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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.