Time-structured detection of pre-event cardiovascular instability using CPAP-derived Cheyne-Stokes breathing

Continuous positive airway pressure (CPAP) telemonitoring provides daily respiratory metrics, including Cheyne-Stokes breathing percentage (CSB%), which may reflect ventilatory-circulatory instability. However, inter-individual variability limits the clinical applicability of fixed thresholds. We developed a time-structured detection architecture using CPAP telemonitoring time-series data and evaluated its ability to identify temporal patterns preceding cardiovascular and cerebrovascular events.

In this retrospective observational study, 1265 patients with obstructive sleep apnoea undergoing CPAP telemonitoring were analysed. Daily CSB% values were smoothed using a 3-day moving average and evaluated relative to individualized dynamic baselines. The detection framework consisted of two complementary components: identification of sustained deviation from baseline and detection of abrupt pre-event surges. Central apnoea predominance, assessed using the central apnoea index, was incorporated as a hierarchical escalation layer. Among 25 adjudicated cardiovascular and cerebrovascular events in 20 patients (heart failure 11, atrial fibrillation 7, cerebrovascular accident 7), the architecture detected 23 events (92.0%) within the predefined D-28 window. Median lead time was 12.0 days (IQR 2.8-24.0). False-positive alerts were concentrated within a subset of individuals. Hierarchical filtering reduced alerts by 94.9% relative to the baseline signal layer while preserving event enrichment. Distinct temporal phenotypes, including trajectory-dominant and spike-dominant patterns, were observed across disease categories, consistent with disease-specific pre-event dynamics.

CPAP-derived CSB% may function as a time-structured digital biomarker reflecting evolving ventilatory-circulatory instability. A trajectory-based detection architecture may enable early identification of cardiovascular instability while maintaining operational feasibility in large-scale telemonitoring environments.