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Predicting Nondiagnostic Home Sleep Apnea Tests Using Machine Learning.

Stretch R, Ryden A, Fung CH, Martires J, Liu S, Balasubramanian V, Saedi B, Hwang D, Martin JL, Della Penna N, Zeidler MR. Predicting Nondiagnostic Home Sleep Apnea Tests Using Machine Learning. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine. 2019 Nov 15; 15(11):1599-1608.

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STUDY OBJECTIVES: Home sleep apnea testing (HSAT) is an efficient and cost-effective method of diagnosing obstructive sleep apnea (OSA). However, nondiagnostic HSAT necessitates additional tests that erode these benefits, delaying diagnoses and increasing costs. Our objective was to optimize this diagnostic pathway by using predictive modeling to identify patients who should be referred directly to polysomnography (PSG) due to their high probability of nondiagnostic HSAT. METHODS: HSAT performed as the initial test for suspected OSA within the Veterans Administration Greater Los Angeles Healthcare System was analyzed retrospectively. Data were extracted from pre-HSAT questionnaires and the medical record. Tests were diagnostic if there was a respiratory event index (REI) 5 events/h. Tests with REI < 5 events/h or technical inadequacy-two outcomes requiring additional testing with PSG-were considered nondiagnostic. Standard logistic regression models were compared with models trained using machine learning techniques. RESULTS: Models were trained using 80% of available data and validated on the remaining 20%. Performance was evaluated using partial area under the precision-recall curve (pAUPRC). Machine learning techniques consistently yielded higher pAUPRC than standard logistic regression, which had pAUPRC of 0.574. The random forest model outperformed all other models (pAUPRC 0.862). Preferred calibration of this model yielded the following: sensitivity 0.46, specificity 0.95, positive predictive value 0.81, negative predictive value 0.80. CONCLUSIONS: Compared with standard logistic regression models, machine learning models improve prediction of patients requiring in-laboratory PSG. These models could be implemented into a clinical decision support tool to help clinicians select the optimal test to diagnose OSA.

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