Accepted for/Published in: JMIR Formative Research
Date Submitted: Apr 25, 2023
Date Accepted: Sep 15, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Machine learning for early prediction of MACEs after first PCI in AMI patients
ABSTRACT
Background:
The incidence of major adverse cardiovascular events (MACEs) remains high in acute myocardial infarction (AMI) patients 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 AMI patients 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. Two machine learning models, artificial neural network (ANN) and random forest (RF), 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 (AUC), 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 AUCs of the ANN, RF and logistic regression models were 80.49%, 72.67% and 71.77%, respectively. The top five predictors in the ANN model were left ventricular ejection fraction (LVEF), implanted stent number, age, diabetes, and number of vessels with coronary artery disease.
Conclusions:
The ANN model showed good MACEs prediction after PCI for AMI patients. The use of machine learning-based prediction models may improve patient management and outcomes in clinical practice.
Citation
