Predicting multifaceted risks using machine learning in Atrial Fibrillation: Insights from GLORIA-AF study

Patients with atrial fibrillation (AF) have a higher risk of ischaemic stroke and death. While anticoagulants are effective at reducing these risks, they increase the risk of bleeding. Current clinical risk scores only perform modestly in predicting adverse outcomes, especially for the outcome of death. We aimed to test the multi-label gradient boosting decision tree (ML-GBDT) model in predicting risks for adverse outcomes in a prospective global AF registry. We studied patients from phase II/III of the GLORIA-AF registry between 2011 and 2020. The outcomes were all-cause death, ischaemic stroke and major bleeding within one year following the AF. We trained the ML-GBDT model and compared its discrimination with the clinical scores in predicting patient outcomes. A total of 25,656 patients were included (mean age 70.3 years (SD 10.3); 44.8% female). Within one-year after AF, ischaemic stroke occurred in 215 (0.8%), major bleeding in 405 (1.6%), and death in 897 (3.5%) patients. Our model achieved an optimized area under the curve (AUC) in predicting death (0.785, 95% CI: 0.757-0.813) compared to Charlson Comorbidity Index (0.747, p=0.007), ischaemic stroke (0.691, 0.626-0.756) comparing to CHA2DS­2-VASc (0.613, p=0.028), and major bleeding (0.698, 0.651-0.745) as opposed to HAS-BLED (0.607, p=0.002), with improvement in net reclassification index (10.0%, 12.5% and 23.6% respectively). The ML-GBDT model outperformed clinical risk scores in predicting the risks in patients with AF. This approach could be used as a single multifaceted holistic tool to optimize patient risk assessment and mitigate adverse outcomes when managing AF.