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

作者：Shuoyan An, Zixiang Ye, Wuqiang Che, Yanxiang Gao, Jiahui Li, Jingang Zheng · 发表于：BMC Infectious Diseases · 年份：2025 · DOI：10.1186/s12879-025-11949-5 · 被引用次数：4 · 研究领域：Sepsis Diagnosis and Treatment、Acute Kidney Injury Research、Heart Failure Treatment and Management

BACKGROUND: 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. METHODS: 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). RESULTS: 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 sens...