Risk factor analysis and machine learning-based prediction model for type 4b acute myocardial infarction induced by very late stent thrombosis.

backgroundTo analyze the risk factors for type 4b acute myocardial infarction (AMI) caused by very late stent thrombosis (VLST) and develop a predictive model using machine learning techniques.

methodsPatients who had a history of coronary stent implantation and developed AMI more than 1 year later, and had undergone coronary angiography were included. Based on the presence of VLST on coronary angiography, patients were classified into the VLST-4b-AMI group and the de novo AMI group. Data including the first coronary stent implantation, drug treatment, baseline hospitalization data, and coronary angiographic findings were collected and compared between the two groups. Logistic regression and Lasso machine learning were used to identify risk factors for VLST-4b-AMI, and a predictive model was developed using machine learning techniques, followed by performance evaluation.

resultsUnivariate logistic regression analysis identified risk factors and protective factors for VLST-4b-AMI. Lasso regression selected 12 variables closely associated with VLST-4b-AMI occurrence. Based on these risk factors, predictive models were developed using eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN). The XGBoost model demonstrated the best predictive performance (AUC = 0.724, 95% CI: 0.640–0.809), followed by KNN (AUC = 0.698, 95% CI: 0.614–0.783), and RF (AUC = 0.669, 95% CI: 0.574–0.763). SVM had the lowest performance (AUC = 0.641, 95% CI: 0.547–0.736).

conclusionsThe XGBoost-based machine learning model showed the best predictive performance for VLST-4b-AMI, offering a promising tool for early prediction of this high-risk type of AMI.