  Appropriate antithrombotic therapy for patients with atrial fibrillation (AF)
requires assessment of ischemic stroke and bleeding risks. However, risk
stratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictive
capacity for patients with AF. Machine learning (ML) techniques may improve
predictive performance and support decision-making for appropriate
antithrombotic therapy. We compared the performance of multilabel ML models
with the currently used risk scores for predicting outcomes in AF patients.
Materials and Methods This was a retrospective cohort study of 9670 patients,
mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, and
had 1-year follow-up. The primary outcome was ischemic stroke and major
bleeding admission. The secondary outcomes were all-cause death and event-free
survival. The discriminant power of ML models was compared with clinical risk
scores by the area under the curve (AUC). Risk stratification was assessed
using the net reclassification index. Results Multilabel gradient boosting
machine provided the best discriminant power for stroke, major bleeding, and
death (AUC = 0.685, 0.709, and 0.765 respectively) compared to other ML models.
It provided modest performance improvement for stroke compared to CHA2DS2-VASc
(AUC = 0.652), but significantly improved major bleeding prediction compared to
HAS-BLED (AUC = 0.522). It also had a much greater discriminant power for death
compared with CHA2DS2-VASc (AUC = 0.606). Also, models identified additional
risk features (such as hemoglobin level, renal function, etc.) for each
outcome. Conclusions Multilabel ML models can outperform clinical risk
stratification scores for predicting the risk of major bleeding and death in
non-valvular AF patients.
