Creation and verification of an interpretable machine learning model for forecasting in-hospital results in Cardiorenal syndrome patients in critical condition

Cardiorenal syndrome (CRS) encompasses a spectrum of disorders in which acute or chronic dysfunction of the heart or kidneys induces dysfunction of the other. The heart–kidney crosstalk underlying CRS has since been extended into the broader cardiovascular-kidney-metabolic (CKM) syndrome by incorporating metabolic risk factors.CRS is associated with significantly increased mortality in intensive care unit (ICU) patients. However, dedicated tools for predicting short-term outcomes remain lacking, which limits timely risk stratification and treatment optimization. This multicenter cohort study enrolled 6,668 CRS patients from MIMIC-IV (v2.2) for model development and 3,602 from eICU-CRD (v2.0) for external validation. In patients remaining in the ICU for ≥24 hours, candidate predictors were ascertained within the first 24 hours, including early treatment exposures. Subsequent all-cause in-hospital mortality was predicted at this 24-hour landmark. Eleven machine learning algorithms were systematically compared after 1:1 random undersampling of the development cohort. SHapley Additive exPlanations (SHAP) was applied to enhance interpretability and identify key prognostic features. The artificial neural network (ANN) model performed best. The final model incorporated six readily obtainable variables: AKI stage, age, mechanical ventilation, vasopressor use, β-blocker use, and ACEI/ARB use. It achieved an AUC of 0.810 (95% CI 0.757–0.860) in internal validation and 0.704 (95% CI 0.679–0.729) in external validation, indicating good internal but moderate external discrimination. We developed the first interpretable machine learning model specifically for predicting in-hospital mortality in critically ill CRS patients. SHAP provides transparent feature contribution analysis, and a web-based interface facilitates clinical implementation. When applied 24 hours after ICU admission, the model enables early identification of high-risk patients. It may also support risk stratification and resource allocation in the ICU.