ArticleBMC cardiovascular disorders2025
Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the CatBoost model.
Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Machine Learning Platform for Imputing Missing Critical Quality Attributes in Pharmaceutical Development.The AAPS journal · 2026Article
- Low atherogenic index of plasma is associated with spontaneous hemorrhagic transformation in acute ischemic stroke patients: a retrospective cohort study and predictive modeling based on machine learning.Lipids in health and disease · 2026Article
- Machine Learning-Identified Potential Interaction Between Clazosentan and Nicardipine in Patients with Subarachnoid Hemorrhage.Journal of clinical medicine · 2026Article
- Integrated Care in Patients with Atrial Fibrillation and Optimal Medical Treatment for Heart Failure: Results from the Heart failuRe ObsErvational Study (HEROES).Journal of clinical medicine · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundHeart 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.
methodThis 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.
resultsOf 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.
conclusionsCatBoost 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.
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