Can a Prediction Model Combining Self-Reported Symptoms, Sociodemographic and Clinical Features Serve as a Reliable First Screening Method for Sleep Apnea.

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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? Justine A. Aaronson, MSc, Janneke Nachtegaal, PhD, Tijs van Bezeij, MD, Erny Groet, MSc, Winni F. Hofman, PhD, Joost G. van den Aardweg, MD, PhD, Coen A.M. van Bennekom, MD, PhD Archives of Physical Medicine and Rehabilitation Volume 95, Issue 4, Pages 747-752 (April 2014) DOI: 10.1016/j.apmr.2013.12.011 Copyright © 2014 American Congress of Rehabilitation Medicine Terms and Conditions

Fig 1 ROC curve for the prediction model for a high likelihood of SAS. The prediction model includes the following variables: age, sex, BMI, apneas, and falling asleep during daytime. The area under the curve is .76 (95% CI, .71–.81). Archives of Physical Medicine and Rehabilitation 2014 95, 747-752DOI: (10.1016/j.apmr.2013.12.011) Copyright © 2014 American Congress of Rehabilitation Medicine Terms and Conditions

Fig 2 Sensitivity and specificity for selected cutoff points of the predicted probability of a high likelihood of SAS. Archives of Physical Medicine and Rehabilitation 2014 95, 747-752DOI: (10.1016/j.apmr.2013.12.011) Copyright © 2014 American Congress of Rehabilitation Medicine Terms and Conditions