Prediction of 1-Year Mortality from Acute Myocardial Infarction Using Machine Learning

Risk stratification at hospital discharge could be instrumental in guiding postdischarge care. In this study, the risk models for 1-year mortality using machine learning (ML) were evaluated for guiding management of acute myocardial infarction (AMI) patients. From the Korea Acute Myocardial Infarction Registry (KAMIR) dataset, 22,182 AMI patients were selected. The 1-year all-cause mortality was recorded at 12-month follow-up periods. Anomaly detection was conducted for removing outliers; principal component analysis for dimensionality reduction, recursive feature elimination algorithm for feature selection. Model selection and training were conducted with 70% of the dataset after the creation and cross-validation of hundreds of models with decision trees, ensembles, logistic regressions, and deepnets algorithms. The rest of the dataset (30%) was used for comparison between the ML and KAMIR score-based models. The mean age of the AMI patients was 64 years, 71.8% were male, and 56.7% were eventually diagnosed with ST-elevation myocardial infarction. There were 1,332 patients suffering from all-cause mortality (6%) during a median 338 days of follow-up. The ML models for 1-year mortality were well-calibrated (Hosmer-Lemeshow p >0.05) and showed good discrimination (area under the curve for test cohort: 0.918). Compared with the performance of the KAMIR score model, the ML model had a higher area under the curve, net reclassification improvement, and integrated discrimination improvement. The ML model for 1-year mortality was well-calibrated and had excellent discriminatory ability and higher performance. In a comprehensive clinical evaluation process, this model could support risk stratification and management in postdischarge AMI patients. Risk stratification at hospital discharge could be instrumental in guiding postdischarge care. In this study, the risk models for 1-year mortality using machine learning (ML) were evaluated for guiding management of acute myocardial infarction (AMI) patients. From the Korea Acute Myocardial Infarction Registry (KAMIR) dataset, 22,182 AMI patients were selected. The 1-year all-cause mortality was recorded at 12-month follow-up periods. Anomaly detection was conducted for removing outliers; principal component analysis for dimensionality reduction, recursive feature elimination algorithm for feature selection. Model selection and training were conducted with 70% of the dataset after the creation and cross-validation of hundreds of models with decision trees, ensembles, logistic regressions, and deepnets algorithms. The rest of the dataset (30%) was used for comparison between the ML and KAMIR score-based models. The mean age of the AMI patients was 64 years, 71.8% were male, and 56.7% were eventually diagnosed with ST-elevation myocardial infarction. There were 1,332 patients suffering from all-cause mortality (6%) during a median 338 days of follow-up. The ML models for 1-year mortality were well-calibrated (Hosmer-Lemeshow p >0.05) and showed good discrimination (area under the curve for test cohort: 0.918). Compared with the performance of the KAMIR score model, the ML model had a higher area under the curve, net reclassification improvement, and integrated discrimination improvement. The ML model for 1-year mortality was well-calibrated and had excellent discriminatory ability and higher performance. In a comprehensive clinical evaluation process, this model could support risk stratification and management in postdischarge AMI patients.