Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study

Pin Zhang; Lei Wu; Ting-Ting Zou; ZiXuan Zou; JiaXin Tu; Ren Gong; Jie Kuang

doi:10.2196/48487

Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study

JMIR Form Res. 2024 Jan 3:8:e48487. doi: 10.2196/48487.

Authors

Pin Zhang^#^{1

2}, Lei Wu^#¹, Ting-Ting Zou¹, ZiXuan Zou¹, JiaXin Tu¹, Ren Gong^#³, Jie Kuang^#¹

Affiliations

¹ Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China.
² School of Public Health and Management, Nanchang Medical College, Nanchang, China.
³ Department of Cardiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.

^# Contributed equally.

PMID: 38170581
PMCID: PMC10794958
DOI: 10.2196/48487

Abstract

Background: The incidence of major adverse cardiovascular events (MACEs) remains high in patients with acute myocardial infarction (AMI) who undergo percutaneous coronary intervention (PCI), and early prediction models to guide their clinical management are lacking.

Objective: This study aimed to develop machine learning-based early prediction models for MACEs in patients with newly diagnosed AMI who underwent PCI.

Methods: A total of 1531 patients with AMI who underwent PCI from January 2018 to December 2019 were enrolled in this consecutive cohort. The data comprised demographic characteristics, clinical investigations, laboratory tests, and disease-related events. Four machine learning models-artificial neural network (ANN), k-nearest neighbors, support vector machine, and random forest-were developed and compared with the logistic regression model. Our primary outcome was the model performance that predicted the MACEs, which was determined by accuracy, area under the receiver operating characteristic curve, and F1-score.

Results: In total, 1362 patients were successfully followed up. With a median follow-up of 25.9 months, the incidence of MACEs was 18.5% (252/1362). The area under the receiver operating characteristic curve of the ANN, random forest, k-nearest neighbors, support vector machine, and logistic regression models were 80.49%, 72.67%, 79.80%, 77.20%, and 71.77%, respectively. The top 5 predictors in the ANN model were left ventricular ejection fraction, the number of implanted stents, age, diabetes, and the number of vessels with coronary artery disease.

Conclusions: The ANN model showed good MACE prediction after PCI for patients with AMI. The use of machine learning-based prediction models may improve patient management and outcomes in clinical practice.

Keywords: acute myocardial infarction; cardiovascular event; early prediction; machine learning; percutaneous coronary intervention.

©Pin Zhang, Lei Wu, Ting-Ting Zou, ZiXuan Zou, JiaXin Tu, Ren Gong, Jie Kuang. Originally published in JMIR Formative Research (https://formative.jmir.org), 03.01.2024.