Interpretable machine learning prediction of in-hospital mortality in ICU patients with cancer and sepsis using first-day data: Development using MIMIC-IV and external validation in eICU-CRD

Background: Critically ill patients with cancer and sepsis have high in-hospital mortality, but externally validated prediction models are limited. Objective: To develop and externally validate an interpretable machine learning framework using first-day intensive care data. Methods: We used MIMIC-IV version 3.1 for development and internal validation and eICU-CRD for external validation. Eligible adults had cancer, an intensive care unit stay of at least 24 hours, and met a prespecified operational sepsis definition. The prediction landmark was 24 hours after admission. The MIMIC-IV cohort included 3,729 stays (training, n = 2, 983; internal validation, n = 746). Same-admission diagnosis-derived variables were excluded, and 345 predictors were retained. Fourteen predictive models and a dummy baseline were evaluated. Frozen pipelines and training-derived thresholds were applied to eICU-CRD without refitting or recalibration. Results: Gradient boosting was selected as the primary model and achieved an internal AUROC of 0.8480 (95% CI, 0.8173-0.8755), AUPRC of 0.6984, and Brier score of 0.1346. Important predictors included Glasgow Coma Scale components, temperature, lactate dehydrogenase, age, respiratory rate, oxygen saturation, blood urea nitrogen, and serum lactate. In eICU-CRD (n = 611), gradient boosting achieved an AUROC of 0.7483 (95% CI, 0.7026-0.7919), AUPRC of 0.6243, and Brier score of 0.1709. Random forest had the highest external AUROC in secondary comparisons (0.7731). Conclusions: First-day data supported useful internal discrimination, but performance declined under locked external validation. Multicenter validation, recalibration, threshold assessment, and prospective evaluation are required before clinical implementation.