Dynamic prediction models for thromboembolic and major bleeding risks in DOAC users outperformed CHA2DS2-VASc (C-index difference 0.051; 95% CI 0.038-0.062) and HAS-BLED (difference 0.070).
Cohort (n=42,450)
Yes
Do dynamic prediction models incorporating time-dependent variables improve the prediction of thromboembolic and major bleeding risks compared to CHA2DS2-VASc and HAS-BLED in patients with atrial fibrillation initiating DOACs?
Dynamic prediction models incorporating time-dependent variables like DOAC adherence offer superior performance to static scores (CHA2DS2-VASc and HAS-BLED) for reassessing thromboembolic and bleeding risks in AF patients.
Effect estimate: C-index difference 0.051 (95% CI 0.038-0.062)
Background Current tools for assessing ischemic stroke and bleeding risks in patients with atrial fibrillation focus on warfarin users and initial therapy decisions; however, dynamic models for reassessing these risks in direct oral anticoagulant (DOAC) users are lacking. We aimed to develop a tool that enables the dynamic evaluation of thromboembolic and bleeding risks in patients with atrial fibrillation receiving DOAC therapy. Methods Using a national claims database, we included 42 450 patients with atrial fibrillation who were newly prescribed DOACs and divided them into development (2018) and temporal‐validation (2019) cohorts. We applied a landmarking approach and developed dynamic prediction models incorporating time‐dependent variables. The Fine–Gray subdistribution hazard model, which accounts for death as a competing risk, was used to derive these models. Results Dynamic prediction models incorporating time‐dependent variables, such as DOAC adherence, were developed to estimate the 1‐year thromboembolic and major bleeding risks over a 2‐year period following DOAC initiation. The thromboembolic and major bleeding risk prediction models achieved a concordance index of 0.715 (95% CI, 0.701–0.729), and 0.697 (95% CI, 0.682–0.710), respectively. The thromboembolic model outperformed CHA 2 DS 2 ‐VASc (concordance index, 0.664 95% CI, 0.652–0.678) by 0.051 (95% CI, 0.038–0.062), and the major bleeding model outperformed HAS‐BLED (concordance index, 0.627 95% CI, 0.611–0.641) by 0.070 (95% CI, 0.055–0.084). Calibration plots showed good alignment between the predicted and observed probabilities. Nomograms were constructed for each model to enhance its clinical utility. Conclusions These dynamic prediction models offer a novel approach for reassessing thromboembolic and major bleeding risks in patients with atrial fibrillation receiving DOACs, demonstrating superior performance compared with existing models. Enabling regular risk updates based on the patient’s current status, these tools support personalized and dynamic risk assessments in clinical practice.
Heo et al. (Tue,) conducted a cohort in Atrial fibrillation (n=42,450). Dynamic prediction models vs. CHA2DS2-VASc and HAS-BLED models was evaluated on 1-year thromboembolic and major bleeding risks (C-index difference 0.051, 95% CI 0.038-0.062). Dynamic prediction models for thromboembolic and major bleeding risks in DOAC users outperformed CHA2DS2-VASc (C-index difference 0.051; 95% CI 0.038-0.062) and HAS-BLED (difference 0.070).