A Data-Driven Framework for Clinical Decision Support Systems in Positive Airway Pressure and Oxygen Titration

Current obstructive sleep apnea treatment relies on manual PAP titration, but it has limitations. Complex interactions during titration and variations in SpO₂ data accuracy pose challenges. Patients with co-occurring chronic hypercapnia may require precise oxygen titration. To address these issues, we propose a Clinical Decision Support System using Markov decision processes.

This study, compliant with data protection laws, focused on adults with OSA-induced hypoxemia utilizing supplemental oxygen and CPAP/BiPAP therapy. PAP titration, conducted over one night, involved vigilant monitoring of vital signs and physiological parameters. Adjustments to CPAP pressure, potential BiLevel transitions, and supplemental oxygen were precisely guided by patient metrics. Markov decision processes outlined three treatment actions for disorder management, incorporating expert medical insights.

In our study involving 14 OSA patients (average age: 63 years, 27% females, BMI 41 kg m^-2), significant improvements were observed in key health parameters after manual titration. The initial AHI of 61.8 events per hour significantly decreased to an average of 18.0 events per hour after PAP and oxygen titration (p < 0.0001), indicating a substantial reduction in sleep-disordered breathing severity. Concurrently, SpO₂ levels increased significantly from an average of 79.7% before titration to 89.1% after titration (p < 0.0003). Pearson correlation coefficients demonstrated aggravation of hypercapnia in 50% of patients (N = 5) with initial pCO₂ < 55 mmHg during the increase in CPAP pressure. However, transitioning to BiPAP exhibited a reduction in pCO₂ levels, showcasing its efficacy in addressing hypercapnia. Simultaneously, BiPAP therapy correlated with a substantial increase in SpO₂, underscoring its positive impact on oxygenation in OSA patients. Markov Decision Process analysis demonstrated realistic patient behavior during stable night conditions, emphasizing minimal apnea and good toleration to high CPAP pressure.

The development of a framework for Markov decision processes of PAP and oxygen titration algorithms holds promise for providing algorithms for improving pCO₂ and SpO₂ values. While challenges remain, including the need for high-quality data, the potential benefits in terms of patient management and care optimization are substantial, and this approach represents an exciting frontier in the realm of telemedicine and respiratory healthcare.