Prototyping OSA: A New Framework for Predicting Cardiovascular Outcomes and CPAP Benefit

Obstructive sleep apnea (OSA) is associated with major adverse cardiac events (MACE); continuous positive airway pressure (CPAP) has not reduced MACE in randomized trials. We hypothesized that multidomain machine learning would identify OSA subgroups differing in cardiovascular risk and CPAP response.

Methods

We conducted a secondary analysis of SAVE trial data. After preprocessing, Gaussian mixture models were fitted; Bayesian information criterion and C-index selected five prototypes. Cox models estimated MACE risk by prototype. Baseline quality of life (EQoL), Epworth Sleepiness Scale (ESS), and Framingham Risk Score (FRS) were compared; an existing causal survival forest model estimated prototype-specific CPAP effects on cardiovascular risk.

Measurements and Main Results

After preprocessing, five OSA prototypes were identified among 2,676 participants using 88 baseline variables. Prototypes differed in selected demographic, cardiometabolic, medication, and sleep variables, including smoking status, diabetes, prior myocardial infarction/stroke, antiplatelet/lipid-lowering therapy, BMI, oxygenation metrics, apnea-hypopnea index, and ESS (overall p<0.001). They also differed in baseline EQoL, FRS, MACE rate, and predicted CPAP response. Prototype 1 had the highest ESS, FRS, and MACE rate, whereas Prototype 5 had the lowest FRS and MACE rate. Using the causal survival forest model to assess prototype-specific CPAP effects on cardiovascular risk, Prototype 5 was predicted to benefit from CPAP (p<0.05), whereas Prototypes 1, 3, and 4 showed no benefit or possible harm.

Conclusion

These findings demonstrate heterogeneity in cardiovascular risk and predicted CPAP response, potentially explaining neutral trial results. After external validation, these prototypes could guide trial enrichment and targeted CPAP use to improve cardiovascular outcomes.

Impact Statement

This study advances clinical medicine by identifying multidomain OSA prototypes that differ in baseline cardiovascular risk and predicted cardiovascular response to CPAP, potentially supporting more precise treatment selection and future cardiovascular-outcomes trials. It adds to the disease knowledge base by showing that OSA-related cardiovascular risk is not captured by conventional severity metrics alone, but reflects interactions among sleep, cardiometabolic, demographic, and clinical characteristics. These findings provide a framework for mechanistic and prospective studies of the biological pathways underlying heterogeneous cardiovascular susceptibility and CPAP response in OSA.