USING SELF-REPORTED QUESTIONNAIRE DATA TO PRIORITIZE OSA PATIENTS FORPOLYSOMNOGRAPHY

Many laboratories have large numbers of patients with suspected obstru ctive sleep apnea (OSA)waiting to be tested. We assessed the use of si mple clinical data to detect those patients with an apnea index <20 (l ow AI) who could be studied less emergently. Using questionnaires comp leted by patients prior to evaluation, we collected data on 354 consec utive patients (281 males, 73 females; mean age 48.6 years) referred f or OSA and assessed with polysomnography (PSG). The questionnaires inc luded the Epworth sleepiness scale (ESS), height, weight, age, and a h istory of observed apnea. Analysis of receiver operating characteristi cs curves revealed that both body mass index (BMI) [area under curve = 0.7258, standard error (SE) = 0.03, p < 0.01] and ESS (area under cur ve = 0.5581, SE = 0.03, p = 0.03) were significantly better than chanc e alone in detecting people with AI < 20. ESS less than or equal to 12 was found in 37.9% of the subjects but 39.6% of those expected to hav e a low AI using ESS had an AI greater than or equal to 20. A BMI less than or equal to 28 was found in 24.9% of the subjects; 14.8% of thos e expected to have a low AI using BMI had an AI greater than or equal to 20. Combining these variables improved accuracy but resulted in sma ller groups; a cut-off of ESS less than or equal to 12 and BMI less th an or equal to 28 resulted in a group of 33 (9.3% of subjects), only t wo (6%) of whom were falsely called low AI. Adding to this the bet tha t apnea had not been observed resulted in a group of nine patients (2. 5% of subjects), none of whom had an AI greater than or equal to 20. T hus there is a tradeoff; the more variables used, the greater the accu racy but the smaller the percent of cases selected to have low AI. How ever, in laboratories with hundreds of patients waiting to be tested, any procedure better than chance to help prioritize patients seems wor thwhile.