Integrating Digital Biomarkers with Feedback-Driven Clustering for Optimized CPAP Adherence in OSA Management

Obstructive Sleep Apnea (OSA) is a significant disorder of cardiovascular health and well-being, but compliance to continuous positive airway pressure (CPAP) therapy is another problem. The existing way of analyzing CPAP adherence relies mainly on the previous compliance data to offer treatments that will delay intervention and increase the risk of therapy termination. An artificial intelligence framework consisting of such functions as biometric monitoring and patient cluster analysis via machine learning is suggested for the development of an individual CPAP therapy plan. The system modifies the pathway of therapy based on consistent monitoring of heart rate variability (HRV), oxygen desaturation index (ODI), and variability in respiration effort measurements. The proposed real time decision support system bests traditional CPAP monitoring systems due to its capability to do individualized interventions where biometric signals together with the participants’ response and observed CPAP usage is used in cultivating better compliance results. By combining CPAP machine data, the system improves patient diagnosis category and plans for intervention. Initial results indicate feasibility on 45,000 French patients for the proactive, precision-based shift of CPAP management from retrospective tracking towards improving long-term success of treatment.