Optimally Predicting Mortality in Patients with Abdominal Aortic Aneurysms

Abdominal aortic aneurysm (AAA) patients in the intensive care unit (ICU) form a high-risk population whose mortality risk evolves across clinical phases. Existing tools rely largely on Cox proportional hazards nomograms with narrow predictor sets and single horizons, leaving the value of machine learning, extended features, and external generalizability uncharacterized. We extracted an ICD-coded AAA ICU cohort from MIMIC-IV v2.2 (858 patients with complete six-predictor admission data) using a 24-hour window. An extended set added hemodynamic, laboratory, and comorbidity variables via LASSO and support vector machine recursive feature elimination. Six models (Cox proportional hazards, logistic regression, random forest, gradient boosting, XGBoost, multilayer perceptron) were trained on a 70% split and evaluated at 7, 14, and 28 days by discrimination, calibration, and Shapley additive explanations, with external validation on a harmonized eICU-CRD cohort. In-hospital mortality was 11.8%. Logistic regression led on the six-predictor set (7-day area under the curve 0.866; 14-day 0.872); random forest achieved the best 28-day extended-set value (0.892). The multilayer perceptron underperformed throughout; external validation attenuated as expected (best 7-day 0.771). Anion gap, blood urea nitrogen, and age were the leading contributors. Regularized models excel under data scarcity, while tree ensembles gain advantage as features and horizons expand.