SAS score: Targeting high-specificity for efficient population-wide monitoring of obstructive sleep apnea

ProposalThis paper investigates a novel screening tool for Obstructive Sleep Apnea Syndrome (OSAS), which aims at efficient population-wide monitoring. To this end, we introduce SASscore which provides better OSAS prediction specificity while maintaining a high sensitivity.MethodsWe process a cohort of 2595 patients from 4 sleep laboratories in Western Romania, by recording over 100 sleep, breathing, and anthropometric measurements per patient; using this data, we compare our SASscore with state of the art scores STOP-Bang and NoSAS through area under curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We also evaluate the performance of SASscore by considering different Apnea–Hypopnea Index (AHI) diagnosis cut-off points and show that custom refinements are possible by changing the score’s threshold.ResultsSASscore takes decimal values within the interval (2, 7) and varies linearly with AHI; it is based on standardized measures for BMI, neck circumference, systolic blood pressure and Epworth score. By applying the STOP-Bang and NoSAS questionnaires, as well as the SASscore on the patient cohort, we respectively obtain the AUC values of 0.69 (95% CI 0.66-0.73, p p p SASscore’s diagnosis cut-off point from 3 to 3.7, both sensitivity and specificity become roughly 0.6.ConclusionsIn comparison with the existing scores, SASscore is a more appropriate screening tool for monitoring large populations, due to its improved specificity. Our score can be tailored to increase either sensitivity or specificity, while balancing the AUC value.