is an urgent need for modeling studies with larger sample sizes and routine collection of electronic medical datasets to develop valid and accurate prediction tools for identifying the risk of OSA among vulnerable stroke patients.
Similar to the models developed for the general population, OSA prediction models for stroke patients also selected predictors such as BMI, snoring, neck circumference, waist circumference, and hypertension. However, the data collection methods in these studies were not clearly specified, and predictors like neck circumference and waist circumference may not be easily available in acute stroke patients who are critically ill. In developing countries, a lack of assistive devices may further hinder the objective collection of data in stroke units, and staff often relies on patients or family members for such data. Therefore, it is crucial to include objective and readily available predictors for better predictive model performance, such as inflammatory biomarkers interleukin-6 (IL-6) or C-reactive protein (CRP), which have been shown to be related to an increase in OSA in stroke patients in previous studies [37, 38]. While oximetry and fatigue were utilized as predictors in some studies, these symptoms are similar to those of stroke disease, which could limit the model’s performance. Other valuable predictors such as infarct location [39], dysphagia [40], and nocturia [41], which have also been associated with OSA in stroke patients, require further exploration. Therefore, future research should incorporate the following predictors in the model: demographics such as age, gender, history of diabetes, smoking, and alcohol consumption; physical examination such as BMI, blood pressure, waist, and neck circumference; clinical data such as CRP, infarct location, and heart failure; sleep characteristic such as snoring, stop breathing, and ESS; and symptoms or severity associated with stroke such as dysphagia, nocturia, and NIHSS. Moreover, the objective and clinically assessable measurements of individual predictors are important. For example, dysphagia could be measured by various methods, such as Kubota water swallowing test (KWST), Gugging Swallowing Screen (GSS), fiber-optic endoscopic evaluation of swallowing (FEES), and ultrasound examination. KWST is commonly employed in clinical settings, but its specificity is suboptimal [42]. FEES is considered the preferred method for diagnosing swallowing disorders, but its application is restricted due to its invasive nature and associated high expenses [43]. Hence, ultrasound testing has become more commonly adopted for dysphagia diagnosis, owing to its lower cost and noninvasive nature [44].
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Most of the studies included in this review were conducted in acute hospitals, with only a few conducted in primary care settings. It is worth noting that the prevalence of OSA was found to be higher in the acute phase of stroke (71.3%) compared to the chronic phase (60.6%) in a meta-analysis [45]. The differences in PSG test time across the included studies could have contributed to the poor performance of the prediction models. Furthermore, it was observed in clinical practice that individuals diagnosed with OSA as negative during the acute stage became positive during the chronic stage. In this review, the PSG test time varied from less than 24 h to 1 year after stroke onset, emphasizing the need for specific and standardized testing times. In future studies, separate prediction models for acute and chronic phases should be constructed to improve their clinical applicability.
In this systematic review, most studies used logistic regression for model construction, while a few also utilized deep learning or other machine learning algorithms. Due to the heterogeneity of included studies in the systematic reviews, there is no solid evidence to suggest differences between regression models and other machine learning models. However, one study of this review [25] showed that there was no significant difference in performance between machine learning and logistic regression models for stroke patients. Future studies should use various methods to develop models within the same populations and compare the effectiveness of these different approaches. This would provide more valuable guidance that could be beneficial to clinical practice.
In addition, the selection of a model should also take into account factors such as the sample size, the nature of data, and the purpose of model construction [46]. For instance, logistic regression is a common statistical method known for its simplicity and interpretability, frequently used in developing prediction models. However, it requires a clear structural relationship between outcome variables and predictors [25]. The decision tree algorithm has high computational efficiency, making it suitable for small datasets with diverse data types [47]. The random forest model, which predicts by aggregating the outcomes of numerous recursively partitioned tree models, is suitable for constructing supervised models with large sample sizes [48].
Although numerous studies have developed or validated prediction models for stroke patients, the generalization of these models was poor due to the lack of external validation. Of the 11 studies included in the review, only three reported the process of internal validation, and only one performed external validation [23]. Additionally, the absence of detailed algorithms hinders the external validation of these models. None of the studies included in the review reported applying their models to online accessible risk calculation tools, despite their potential benefit for stroke patients. It is of note that this review might have missed some studies published in languages other than English and Chinese due to language restrictions. Nevertheless, future research should focus on strengthening external validation tests and selecting appropriate methods to validate models, to improve the generalization of these models.
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In this review, the quality of the included studies was found at high risk of bias in terms of study design, predictors, and the handling of missing data. The majority of studies adopted cross-sectional design, which may be suitable for diagnostic models but not for the early prediction of OSA incidence [49]. Furthermore, the included studies poorly reported the number and handling method of missing data. Only three studies reported the detailed process of predictor selection, and stepwise selection, a widely used traditional method, was employed. However, previous evidence has confirmed that stepwise selection could generate the risk of model overfitting [50]. Modern statistical methods, such as bootstrapping or the least absolute shrinkage and selection operator, are promising methods for identifying important variables to resolve the overfitting problem [51, 52]. Therefore, future studies should restrict the candidate list and adopt the shrinkage method to develop high-quality prediction models.
In this review, model discrimination performance, as indicated by AUC values, varied from 0.502 to 0.84, with newly developed models performing better than existing models for the general population. Future models should consider incorporating factors related to patients with stroke to enhance their quality. Calibration, defined as the agreement between observed outcome and prediction, is also important, but none of the included studies in this review described it using the calibration plot or Hosmer–Lemeshow test [27]. Therefore, future studies should include calibration and discrimination in assessing model clinical usefulness. Additionally, model classification largely depends on the predefined threshold and should be carefully considered based on clinical settings in future studies.
Given the high prevalence of undiagnosed OSA in the general population [53, 54], it is crucial to develop advanced tools that can effectively identify individuals at high risk. These tools should help healthcare professionals and patients make informed decisions, streamline the referral process for PSG testing, ensure accurate diagnoses, and promote prompt initiation of treatment. Various scales, such as the Berlin Questionnaire, STOP-BANG, and ESS, have been commonly used, along with regression models, to detect high-risk populations. For instance, Chang et al. utilized snoring in sitting as predictors, while the OSA50 scale incorporated age 50 or older, snoring, observed apnea, and waist circumference for predictions [55]. Other studies have included tongue position, BMI, and tonsil size as predictors [56]. However, these models have shown low to moderate performance. Dysphagia, a symptom frequently observed in stroke patients, impacts an estimated 38.5 to 50% of individuals who have experienced a stroke [57, 58]. Previous research has indicated that dysphagia serves as an independent risk factor for stroke patients with OSA [59]. Additionally, the location of the infarction within the brain stem has been associated with the severity of OSA in stroke patients [60]. These identified predictors could be integrated into forthcoming models as stroke-specific factors, thereby enhancing the efficacy and precision of these predictive models. Furthermore, in terms of model development methods, exploring artificial intelligence models like random forests and decision trees in the general population are necessary. Regarding the application of the model, utilizing web-based methods to present the developed model can enhance its applicability and assist clinical medical personnel, family caregivers, or individuals themselves in early screening.
Conclusion
Various prediction models for OSA in stroke patients have been developed or validated, but their performance was found to be low, and the methodology had high-risk bias. To address these issues, future studies should focus on the following gaps: first, successful prediction models for stroke patients should incorporate accessible clinical predictors. Second, internal and external validation should be conducted using a sufficient sample size, and missing values should be appropriately handled to reduce bias. Providing an easily accessible final model for clinical work, such as through web-based calculators or apps, is valuable. Additionally, subgroup comparisons, such as patients with acute, subacute, or chronic stroke, should be taken into account. Finally, generalization can be increased by collecting samples from multiple centers and different environments.
Acknowledgements
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Declarations
Ethics approval and consent to participate
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Consent for publication
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Competing interests
The authors declare that they have no competing interests.
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