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

Rationale: 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 multi-domain 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 Sleepines

This content is for informational purposes only and is not medical advice. It is not intended to diagnose, treat, cure, or prevent any disease. Consult a healthcare professional before starting any supplement, treatment, or program: especially if you are pregnant, nursing, taking medication, or managing a health condition.