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Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization:
Jinwan Wang1, Shuai Wang2, Mark Xuefang Zhu1
1School of Information Management, Nanjing University, Nanjing, China.
Insights
Machine learning models can predict major adverse cardiovascular events (MACE) after coronary revascularization. The XGBoost algorithm demonstrated the best performance, offering valuable insights for clinical decision-making in MACE prevention.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Coronary heart disease incidence is rising globally.
- Coronary revascularization, including percutaneous coronary intervention, is crucial for treatment.
- Major adverse cardiovascular events (MACE) post-revascularization remain a significant clinical challenge.
Purpose of the Study:
- To develop and validate machine learning models for predicting MACE within six months post-coronary revascularization.
- To identify key factors influencing MACE occurrence.
Main Methods:
- A retrospective study of 1004 patients undergoing coronary revascularization.
- Utilized six machine learning algorithms: decision tree, random forest, logistic regression, naïve Bayes, support vector machine, and XGBoost.
- Employed an oversampling strategy, 70% training/30% validation split, and assessed performance using accuracy, precision, recall, F1-score, and AUC.
Main Results:
- XGBoost achieved the highest performance with an AUC of 0.8599, accuracy of 0.7788, precision of 0.8058, recall of 0.7345, and F1-score of 0.7685.
- Anticoagulant drug use and disease course were identified as top predictive factors for MACE.
- 21 patient characteristics were found to be statistically significant predictors of MACE.
Conclusions:
- Machine learning models provide acceptable performance for MACE prediction after coronary revascularization.
- The XGBoost model offers a valuable tool for targeted intervention and clinical decision-making.
- These models can aid in the prevention of MACE in patients undergoing coronary revascularization.
Background:
As a major health hazard, the incidence of coronary heart disease has been increasing year by year. Although coronary revascularization, mainly percutaneous coronary intervention, has played an important role in the treatment of coronary heart disease, major adverse cardiovascular events (MACE) such as recurrent or persistent angina pectoris after coronary revascularization remain a very difficult problem in clinical practice.
Objective:
Given the high probability of MACE after coronary revascularization, the aim of this study was to develop and validate a predictive model for MACE occurrence within 6 months based on machine learning algorithms.
Methods:
A retrospective study was performed including 1004 patients who had undergone coronary revascularization at The People's Hospital of Liaoning Province and Affiliated Hospital of Liaoning University of Traditional Chinese Medicine from June 2019 to December 2020. According to the characteristics of available data, an oversampling strategy was adopted for initial preprocessing. We then employed six machine learning algorithms, including decision tree, random forest, logistic regression, naïve Bayes, support vector machine, and extreme gradient boosting (XGBoost), to develop prediction models for MACE depending on clinical information and 6-month follow-up information. Among all samples, 70% were randomly selected for training and the remaining 30% were used for model validation. Model performance was assessed based on accuracy, precision, recall, F1-score, confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), and visualization of the ROC curve.
Results:
Univariate analysis showed that 21 patient characteristic variables were statistically significant (P<.05) between the groups without and with MACE. Coupled with these significant factors, among the six machine learning algorithms, XGBoost stood out with an accuracy of 0.7788, precision of 0.8058, recall of 0.7345, F1-score of 0.7685, and AUC of 0.8599. Further exploration of the models to identify factors affecting the occurrence of MACE revealed that use of anticoagulant drugs and course of the disease consistently ranked in the top two predictive factors in three developed models.
Conclusions:
The machine learning risk models constructed in this study can achieve acceptable performance of MACE prediction, with XGBoost performing the best, providing a valuable reference for pointed intervention and clinical decision-making in MACE prevention.
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