Accuracy of Machine Learning Models for Early Prediction of Major Cardiovascular Events Post Myocardial Infarction: A Systematic Review and Meta-Analysis - Yi Xiang; Dong Liu; Leilei Guo; Yuhua Zheng; Xiaoman Xiong; Tao Xu (Reviews in Cardiovascular Medicine, 2025)
Background: Major adverse cardiovascular events (MACEs) significantly affect the prognosis of patients with myocardial infarction (MI). With the widespread application of machine learning (ML), researchers have attempted to develop models for predicting MACEs following MI. However, there remains a lack of evidence-based proof to validate their value. Thus, we conducted this study to review the ML models’ performance in predicting MACEs following MI, contributing to the evidence base for the application of clinical prediction tools. Methods: A systematic literature search spanned four major databases (Cochrane, Embase, PubMed, Web of Science) with entries through to June 19, 2024. With the Prediction Model Risk of Bias Assessment Tool (PROBAST), the risk of bias in the included models was appraised. Subgroup analyses based on whether patients had percutaneous coronary intervention (PCI) were carried out for the analysis. Results: Twenty-eight studies were included for analysis, covering 59,392 patients with MI. The pooled C-index for ML models in the validation sets was 0.77 (95% CI 0.74–0.81) in predicting MACEs post MI, with a sensitivity (SEN) and specificity (SPE) of 0.78 (95% CI 0.73–0.82) and 0.85 (95% CI 0.81–0.89), respectively; the pooled C-index was 0.73 (95% CI 0.66–0.79) in the validation sets, with an SEN of 0.75 (95% CI 0.67–0.81) and an SPE of 0.84 (95% CI 0.75–0.90) in patients who underwent PCI. Logistic regression was the predominant model in the studies and demonstrated relatively high accuracy. Conclusions: ML models based on clinical characteristics following MI, influence the accuracy of prediction. Therefore, future studies can include larger sample sizes and develop simplified tools for predicting MACEs. The PROSPERO registration: CRD42024564550, https://www.crd.york.ac.uk/PROSPERO/view/CRD42024564550.