Machine Learning to Predict Long-term Cardiovascular Death following Myocardial Infarction: Incremental Value of Echocardiographic Data

Abstract Background Machine learning (ML) for prediction of cardiovascular (CV) death following myocardial infarction (MI) has not been well studied. This study sought to define the incremental value of (i) integrating comprehensive echocardiographic data in ML models, and (ii) ML approaches over Cox Regression (CPH), for predicting CV death following MI. Methods Retrospective cohort study of consecutive patients with MI admitted at a tertiary referral hospital, with echocardiography performed within 24 hours of admission. Models were trained on a cohort admitted between 2013-2017 (n=1,568) and validated on a separate temporal holdout cohort from 2018-2021 (n=1,634). Two ML models Gradient Boosted Cox and a DeepSurv Neural Network were developed and compared with conventional multivariable Cox regression. The Shapley Additive exPlanations method was used for ML model interpretation. Results In the final study population of 3,202 patients (mean age 63.2±12.5 years;29.2% females), 28.8% had ST-elevation MI and the mean LVEF was 52.5±11.2%. At a median follow-up of 4.5 years, there were 139 (4.3%) CV deaths. In the validation set, Gradient Boosted Cox achieved the highest performance (C-index 0.861), compared with conventional Cox regression (C-index 0.813, p=0.037) and the DeepSurv Neural Network (C-index 0.847, p=0.38) for the prediction of CV death. Within the GB Cox model, 14 out of the top 20 features for predicting CV death were echocardiographic variables, including LV size, LVEF, and diastolic parameters. Further, in nested ML models, the addition of echocardiographic parameters provided incremental value beyond clinical variables + LVEF alone (C-index 0.861 vs 0.792, p=0.017). Conclusion ML integration of comprehensive echocardiographic data leads to improved prediction of CV death following MI, with key measures of LV size, systolic and diastolic function contributing substantially to prognostic models.