Key result
Neural network predicts cardiorenal ICU mortality with moderate-to-good accuracy across internal and external cohorts.
Why the study?
Does an interpretable machine learning model using early clinical features accurately forecast in-hospital mortality in critically ill patients with cardiorenal syndrome?
Cohort (n=10,270)
Yes
Does an interpretable machine learning model using early clinical features accurately forecast in-hospital mortality in critically ill patients with cardiorenal syndrome?
Answers are grounded in this paper and cite their sources.
This study provides the first interpretable, web-accessible machine learning tool using six routine 24-hour ICU variables to stratify in-hospital mortality risk in critically ill cardiorenal syndrome patients.
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.
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Xia et al. (2026) conducted a cohort in Cardiorenal syndrome in critical condition (n=10,270). Artificial neural network (ANN) machine learning model using 6 variables (AKI stage, age, mechanical ventilation, vasopressor use, beta-blocker use, and ACEI/ARB use) ascertained within the first 24 hours of ICU stay vs. 10 other machine learning algorithms was evaluated on All-cause in-hospital mortality predicted at the 24-hour ICU landmark (AUC 0.810 (internal) / AUC 0.704 (external), 95% CI 0.757-0.860 (internal) / 0.679-0.729 (external)). An artificial neural network model predicted in-hospital mortality in ICU cardiorenal syndrome patients with an internal AUC of 0.810 (95% CI 0.757-0.860) and external AUC of 0.704 (95% CI 0.679-0.729).
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