Risk Prediction of Major Adverse Cardiovascular Events Within One Year After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome: Machine Learning–Based Time-to-Event Analysis
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Overview
Machine learning demonstrates improved risk prediction for major adverse cardiovascular events in patients with ACS after percutaneous coronary intervention, suggesting new early intervention options.
Key Points
Machine learning improves risk prediction for major adverse cardiovascular events after percutaneous coronary intervention in patients with ACS.
The best model achieved a concordance index of 0.743 at 30 days and 0.616 at 1 year post-discharge.
Analysis of electronic health records from 3159 ACS patients revealed key risk factors including medication adherence and glomerular filtration rate.
These findings support tailored postdischarge management, focusing on early intervention strategies for high-risk patients.