Random Forest-Driven Mortality Prediction in Critical IBD Care: A Dual-Database Model Integrating Comorbidity Patterns and Real-Time Physiometrics

Zhenze Zhang¹Caiqing Zhao¹Junyi Zhou²Ling Yao³Peng Liu³Ziling Fang^4*Nian Fang^1,3*

• ¹Nanchang University, Jiangxi Medical College, Clinical Graduate School, Nanchang, China
• ²Jiangxi University of Traditional Chinese Medicine, Clinical Graduate School, Nanchang, China
• ³Nanchang University, the 3st affiliated hospital(The First Hospital of Nanchang), Nanchang, China
• ⁴Nanchang University, the 1st affiliated hospital, Nanchang, China

Background Inflammatory bowel disease (IBD) poses significant mortality risks for critically ill patients requiring intensive care unit (ICU) admission, driven by complications such as malnutrition, thromboembolism, and multi-organ dysfunction. Current prognostic tools for mortality prediction in this population remain limited. Machine learning (ML) offers advantages in handling complex clinical data but has not been systematically applied to this high-risk cohort. This multicenter study aimed to develop and validate ML-based models for mortality risk stratification in critically ill IBD patients using large-scale ICU databases. Methods Data from 551 IBD patients in the MIMIC-IV database (2008–2019) were analyzed, with external validation using the eICU dataset. Nine ML algorithms (XGBoost, logistic regression, LightGBM, random forest, decision tree, elastic net, MLP, KNN, RSVM) were trained to predict 1-year mortality. Predictors included demographics, comorbidities, laboratory parameters, vital signs, and disease severity scores. Missing data (<30%) were imputed using random forest. The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. Model performance was evaluated using AUC, sensitivity, specificity, and calibration curves. The SHAP framework was integrated with predictive analytics to systematically evaluate key determinants of mortality risk through quantitative feature importance analysis. A nomogram was constructed based on key predictors identified through logistic regression. Results The random forest model achieved superior discrimination in internal validation (AUC >0.8). Seven predictors were identified: malignancy history, Charlson Comorbidity Index (CCI), Red Cell Distribution Width (Rdw), Glasgow Coma Scale (GCS), Sequential Organ Failure Assessment (Sofa), age, heart rate, weight and gender. The nomogram demonstrated robust external validation performance in the eICU cohort (AUC >0.8). Conclusion We developed and validated a machine learning-based nomogram to predict mortality in critically ill IBD patients, integrating interpretable predictors from multicenter ICU data.