Interpretable Machine Learning Model for Early Prediction of 30-Day Mortality in ICU Patients With Coexisting Hypertension and Atrial Fibrillation: A Retrospective Cohort Study

Abstract

Hypertension and atrial fibrillation (AF) often coexist in critically ill patients, significantly increasing mortality rates in the ICU. Early identification of high-risk individuals is crucial for targeted interventions. However, limited research has focused on short-term mortality prediction for this subgroup. This study analyzed 1,301 adult ICU patients with hypertension and AF from the MIMIC-IV database. Data including chart events, laboratory results, procedures, medications, and demographic information from the first 24 hours of ICU admission were extracted. After quality control, missing data imputation, and feature selection, 17 clinically relevant variables were retained. The cohort was split into training (70%) and test (30%) sets, with outcome-weighted training applied to address class imbalance. The CatBoost model, along with five baseline models (LightGBM, XGBoost, logistic regression, Naive Bayes, and neural networks), was evaluated using five-fold cross-validation, with AUROC as the primary performance metric. Model interpretability was assessed using SHAP, ALE, and DREAM analyses. The CatBoost model showed strong performance with an AUROC of 0.889 (95% CI: 0.840-0.924), accuracy of 0.831, and sensitivity of 0.837. Key predictors identified by SHAP and other methods included the Richmond-RAS Scale, pO2, CefePIME, and Invasive Ventilation, demonstrating the model's robustness and clinical applicability. This model shows strong performance and interpretability in early mortality prediction, enabling early intervention and personalized care decisions. Future work will involve multi-center validation and extending the approach to other diseases.