Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the...

Heart failure and atrial fibrillation (HF-AF) frequently coexist, resulting in complex interactions that substantially elevate mortality risk. This study aimed to develop and validate a machine learning (ML) model predicting the 3-year all-cause mortality risk in HF-AF patients to support personalized risk stratification and management.

This retrospective cohort study included 558 HF-AF patients admitted in 2018, with a median follow-up duration of 1,185 days. The cohort was randomly divided into training (70%) and test (30%) sets. Feature selection utilized the Boruta algorithm and least absolute shrinkage and selection operator regression. Six ML models were trained using tenfold cross-validation and optimized via grid search. Model performance was evaluated across 12 metrics, including the area under the receiver operating characteristic curve (AUC), to identify the best-performing model. Subsequently, Shapley Additive exPlanations (SHAP) analysis was used to interpret the optimal model and investigate interactions between features.

Of the 558 patients, 215 reached the primary endpoint. Feature selection identified 14 key variables for model development. The best-performing model, CatBoost, achieved the highest AUC (0.809) and demonstrated robust performance across multiple evaluation metrics. SHAP analysis highlighted the New York Heart Association (NYHA) classification, absolute lymphocyte count (ALC), high-sensitivity C-reactive protein, B-type natriuretic peptide (BNP), and age as key predictors. SHAP interaction analysis identified several feature interactions, with relatively strong ones observed between ALC and NYHA classification, and ALC and BNP.

CatBoost was identified as the optimal model for predicting three-year all-cause mortality in HF-AF patients, potentially aiding clinicians in risk stratification and individualized treatment planning to improve patient outcomes.