Development and validation of machine learning models to predict in-hospital mortality in ICU patients with sepsis and chronic kidney disease

Sepsis is a life-threatening condition, particularly in intensive care unit (ICU) patients with chronic kidney disease (CKD). However, accurate prediction of in-hospital mortality in this high-risk population remains a clinical challenge. This study aimed to develop and validate machine learning (ML) models to predict in-hospital mortality among ICU patients with sepsis and CKD. Patients diagnosed with both sepsis and CKD were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Feature selection was performed using the Boruta algorithm. Multiple ML models were developed, including logistic regression (LR), decision tree, k-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), neural network (NN), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost), along with the Sequential Organ Failure Assessment (SOFA) score for comparison. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and average precision (AP). The best-performing model was externally validated in an independent cohort from the eICU Collaborative Research Database (eICU-CRD) and further interpreted using Shapley Additive Explanations (SHAP). A total of 4,686 ICU patients with sepsis and CKD were included in the development cohort. Among the models, XGBoost demonstrated the best performance with an AUC of 0.911, AP of 0.771, specificity of 96%, and sensitivity of 62%. In the external validation cohort of 3,718 patients, XGBoost also achieved excellent predictive performance with an AUC of 0.855. Model calibration and decision curve analysis confirmed its clinical utility. The top 20 predictors were visualized and ranked based on SHAP values. Machine learning models, particularly XGBoost, can accurately predict in-hospital mortality in ICU patients with sepsis and CKD. These models may assist clinicians in risk stratification and decision-making for this vulnerable patient population.