Abstract Purpose Patients with acute myocardial infarction (AMI) are in a critical condition, facing a high risk of death in the intensive care unit (ICU) with significant individual differences. The aim of this study is to integrate clinical data using machine learning algorithms to construct a model for predicting the risk of death in ICU for AMI patients, thereby providing clinicians with an objective risk assessment tool. Patients and methods This study is a retrospective study that included 2285 patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The primary outcome was in-hospital mortality of ICU patients with acute myocardial infarction. Univariate analysis was performed to screen statistically significant variables, and Lasso regression was used to further identify independent influencing factors that were significantly related to the risk of death. Based on the screened variables, we developed multiple machine learning(ML) models and evaluated their predictive efficiency for the risk of death in ICU patients with acute myocardial infarction. Results A total of 2285 ICU-admitted AMI patients were included, and 613 AMI patients died in the ICU. After the screening process, a total of 15 clinical characteristic variables were included in developing logistic regression models and ten ML models (each model utilized the same 15 clinical characteristics), and the table outlines the predictive performance of these models. According to the area under the Area Under the Curve(AUC), the 3 prediction models showed good predictive efficacy for AMI ICU mortality risk. The CatBoostTEST model AUC = 0.78, the LGBMTEST model AUC = 0.766, and the RFTEST model AUC = 0.755. Conclusion Machine learning models provide an excellent tool for predicting the risk of death from myocardial infarction in the ICU, laying the groundwork for potential improvements in clinical decision-making and patient outcomes. Machine learning models offer a good predictive tool for the mortality risk of AMI in the ICU, enabling early and accurate identification of high-risk patients, timely implementation of targeted interventions, and ultimately reducing the ICU mortality rate of AMI patients.
Xiao et al. (Tue,) studied this question.