A two-tier screening model using quality-of-life measures and pulse oximetry to screen adults with sleep-disordered breathing

Abstract

Using quality-of-life measures and pulse oximetry, this study developed a two-tiered prediction algorithm with an aim to prioritize sleep-disordered breathing patients for polysomnography. Data from 355 patients were evaluated to obtain their clinical information, Chinese version of Epworth sleepiness scale, and snore outcomes survey scores against respiratory disturbance index (RDI). In the first-tier screening, receiver-operating characteristics were calculated with an initial strategy of choosing optimal prediction sensitivity. The second-tier strategy investigated the association between pulse oximetry data (desaturation index of 3%) against RDI to optimize prediction specificity. The "SOS score of 55 and ESS score of 9" was the optimal combination that yielded the highest sensitivity (0.603) in the first-tier screening. The strategy can includ 94.93% possible patients (probability = 0.6) with positive predictive value of 0.997. The area under the curve (AUC) was 0.88 (p < 0.001). Desaturation index of 3% would optimized specificity (0.966, probability = 0.5) in the second-tier screening to exclude 54% of innocent patients, with negative predictive values of 0.93 and AUC of 0.951 (p < 0.001). The two-tier screening model jointly excluded 4.8% of innocent subjects and prioritized 40% of severe patients for polysomnography. The prediction model is sufficiently accurate and feasible for large-scale population screening.