Dynamic Portfolio for Personalized CPAP Treatment: Adaptive Digital Biomarker-Driven Strategies Across the OSA Care Pathway

Obstructive Sleep Apnea (OSA) management struggles from static retrospective adherence models which build their assessment on rigid compliance benchmarks. This paper describes a modular AI-based framework for CPAP (Continuous Positive Airway Pressure) personalization, which combines real-time digital biomarkers and dynamic therapystage clustering. Patients are stratified into four progressive stages, new, adherent, attempting and non-adherent, using unsupervised learning on the CPAP usage data, clinical history, and behavioral surveys. The system dynamically adjusts biomarker tracking such as HRV, ODI and RVO, produces weekly risk scores and triggers phase specific interventions such as alerts, education modules and physician reviewed actions. Unlike previous models, the framework continually improves clusters and provides interventions depending on physiological feedback and engagement history. Preliminary timescale review shows its ability of identifying dropout threat and adaptation of interventions in response to real time surveillance. The proposed system enables scalable, explainable, and medically integrated for biomarker-driven real-time CPAP personalization methods throughout the OSA care pathway.