Interpretable Machine Learning Models for Predicting Malignant Ventricular Arrhythmia in Patients with Acute ST-Segment Elevation Myocardial Infarction Based on Systemic Inflammation Index.

ABSTRACT: BackgroundPercutaneous coronary intervention (PCI) improves outcomes in ST-segment elevation myocardial infarction (STEMI) by restoring myocardial perfusion. However, post-procedural malignant ventricular arrhythmias (MVA), as a serious complication, can cause hemodynamics instability and lead to in-hospital sudden cardiac death. Systemic inflammation indices serve as reliable biomarkers of inflammatory status and may predict arrhythmia risk. Current prediction models, however, frequently overlook key inflammatory markers and predominantly rely on traditional linear methods rather than advanced machine learning (ML) techniques. To address this limitation, our study developed an interpretable ML model using systemic inflammation indices to predict in-hospital MVA risk in STEMI patients foll

SUBMITTER: Han J 

PROVIDER: S-EPMC12402570 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature