Predicting optimal CPAP by neural network reduces titration failure: A randomized study

Purpose: Continuous positive airway pressure (CPAP) is considered the standard therapy for obstructive sleep apnea syndrome. In the absence of standard protocol, CPAP titration may be unsuccessful. The purpose of this study was to test the hypothesis that application of an artificial neural network (ANN) to CPAP titration would achieve an optimal CPAP pressure within a shorter time interval and would lead to a decrease in CPAP titration failure. Methods: One hundred fifteen patients were randomized 1:1 to either conventional CPAP titration (n = 58) or to an ANN-guided CPAP titration (n = 57). Both groups were assessed for time to optimal CPAP pressure, for titration failure, and for CPAP compliance therapy. Results: Patients in the ANN-guided CPAP titration arm were able to achieve optimal CPAP at a shorter time interval compared to the conventional group (198.7 ± 143.8 min versus 284.0 ± 126.5 min) (p < 0.001). There was also a lower titration failure in patients randomized to the ANN-guided CPAP titration arm (16%) compared to the conventional arm (36%) (p = 0.02). Compliance with treatment did not differ across the two arms. Conclusions: The use of ANN for guiding CPAP titration may be superior to the conventional method in maximizing the time to achieve optimal CPAP and in reducing CPAP titration failure.