• Steven J Holfinger, M Melanie Lyons, Brendan T Keenan, Diego R Mazzotti, Jesse Mindel, Greg Maislin, Peter A Cistulli, Kate Sutherland, Nigel McArdle, Bhajan Singh, Ning-Hung Chen, Thorarinn Gislason, Thomas Penzel, Fang Han, Qing Yun Li, Richard Schwab, Allan I Pack, and Ulysses J Magalang.
• Division of Pulmonary, Critical Care, and Sleep Medicine, The Ohio State University Wexner Medical Center, Columbus, OH. Electronic address: Steven.Holfinger@osumc.edu.
• Chest. 2022 Mar 1; 161 (3): 807817807-817.

BackgroundPrediction tools without patient-reported symptoms could facilitate widespread identification of OSA.Research QuestionWhat is the diagnostic performance of OSA prediction tools derived from machine learning using readily available data without patient responses to questionnaires? Also, how do they compare with STOP-BANG, an OSA prediction tool, in clinical and community-based samples?Study Design And MethodsLogistic regression and machine learning techniques, including artificial neural network (ANN), random forests (RF), and kernel support vector machine, were used to determine the ability of age, sex, BMI, and race to predict OSA status. A retrospective cohort of 17,448 subjects from sleep clinics within the international Sleep Apnea Global Interdisciplinary Consortium (SAGIC) were randomly split into training (n = 10,469) and validation (n = 6,979) sets. Model comparisons were performed by using the area under the receiver-operating curve (AUC). Trained models were compared with the STOP-BANG questionnaire in two prospective testing datasets: an independent clinic-based sample from SAGIC (n = 1,613) and a community-based sample from the Sleep Heart Health Study (n = 5,599).ResultsThe AUCs (95% CI) of the machine learning models were significantly higher than logistic regression (0.61 [0.60-0.62]) in both the training and validation datasets (ANN, 0.68 [0.66-0.69]; RF, 0.68 [0.67-0.70]; and kernel support vector machine, 0.66 [0.65-0.67]). In the SAGIC testing sample, the ANN (0.70 [0.68-0.72]) and RF (0.70 [0.68-0.73]) models had AUCs similar to those of the STOP-BANG (0.71 [0.68-0.72]). In the Sleep Heart Health Study testing sample, the ANN (0.72 [0.71-0.74]) had AUCs similar to those of STOP-BANG (0.72 [0.70-0.73]).InterpretationOSA prediction tools using machine learning without patient-reported symptoms provide better diagnostic performance than logistic regression. In clinical and community-based samples, the symptomless ANN tool has diagnostic performance similar to that of a widely used prediction tool that includes patient symptoms. Machine learning-derived algorithms may have utility for widespread identification of OSA.Copyright © 2021 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

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