Survival Forest Models for ICU Mortality Prediction based on Nutrition and Clinical Factors

Objective: This study investigates the predictive role of nutritional factors and gastrointestinal dysfunction in determining mortality among critically ill patients admitted to the intensive care unit (ICU). Our goal is to enhance mortality prediction by incorporating detailed nutritional and clinical data collected during the first 48 hours following ICU admission. Methods: The study included data from 2,942 patients admitted to Beilinson Hospital ICU. Variables collected comprised nutritional measures, gastrointestinal dysfunction symptoms, patient demographics, vital signs, respiratory parameters, and laboratory results. For each clinical variable, per-patient 48-hour summary statistics were derived. Three feature selection strategies were compared: stepwise Cox proportional hazards modeling, Random Forest feature importance, and Principal Component Analysis (PCA). For each feature set, we developed Random Survival Forest (RSF) models and evaluated predictive performance using six-fold cross-validation. Performance metrics included Concordance Index (CI), Akaike Information Criterion (AIC), time-dependent AUC at 10 days, and Integrated Brier Score (IBS≤10 days). Results: The stepwise-selected feature set produced the most accurate model (CI=0.76) and achieved the strongest overall performance across complementary metrics (AUC_10d=0.874, IBS≤10d=0.049). The Random Forest–selected feature set performed closely behind (CI=0.747; AUC_10d=0.872; IBS≤10d=0.050). PCA-based feature reduction resulted in substantially poorer discrimination (CI=0.73) and higher prediction error (AUC_10d=0.715; IBS≤10d=0.068). Across approaches, nutritional and gastrointestinal (GI) dysfunction variables, including nasogastric output, feeding tolerance, and timing of enteral nutrition, emerged as important predictors of early mortality. Conclusions: Our findings underscore the critical importance of early nutritional intervention and GI dysfunction management for ICU patient survival. The study demonstrates that targeted feature selection, which incorporates clinical insight, yields superior discriminative performance compared with purely data-driven reduction methods. Future research should validate these models externally and integrate real-time monitoring data to further refine clinical decision-making and improve patient outcomes in ICU settings.