Multivariate Machine Learning Model for Long-Term Risk Prediction of Acute Coronary Syndrome in Patients With Heart...

Heart failure with preserved ejection fraction is a heterogeneous syndrome, and comorbid obstructive sleep apnea further increases the risk of acute coronary syndrome. However, effective tools for long-term acute coronary syndrome risk stratification in this population remain limited. This study aimed to develop and externally validate a machine learning-based prognostic model for predicting acute coronary syndrome risk at multiple time points in patients with heart failure with preserved ejection fraction and obstructive sleep apnea.

We retrospectively enrolled 2272 patients with heart failure with preserved ejection fraction and obstructive sleep apnea from 2 tertiary hospitals. Candidate predictors from clinical characteristics, sleep monitoring, laboratory tests, and echocardiography were selected using least absolute shrinkage and selection operator regression and the Boruta algorithm. Thirty-three survival models were developed, and the optimal model was selected based on the concordance index and externally validated.

Ten core predictors were identified, including metabolic, sleep-related, and cardiac parameters. The accelerated oblique random survival forest model achieved the best discrimination among all evaluated models, with a concordance index of 0.731 in the external validation cohort. The model demonstrated good time-dependent discrimination, with area under the curve values of 0.742, 0.734, and 0.755 at 20, 30, and 40 months, respectively. Calibration analyses and decision curve analysis supported its reliability and clinical utility.

We developed and externally validated a machine learning-based model for multitime-point acute coronary syndrome risk prediction in patients with heart failure with preserved ejection fraction and obstructive sleep apnea. The accelerated oblique random survival forest model, implemented as a web-based calculator, may facilitate individualized risk assessment and clinical decision-making.

URL: https://www.chictr.org.cn/; Unique Identifier: ChiCTR2300075727.