Temporal Clinical Features for 24-Hour Landmark Prediction of In-Hospital Mortality in ICU Patients With Diabetic Neuropathy: A MIMIC-IV Study

Diabetic neuropathy is associated with substantial systemic disease burden, but short-term mortality risk among affected intensive care unit (ICU) patients remains difficult to characterize. We evaluated whether temporal information from the first 24 hours of ICU care improves post-landmark mortality prediction beyond severity scores and static clinical summaries. Patients aged > 18 years with diabetic neuropathy were identified in MIMIC-IV v3.1. A 24-hour landmark was used: only patients alive and still hospitalized at 24 hours were included, and the outcome was subsequent in-hospital death. The final cohort included 1,347 patients, including 83 deaths (6.16%). Data were divided into an 80% development set and a locked 20% test set. Feature selection, hyperparameter tuning, calibration, and threshold selection were restricted to development data. Logistic regression, random forest, and XGBoost were evaluated. Random forest had the highest development cross-validated PR-AUC and was selected for interpretation. On the locked test set, random forest achieved an AUROC of 0.851 (95% CI 0.765–0.924), PR-AUC of 0.339, and Brier score of 0.051; XGBoost and logistic regression achieved AUROCs of 0.847 and 0.806. In a post hoc strictly nested analysis, adding temporal predictors increased discrimination across all three algorithms; random-forest AUROC increased from 0.815 with severity and static predictors to 0.870 with the full temporal representation. First-day temporal information therefore showed additional prognostic value, but external validation is required before clinical use.