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Background <p>To 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.</p> Methods <p>Patients who had a history of coronary stent implantation and developed AMI more than 1&#xa0;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.</p> Results <p>Univariate 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).</p> Conclusions <p>The 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月14日

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  • Xiaowei Li,
  • Mingdong Gao,
  • Jianyong Xiao,
  • Jixiang Wang,
  • Nan Zhang,
  • Zhiyuan Zhang,
  • Pengju Lu,
  • Yuqing Li,
  • Yu Zhou,
  • Yin Liu,
  • Jing Gao

摘要

Background

To 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.

Methods

Patients 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.

Results

Univariate 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).

Conclusions

The 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.