Am. J. Respir. Crit. Care Med., Volume 164, Number 10, November 2001, 1904-1909

An Algorithm to Stratify Sleep Apnea Risk in a Sleep Disorders Clinic Population

INDIRA GURUBHAGAVATULA, GREG MAISLIN, and ALLAN I. PACK

Center for Sleep and Respiratory Neurobiology, Pulmonary and Critical Care Division, Department of Medicine, University of Pennsylvania Medical Center, Philadelphia, Pennsylvania; and Pulmonary and Critical Care Section, Philadelphia VA Medical Center, Philadelphia, Pennsylvania

Obstructive sleep apnea may lead to complications if not identified and treated. Polysomnography is the diagnostic standard, but is often inaccessible due to bed shortages. A system that facilitates prioritization of patients requiring sleep studies would thus be useful. We retrospectively compared the accuracy of a two-stage risk-stratification algorithm for sleep apnea using questionnaire plus nocturnal pulse oximetry against using polysomnography to identify patients without apnea (Objective 1) and those with severe apnea (Objective 2). Patients were those referred to a university-based sleep disorders clinic due to suspicion of sleep apnea. Subjects completed a sleep apnea symptom questionnaire, and underwent oximetry and two-night polysomnography. We used bootstrap methodology to maximize sensitivity of our model for Objective 1 and specificity for Objective 2. We calculated sensitivity, specificity, positive and negative predictive values, and rate of misclassification error of the two-stage risk-stratification algorithm for each of our two objectives. The model identified cases of sleep apnea with 95% sensitivity and severe apnea with 97% specificity. It excluded only 8% of patients from sleep studies, but prioritized up to 23% of subjects to receive in-laboratory studies. Among sleep disorders clinic referrals, a two-stage risk-stratification algorithm using questionnaire and nocturnal pulse oximetry excluded few patients from sleep studies, but identified a larger proportion of patients who should receive early testing because of their likelihood of having severe disease.

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