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?

Autor:	Aaronson JA; Heliomare Research and Development, Wijk aan Zee, The Netherlands; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands. Electronic address: j.aaronson@heliomare.nl., Nachtegaal J; Heliomare Research and Development, Wijk aan Zee, The Netherlands., van Bezeij T; Heliomare Rehabilitation, Wijk aan Zee, The Netherlands., Groet E; Heliomare Research and Development, Wijk aan Zee, The Netherlands; Heliomare Rehabilitation, Wijk aan Zee, The Netherlands., Hofman WF; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands., van den Aardweg JG; Department of Pulmonary Medicine, Medical Centre Alkmaar, Alkmaar, The Netherlands., van Bennekom CA; Heliomare Research and Development, Wijk aan Zee, The Netherlands; Heliomare Rehabilitation, Wijk aan Zee, The Netherlands.
Jazyk:	angličtina
Zdroj:	Archives of physical medicine and rehabilitation [Arch Phys Med Rehabil] 2014 Apr; Vol. 95 (4), pp. 747-52. Date of Electronic Publication: 2013 Dec 28.
DOI:	10.1016/j.apmr.2013.12.011
Abstrakt:	Objective: 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. Interventions: Not applicable. Main Outcome Measures: We administered an SAS questionnaire consisting of self-reported symptoms and 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 .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%. Conclusions: The prediction model shows acceptable diagnostic accuracy for a high likelihood of SAS. Therefore, we conclude that the prediction model can serve as a reasonable first screening method in a stepped diagnostic approach to SAS in stroke rehabilitation. (Copyright © 2014 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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