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 following emergency PCI, thereby facilitating clinical decision-making.MethodsWe 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.ResultsAmong 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.ConclusionThe 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.

Keywords: ST-segment elevation myocardial infarction; machine learning; malignant ventricular arrhythmia; percutaneous coronary intervention; systemic inflammation index.

Figure 1.

Flowchart Illustrating Patient Selection and Cohort Distribution for Developing and Validating Predictive Models in STEMI Patients. STEMI, ST-Segment Elevation Myocardial Infarction; PCI, Percutaneous Coronary Intervention; SHAP, Shapley Additive Explanations.

Figure 2.

LASSO Regression Analysis for Feature Selection. (A) Coefficient Profiles of 11 Variables. (B) Optimal Penalty Coefficient (Lambda) Selection Via Five-Fold Cross-Validation. The Plot Displays Partial Likelihood Deviance Versus log(lambda), with Lambda as the Tuning Parameter. Red Dots Indicate Mean Deviance Values per Model at Each Lambda, Accompanied by Error Bars for Standard Error. Dotted Vertical Lines Denote Optimal Lambda Values Based on Minimum Deviance and the 1-SE Rule.

Figure 3.

Correlation Heatmap of Variables.

Figure 4.

Performance Comparison of ML Classifiers (Logit, SVM, RF, XGBoost) Using Traditional Clinical Data: (A) ROC Curves, (B) Calibration Plots, and (C) DCA. The ROC-AUC Values were 0.806, 0.713, 0.869, and 0.863, Respectively. ML, Machine Learning; ROC, Receiver Operating Characteristic; AUC, Area under the Curve; DCA, Decision Curve Analysis; Logit, Logistic Regression; SVM, Support Vector Machine; RF, Random Forest; XGBoost, Extreme Gradient Boosting.

Figure 5.

Performance Evaluation of ML Classifiers (Logit, SVM, RF, XGBoost) Using Systemic Inflammation Indices: (A) ROC Curves, (B) Calibration Plots, and (C) DCA. The ROC-AUC Values Were 0.841, 0.857, 0.898, and 0.884, Respectively. ML, Machine Learning; ROC, Receiver Operating Characteristic; AUC, Area Under the Curve; DCA, Decision Curve Analysis; Logit, Logistic Regression; SVM, Support Vector Machine; RF, Random Forest; XGBoost, Extreme Gradient Boosting.

Figure 6.

Performance Comparison of ML Classifiers (Logit, SVM, RF, XGBoost) Using Combined Traditional Clinical Data and Systemic Inflammation Indices: (A) ROC Curves, (B) Calibration Plots, and (C) DCA. The ROC-AUC Values Were 0.894, 0.898, 0.925, and 0.915, Respectively. ML, Machine Learning; ROC, Receiver Operating Characteristic; AUC, Area Under the Curve; DCA, Decision Curve Analysis; Logit, Logistic Regression; SVM, Support Vector Machine; RF, Random Forest; XGBoost, Extreme Gradient Boosting.

Figure 7.

Evaluating the Optimal ML Model's Predictive Performance with an External Validation Cohort: (A) ROC Curve (AUC = 0.899), (B) Calibration Curve, and (C) DCA. ML, Machine Learning; ROC, Receiver Operating Characteristic; AUC, Area Under the Curve; DCA, Decision Curve Analysis.

Figure 8.

SHAP Analysis of the RF Model for MVA Prediction: (A) Summary Plot and (B) Feature Importance Ranking. SHAP, Shapley Additive Explanations; RF, Random Forest; MVA, Malignant Ventricular Arrhythmias; SII, Systemic immune-inflammation Index; CK-MB: Creatine Kinase-MB; NLR, Neutrophil to Lymphocyte Ratio; SBP, Systolic Blood Pressure.

Figure 9.

SHAP Dependency Plot of the RF Model. SHAP, Shapley Additive Explanations; RF, Random Forest; SII, Systemic Immune-Inflammation index; NLR, Neutrophil to Lymphocyte Ratio.

Figure 10.

SHAP Force Plots Demonstrating Individual Prediction Outcomes: (A) MVA Patient and (B) non-MVA Patient. SHAP, Shapley Additive Explanations; MVA, Malignant Ventricular Arrhythmias; SBP, Systolic Blood Pressure; SII, Systemic Immune-inflammation Index; NLR, Neutrophil to Lymphocyte Ratio.

Figure 11.

Web Application for Predicting in-Hospital MVA post-PCI in STEMI Patients: User Interface and Prediction Display. MVA, Malignant Ventricular Arrhythmias; PCI, Percutaneous Coronary Intervention; STEMI, ST-Segment Elevation Myocardial Infarction; SBP, Systolic Blood Pressure; NLR, Neutrophil to Lymphocyte Ratio; SII, Systemic Immune-Inflammation index.