Development of an Intelligent Clinical Decision Support System for Predicting One-Year CPAP Adherence in Patients with Obstructive Sleep Apnea: A Pilot Study

Background/Objectives: Obstructive sleep apnea (OSA) is a prevalent chronic disorder whose first-line treatment, continuous positive airway pressure (CPAP), is effective only if the patient maintains sufficient adherence. Early predictions of the risk of low adherence would make it possible to personalize follow-up and optimize healthcare resources. The aim of this study was to develop and evaluate a machine-learning-based clinical decision support system to predict CPAP adherence after one year of treatment. Methods: A cohort of 200 patients with OSA from the Sleep-Disordered Breathing Unit of Hospital Álvaro Cunqueiro in Vigo was used. The cohort was split into a training set (n = 160) and an independent test set (n = 40). Two scenarios were defined: Scenario A, with pre-treatment variables, and Scenario B, which also includes early adherence metrics. In each scenario, variables were selected through recursive feature elimination. The selected variables were apnea-hypopnea index (AHI), 3% oxygen desaturation index (ODI3%), chronic obstructive pulmonary disease and neck circumference in Scenario A, and first-month adherence, ODI3% and AHI in Scenario B. Once these subsets were defined, several classifiers were analyzed. Results: Random Forest was the model selected in both scenarios. On the test set, Scenario A reached an area under the curve (AUC) of 0.71 (sensitivity 0.83; specificity 0.45) and Scenario B an AUC of 0.91 (sensitivity 0.90; specificity 0.82). Conclusions: Early prediction of CPAP adherence using machine learning is feasible; incorporating real first-month use markedly improves discriminative ability. The system was integrated into a web prototype as a proof of concept. Given the modest sample size and the absence of external validation, these results should be interpreted as preliminary, corresponding to an exploratory, feasibility study. For future implementation, an extensive clinical validation process will be required, along with the expansion of the database, which will likely contribute to improving the system’s robustness and generalization capacity.