Development and deployment of an interpretable stacking ensemble model for predicting in-hospital mortality in ICU patients with chronic kidney disease and sepsis.

Objective: To develop an interpretable stacking ensemble model for predicting in-hospital mortality in intensive care unit (ICU) patients with CKD and sepsis and to deploy it as a web-based tool for bedside clinical use. Methods: Data were extracted from the MIMIC-IV 3.0 database and split into training and test sets at a 7:3 ratio. Feature selection was performed by combining the least absolute shrinkage and selection operator (LASSO) regression with the Boruta algorithm. Eight machine learning (ML) models were trained and optimized via ten-fold cross-validation and grid search. The two models with the highest area under the curve (AUC) in the training set were combined using a stacking ensemble strategy. SHapley Additive exPlanations (SHAP) were applied to improve interpretability. Model performance was compared with the SOFA score. Results: A total of 5344 ICU patients with CKD and sepsis were included, with an in-hospital mortality rate of 19.1%. After feature selection, 16 variables were retained. In the training set, XGBoost and LightGBM performed best. The stacking model achieved an AUC of 0.757 on the test set, outperforming SOFA (AUC = 0.668). SHAP analysis identified age, Acute Physiology Score III, Simplified Acute Physiology Score II, and respiratory rate as the top predictors. The model was also deployed as a publicly accessible web application. Conclusion: The stacking ensemble model demonstrated good discriminatory performance and interpretability for predicting in-hospital mortality in ICU patients with CKD and sepsis. Its web-based deployment provides a convenient platform for early risk assessment, although external validation is needed to confirm its broader applicability.