Jason J Sico1, H Klar Yaggi2, Susan Ofner3, John Concato2, Charles Austin4, Jared Ferguson5, Li Qin6, Lauren Tobias7, Stanley Taylor8, Carlos A Vaz Fragoso7, Vincent McLain3, Linda S Williams9, Dawn M Bravata10. 1. Neurology Service, VA Connecticut Healthcare System, West Haven, Connecticut; Department of Neurology and Center for Neuroepidemiology and Clinical Neurological Research, Yale University School of Medicine, New Haven, Connecticut; Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, Connecticut. Electronic address: jason.sico@yale.edu. 2. Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, Connecticut. 3. Department of Biostatistics, IUPUI, Indiana University School of Medicine, Indianapolis, Indiana. 4. VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, Indianapolis, Indiana; Department of Internal Medicine, Indiana University School of Medicine, Indianapolis, Indiana. 5. Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, Indianapolis, Indiana. 6. Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut. 7. Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut. 8. Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, Connecticut. 9. VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, Indianapolis, Indiana; Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana; Regenstrief Institute, Indianapolis, Indiana. 10. VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, Indianapolis, Indiana; Department of Internal Medicine, Indiana University School of Medicine, Indianapolis, Indiana; Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana; Regenstrief Institute, Indianapolis, Indiana.
Abstract
BACKGROUND: Screening instruments for obstructive sleep apnea (OSA), as used routinely to guide clinicians regarding patient referral for polysomnography (PSG), rely heavily on symptomatology. We sought to develop and validate a cerebrovascular disease-specific OSA prediction model less reliant on symptomatology, and to compare its performance with commonly used screening instruments within a population with ischemic stroke or transient ischemic attack (TIA). METHODS: Using data on demographic factors, anthropometric measurements, medical history, stroke severity, sleep questionnaires, and PSG from 2 independently derived, multisite, randomized trials that enrolled patients with stroke or TIA, we developed and validated a model to predict the presence of OSA (i.e., Apnea-Hypopnea Index ≥5 events per hour). Model performance was compared with that of the Berlin Questionnaire, Epworth Sleepiness Scale (ESS), the Snoring, Tiredness, Observed apnea, high blood Pressure, Body mass index, Age, Neck circumference, and Gender instrument, and the Sleep Apnea Clinical Score. RESULTS: The new SLEEP Inventory (Sex, Left heart failure, ESS, Enlarged neck, weight [in Pounds], Insulin resistance/diabetes, and National Institutes of Health Stroke Scale) performed modestly better than other instruments in identifying patients with OSA, showing reasonable discrimination in the development (c-statistic .732) and validation (c-statistic .731) study populations, and having the highest negative predictive value of all in struments. CONCLUSIONS: Clinicians should be aware of these limitations in OSA screening instruments when making decisions about referral for PSG. The high negative predictive value of the SLEEP INventory may be useful in determining and prioritizing patients with stroke or TIA least in need of overnight PSG. Published by Elsevier Inc.
RCT Entities:
BACKGROUND: Screening instruments for obstructive sleep apnea (OSA), as used routinely to guide clinicians regarding patient referral for polysomnography (PSG), rely heavily on symptomatology. We sought to develop and validate a cerebrovascular disease-specific OSA prediction model less reliant on symptomatology, and to compare its performance with commonly used screening instruments within a population with ischemic stroke or transient ischemic attack (TIA). METHODS: Using data on demographic factors, anthropometric measurements, medical history, stroke severity, sleep questionnaires, and PSG from 2 independently derived, multisite, randomized trials that enrolled patients with stroke or TIA, we developed and validated a model to predict the presence of OSA (i.e., Apnea-Hypopnea Index ≥5 events per hour). Model performance was compared with that of the Berlin Questionnaire, Epworth Sleepiness Scale (ESS), the Snoring, Tiredness, Observed apnea, high blood Pressure, Body mass index, Age, Neck circumference, and Gender instrument, and the Sleep Apnea Clinical Score. RESULTS: The new SLEEP Inventory (Sex, Left heart failure, ESS, Enlarged neck, weight [in Pounds], Insulin resistance/diabetes, and National Institutes of Health Stroke Scale) performed modestly better than other instruments in identifying patients with OSA, showing reasonable discrimination in the development (c-statistic .732) and validation (c-statistic .731) study populations, and having the highest negative predictive value of all in struments. CONCLUSIONS: Clinicians should be aware of these limitations in OSA screening instruments when making decisions about referral for PSG. The high negative predictive value of the SLEEP INventory may be useful in determining and prioritizing patients with stroke or TIA least in need of overnight PSG. Published by Elsevier Inc.
Entities:
Keywords:
Ischemic stroke; Obstructive sleep apnea; Screening; TIA
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