LAUSR

Obstructive sleep apnea (OSA) is a major sleep disorder that presents with excessive daytime sleepiness (EDS). Epworth Sleepiness Scale (ESS) as a measure of EDS is used to monitor population sleep health. We aim to assess the association between ESS and apnea hypopnea index (AHI) extracted from polysomnography (PSG) reports using natural language processing (NLP) algorithms. We curated 90,483 PSG notes from the nationwide Corporate Data Warehouse (CDW) of Veteran Affair (VA) from 10/1999 to 10/2020. We used rule-based nearest neighbor and forward-backward NLP techniques to extract ESS and AHI from PSG reports. We reported the performance of NLP algorithm compared to chart review. We used AHI>5 as the cut-point to identify OSA and incremented the threshold for abnormal ESS from 5 to 15 to find the best cut-point for stratification of EDS. We used logistic regression to report the performance of the ESS cut-point using the area under the curve (AUC). The model also adjusted for age, sex, BMI, race, ethnicity, and Charlson comorbidity index. 39,318 patient clinical notes’ (age 50±15 years; BMI 30±5) notes documented both AHI and ESS. (50±15 year; 30±5 BMI). NLP algorithms accuracy was ≥ 90%. We observed the same level of sensitivity (57%) or specificity (42%) for ESS of 5 and 10, but the ESS of 5 resulted in the best negative (27%) and positive (72%) predictive value for AHI prediction. The area under curve for prediction OSA based on ESS cut-off of 5 was 0.50 (95%CI, 0.49, 0.51) and the AUC improved to 0.65 (95%CI, 0.64, 0.65) after adjustment. Our results suggest that ESS is not an effective tool for predicting AHI. Reducing the ESS cut-off to 5 may enhance its predictive value, but it does not have clinical implication in identifying patient with OSA. Sleep medicine clinicians may use other sleep questionnaires, such as the STOP-Bang questionnaire, which is reported to be a reliable, concise, and easy-to-use screening tool. This work is supported by the VA Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413); National Institute of Health (NHI) Grant # 1K25HL152006-01 (PI: Razjouyan) and Grant # R01NR018342 (PI: Nowakowski).