Interpretable machine learning for early in-hospital mortality prediction in intensive care unit patients with acute upper gastrointestinal bleeding: Development and multicenter external validation.

Background

Early mortality risk stratification remains challenging in intensive care unit (ICU) patients with acute upper gastrointestinal bleeding (AUGIB). We developed a parsimonious, interpretable ensemble model and evaluated its performance across independent ICU cohorts.

Methods

MIMIC-IV was used for model development and internal validation (n=3,728), and eICU-CRD (n=7,529) and a Hainan cohort (n=200) were used for external validation. Adults with ICU stays of at least 24 hours were included. Subsequent all-cause in-hospital mortality was predicted from routinely available data collected during the first 24 hours after ICU admission. Within MIMIC-IV, predictors were prioritized according to multi-algorithm ranking and clinical feasibility, and a 17-feature subset was selected using a performance-parsimony criterion supported by knee-point analysis. Nine machine-learning algorithms were evaluated, and four complementary learners were combined using equal-weight soft voting based on cross-validated performance and prediction complementarity. Model evaluation included AUROC, PR-AUC, Brier score, calibration, and decision-curve analysis. Ensemble-level SHAP and pathway analyses supported model interpretation.

Results

AUGIB Soft-Voting Ensemble (AUGIB-SVE) achieved AUROCs of 0.847, 0.837, and 0.848 in MIMIC-IV, eICU, and Hainan validation, respectively. It also achieved higher AUROCs than AIMS65, pre-endoscopic Rockall, SOFA, and SAPS II in the cohorts in which these scores could be reconstructed. Decision-curve analysis showed positive net benefit across clinically relevant threshold ranges. SHAP and pathway-support analyses linked influential predictors to four clinically coherent biological axes: hemorrhage/perfusion, coagulation dysfunction, inflammation/hypoxia, and multiorgan dysfunction. AUGIB-SVE was deployed as an online risk-prediction tool.

Conclusions

AUGIB-SVE provides a parsimonious and interpretable framework for early in-hospital mortality risk stratification in ICU patients with AUGIB. It showed consistent discrimination across independent cohorts, favorable clinical utility, and higher discrimination than reconstructable conventional scores. TRIAL REGISTRATION Not applicable.