230 Machine Learning Models for Predicting Ischemic Stroke and Major Bleeding Risk in Patients with Atrial Fibrillation

Abstract

Background Risk scores such as CHA2DS2-VASc and HAS-BLED are used to assess stroke and bleeding risk respectively and choose appropriate antithrombotic therapy in patients with atrial fibrillation (AF). The application of ML models may improve risk prediction and identification of potential risk factors. Objective To investigate the usefulness of ML methods in estimating one-year risk of ischemic stroke and major bleeding in patients after hospitalisation with AF. Methods We identified adults with a history of non-valvular AF or atrial flutter who were admitted to a tertiary or secondary hospital in Perth, Western Australia from 2009 to 2016 using linked clinical and administrative data. Based on all the available risk factors in the data including individual risk factors in the scores, we built ML models and compared their predictive performance [Area under the receiver operating characteristic curve (AUC)] with the standard risk scores. Results There were 9,634 patients in the study cohort with a mean age of 77 years and 46% were female. 2407 patients died (n=1636) or were readmitted for ischemic stroke (n=157) and major bleeding (n=614) within one year after the first admission. All-cause death was treated as a competing risk. Gradient Boosting Machine identified nonconventional risk factors and achieved the best prediction (ischemic stroke: AUC 0.67 vs 0.64 for CHA2DS2-VASc; major bleeding: AUC 0.66 vs 0.53 for HAS-BLED). Conclusion ML models can identify nonconventional risk factors and also outperform commonly used risk scores for predicting ischemic stroke and major bleeding in patients with AF.