Why the study?
Early risk stratification for in-hospital mortality remains challenging in ICU patients with HF due to clinical heterogeneity and complex pathophysiological interactions, which explainable machine learning may help address.
Does an explainable XGBoost-based machine learning model accurately predict in-hospital all-cause mortality in ICU patients with heart failure?
Population
18,526 development and 314 external validation ICU patients with HF
Comparison
Five machine learning algorithms compared for predicting in-hospital mortality
Design
Retrospective dual-cohort study
Key result
An explainable XGBoost machine learning model using nine routinely available clinical variables predicted in-hospital all-cause mortality with an AUC of 0.877 in an external validation cohort of ICU patients with heart failure.
Authors
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May support ML risk triage in ICU heart failure; leaves open need for prospective outcome trials.
Cohort (n=18,840)
Yes
Does an explainable XGBoost-based machine learning model accurately predict in-hospital all-cause mortality in ICU patients with heart failure?
Effect estimate: AUC 0.877 (95% CI 0.820-0.933)
An explainable XGBoost machine learning model using nine routine clinical variables demonstrated strong discrimination for predicting in-hospital mortality in ICU patients with heart failure, potentially aiding early risk stratification.
Xu et al. (2026) conducted a cohort in Heart failure in intensive care unit patients (n=18,840). XGBoost machine learning model vs. Other predictive models (SOFA, APS III, etc.) was evaluated on In-hospital all-cause mortality prediction (AUC in external validation) (AUC 0.877, 95% CI 0.820-0.933). An explainable XGBoost machine learning model using nine routinely available clinical variables predicted in-hospital all-cause mortality with an AUC of 0.877 in an external validation cohort of ICU patients with heart failure.