Article

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.
CI - Copyright (c) 2014 American Congress of Rehabilitation Medicine. Published by
Elsevier Inc. All rights reserved.

Langue : ANGLAIS