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

Abstract

Background Percutaneous 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 following emergency PCI, thereby facilitating clinical decision-making. Methods We retrospectively analyzed 485 consecutive STEMI patients, dividing them into training and temporal validation cohorts. Based on clinical outcomes, patients were stratified into MVA and non-MVA groups. In the training cohort, we developed and internally validated multiple ML models using three predictor sets: (1) systemic inflammation indices alone, (2) traditional clinical indicators alone, and (3) their combination. The models’ performance was subsequently assessed in the temporal validation cohort. For the optimal model, we employed SHAP (Shapley Additive Explanations) values to evaluate feature importance and enhance model interpretability. Results Among the 485 enrolled patients, 88 (18.1%) developed MVA during hospitalization. Nine predictors, including systemic inflammation indices and traditional clinical markers, were significantly associated with MVA risk. The random forest (RF) model demonstrated superior predictive performance, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.925, outperforming logistic regression (Logit, AUC: 0.894), support vector machines (SVM, AUC: 0.898), and extreme gradient boosting (XGBoost, AUC: 0.915). SHAP analysis identified five key predictors—two systemic inflammation indices and three traditional clinical markers—as the most influential factors for assessing in-hospital MVA risk in STEMI patients after emergency PCI. Conclusion The RF model, integrating both systemic inflammation indices and traditional clinical indicators, provides an effective tool for predicting in-hospital MVA in STEMI patients following PCI. This ML approach enhances risk stratification accuracy, facilitating early clinical intervention to mitigate MVA occurrence.