Predicting ICU Transfer and Short-term Mortality in Emergency Department Atrial Fibrillation Patients: An Enhanced Machine Learning Model Using MIMIC Data

Atrial fibrillation (AF) is a prevalent condition in emergency department (ED) patients and is associated with an elevated risk of intensive care unit (ICU) transfer and short-term mortality. Early identification of high-risk patients is critical for timely intervention and improved clinical outcomes. We constructed a combined cohort from the MIMIC-IV-ED and MIMIC-IV databases, comprising ED admissions of patients with AF, and developed an interpretable machine learning (ML) framework to predict ICU transfer and mortality within 3, 7, and 30 days using clinical variables obtained at triage. The preprocessing pipeline included imputation of missing data, z-score normalization of numerical features, one-hot encoding of categorical variables, and correction for class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE). A hybrid feature selection strategy combining Recursive Feature Elimination with Cross-Validation (RFECV) and Least Absolute Shrinkage and Selection Operator (LASSO) regression reduced the initial set to 19 clinically relevant predictors. Among six evaluated machine learning algorithms, LightGBM demonstrated the highest performance for ICU transfer (AUROC = 0. 7979, 95\% confidence interval (CI): 0. 7916–0. 8041) and for 7- and 30-day mortality (AUROC = 0. 8316, 95\% CI: 0. 8156–0. 8476; AUROC = 0. 8010, 95\% CI: 0. 7898–0. 8123), while CatBoost achieved the best performance for 3-day mortality (AUROC = 0. 8444, 95\% CI: 0. 8237–0. 8644). SHAP (SHapley Additive exPlanations) analysis identified O2sat, acuity, and resprate as key determinants, underscoring the clinical plausibility and interpretability of the models. These findings highlight the potential of interpretable machine learning approaches to enable early, time-sensitive risk stratification and support informed clinical decision-making for AF patients in the ED.