ArticleSleep medicine2026
Predicting sleep state from continuous positive airway pressure flow in patients with obstructive sleep apnea.
Article in Sleep medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionThe first line treatment for obstructive sleep apnea (OSA) is continuous positive airway pressure (CPAP). CPAP adherence is assessed by hours of mask usage per night. To understand whether patients using CPAP are sleeping while wearing their mask (and thus may benefit from therapy), we aimed to predict sleep from CPAP flow signals.
methodsWe developed a machine learning algorithm to predict sleep states (wake vs. sleep) using 130 features (126 derived from CPAP flow data, age, sex, body mass index (BMI) and CPAP level). We used polysomnography records from 100 randomly selected adult OSA patients undergoing CPAP titration at Yale Sleep Center (n = 70 training, n = 30 testing samples). Sleep stages were scored by AASM-certified technologists. We determined model accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
resultsThe dataset included 48 women and 52 men with a median (interquartile) age of 61 (46.3-69.0) years, BMI of 33.8 (27.8-39.6) kg/m
conclusionEpoch-by-epoch sleep and wake can be effectively predicted by a model using CPAP flow and demographics. Such information may complement usage data from CPAP devices to better assess CPAP effectiveness.
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