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

Abstract

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.

Keywords: atrial fibrillation; direct oral anticoagulants; hemorrhagic stroke; machine learning; major bleeding; risk prediction.