Predictive Model of Optimal Continuous Positive Airway Pressure for Obstructive Sleep Apnea Patients with Obesity by Using Machine Learning

ABSTRACT

OBJECTIVES: The aim of this study was to develop a predicting model for the optimal continuous positive airway pressure (CPAP) for obstructive sleep apnea (OSA) patient with obesity by using a machine learning METHODS: We retrospectively investigated the medical records of 162 OSA patients who had obesity [body mass index (BMI) ≥ 25] and undertaken successful CPAP titration study. We divided the data to a training set (90%) and a test set (10%), randomly. We made a random forest model and a least absolute shrinkage and selection operator (lasso) regression model to predict the optimal pressure by using the training set, and then applied our models and previous reported equations to the test set. To compare the fitness of each models, we used a correlation coefficient (CC) and a mean absolute error (MAE). RESULTS: The random forest model showed the best performance {CC 0.78 [95% confidence interval (CI) 0.43–0.93], MAE 1.20}. The lasso regression model also showed the improved result [CC 0.78 (95% CI 0.42–0.93), MAE 1.26] compared to the Hoffstein equation [CC 0.68 (95% CI 0.23–0.89), MAE 1.34] and the Choi's equation [CC 0.72 (95% CI 0.30–0.90), MAE 1.40]. CONCLUSIONS: Our random forest model and lasso model (26.213+0.084×BMI+0.004×apnea-hypopnea index+0.004×oxygen desaturation index−0.215×mean oxygen saturation) showed the improved performance compared to the previous reported equations. The further study for other subgroup or phenotype of OSA is required.