Abstract
Severe acute kidney injury (sAKI) is a prevalent and serious complication among patients with sepsis-induced myocardial injury (SIMI). Prompt and early prediction of sAKI has an important role in timely intervention, ultimately improving the patients' survival rate. This study aimed to establish machine learning models to predict sAKI via thorough analysis of data derived from electronic medical records. The data of eligible patients were retrospectively collected from the Medical Information Mart for Intensive Care IV database (MIMIC-IV database) from 2008 to 2019. A total of 1,467 patients with SIMI were included and the primary outcome was the development of sAKI within 7 days after intensive care unit admission. Nine predictive variables were selected and further used to establish the machine learning models. Five different machine learning models were established. The random forest model yielded the most accurate predictions with the highest area under receiver operating characteristic curve (AUC = 0.81) and accuracy (0.79), while the AUC of the traditional SOFA model is 0.66, with an accuracy of 0.71. The machine learning models can be effective tools for predicting the risk of sAKI in patients with SIMI and the RF model performed best.