Journal Article

Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant

Jain, Sandeep, Bliden, Kevin P., Gurbel, Paul A., Bhonsale, Aditya, Saba, Samir, Harinstein, Matthew E., Visweswaran, Shyam, Chaudhary, Rahul, Thoma, Floyd W., Gellad, Walid F., Wang, Yanshan, Neal, Matthew D., Nourelahi, Mehdi, Mulukutla, Suresh R., Dua, Anahita, Lo-Ciganic, Wei-Hsuan, Chaudhary, Rohit

2025

Overview

•ML models outperformed conventional scores in predicting major bleeding in AF.•Random forest achieved an AUC of 0.76 vs HAS-BLED's AUC of 0.57 (p < 0.001).•SHAP analysis identified new bleeding risk factors like BMI and cholesterol profile.•Study included 24,468 AF patients on DOACs with a 5-year follow-up for bleeding events.•ML models offer more personalized bleeding risk assessment for AF patients on DOACs. Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized care. Alternatives like left atrial appendage closure devices lower stroke risk with fewer nonprocedural bleeds. This study compares machine learning (ML) models with conventional bleeding risk scores (HAS-BLED, ORBIT, and ATRIA) for predicting bleeding events requiring hospitalization in AF patients on DOACs at their index cardiologist visit. This retrospective cohort study used electronic health records from 2010 to 2022 at the University of Pittsburgh Medical Center. It included 24,468 nonvalvular AF patients (age ≥18) on DOACs, excluding those with prior significant bleeding or warfarin use. The primary outcome was hospitalization for bleeding within one year, with follow-up at one, two, and five years. ML algorithms (logistic regression, classification trees, random forest, XGBoost, k-nearest neighbor, naïve Bayes) were compared for performance. Of 24,468 patients, 553 (2.3%) had bleeding within one year, 829 (3.5%) within two years, and 1,292 (5.8%) within five years. ML models outperformed HAS-BLED, ATRIA, and ORBIT in 1-year predictions. The random forest model achieved an AUC of 0.76 (0.70 to 0.81), G-Mean of 0.67, and net reclassification index of 0.14 compared to HAS-BLED's AUC of 0.57 (p < 0.001). ML models showed superior results across all timepoints and for hemorrhagic stroke. SHAP analysis identified new risk factors, including BMI, cholesterol profile, and insurance type. In conclusion, ML models demonstrated improved performance to conventional bleeding risk scores and uncovered novel risk factors, offering potential for more personalized bleeding risk assessment in AF patients on DOACs.

• Publisher: Elsevier Inc,Elsevier Limited
• Subject: Administration, Oral / Aged / Algorithms / Anticoagulants / Anticoagulants - administration & dosage / Anticoagulants - adverse effects / atrial fibrillation / Atrial Fibrillation - complications / Atrial Fibrillation - drug therapy / Bleeding / Cardiac arrhythmia / Cardiology / Cholesterol / Clinical significance / Cohort analysis / Comorbidity / Customization / Data collection / direct oral anticoagulants / Electronic health records / Electronic medical records / Fatalities / Female / Fibrillation / Health care facilities / Health risks / Hemorrhage / Hemorrhage - chemically induced / Hemorrhage - epidemiology / hemorrhagic stroke / Hospitalization - statistics & numerical data / Humans / Learning algorithms / Machine Learning / major bleeding / Male / Middle Aged / Precision medicine / Retrospective Studies / Risk assessment / Risk Assessment - methods / Risk Factors / risk prediction / Stroke / Stroke - etiology / Stroke - prevention & control / Variables