A machine learning model for prediction of cardiac arrest-associated acute kidney injury in the ICU: an internal and external validation study

Abstract

Background: Cardiac arrest-associated acute kidney injury is common after cardiac arrest and adversely affects patient survival and disease outcomes. Early prediction of acute kidney injury is essential for guiding clinical management, especially in cardiac arrest patients admitted to the intensive care unit. Early detection of acute kidney injury can improve long-term outcomes. Methods: Data were obtained from two local hospitals and the Medical Information Mart for Intensive Care (MIMIC)-IV database. Feature selection was performed using least absolute shrinkage and selection operator regression. Model performance was evaluated using decision curve analysis and calibration curves, and the best-performing model was interpreted with SHapley Additive exPlanations. Results: This study included 873 patients from local hospitals and 719 patients from the MIMIC-IV database as an external validation cohort, least absolute shrinkage and selection operator regression identified 10 predictor variables. The logistic regression model demonstrated the best performance in predicting cardiac arrest-associated acute kidney injury, with an area under the curve of 0.958 (95% CI: 0.942–0.974) in the training set, 0.953 (95% CI: 0.920–0.987) in the internal validation set, and 0.825 (95% CI: 0.791–0.859) in the external validation set. The model was further interpreted using the SHAP framework. Conclusion: An externally validated logistic regression model incorporating 10 variables effectively predicted early acute kidney injury onset in cardiac arrest patients. The SHapley Additive exPlanations algorithm facilitated model interpretation, helping clinicians understand the contribution of each variable to acute kidney injury risk, to determine which factors contribute most significantly to patient risk.