Interpretable machine learning model for predicting myocardial injury in intensive care unit patients using SHapley Additive exPlanations analysis

Objective The identification of myocardial injury in the intensive care unit (ICU) has received little attention from researchers. Therefore, this retrospective cohort study aimed to develop a machine-learning model to predict the occurrence of myocardial injury in the ICU. Methods Based on the Clinical Research Data Platform of Peking University People's Hospital, we enrolled adult, non-cardiac surgical, and non-obstetric patients who were admitted to the ICU between 2012 and 2022. Logistic regression, random forest, LASSO regression, support vector machine and extreme gradient boosting (XGBoost) models were developed to predict myocardial injury. Results Data from 7453 non-cardiac surgery adult patients in ICU were collected in the derivation cohort (myocardial injury group: 2161 [29%], non-myocardial injury group: 5292 [71%]). Among the five models, the XGBoost model (area under the curve = 0.779; accuracy = 0.781) exhibited the best predictive performance for myocardial injury and the results were explained by the SHapley Additive exPlanations analysis. The top six features of the XGBoost model were maximal heart rate, respiratory rate, temperature, minimal heart rate, age and plasma transfusion. Conclusion This machine-learning model, developed using the XGBoost algorithm, could be a valuable tool for clinical decision-making and detecting myocardial injury in the ICU.