Key result
Dynamic risk models outperform static scores for predicting AF stroke risk by ~0.05 C-index points.
Why the study?
Risk tools focus on warfarin and initial therapy decisions, but dynamic models for reassessing thromboembolic and bleeding risks in DOAC users are lacking.
Does a dynamic prediction model incorporating time-dependent variables improve prediction of thromboembolic and major bleeding risks compared to conventional static scores in patients with atrial fibrillation receiving DOACs?
Cohort (n=42,450)
Yes
Does a dynamic prediction model incorporating time-dependent variables improve prediction of thromboembolic and major bleeding risks compared to conventional static scores in patients with atrial fibrillation receiving DOACs?
A dynamic risk prediction model incorporating time-dependent clinical changes and adherence significantly improved accuracy for predicting 1-year thromboembolic and bleeding risks compared with static CHA2DS2-VASc and HAS-BLED scores in AF patients on DOACs.
Answers are grounded in this paper and cite their sources.
Dynamic models may enhance AF risk stratification on DOACs; leaves open prospective validation and outcome impact before practice change.
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.
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Heo et al. (2025) conducted a cohort in Atrial fibrillation (n=42,450). Dynamic prediction model incorporating time-dependent variables vs. Static prediction models (CHA2DS2-VASc and HAS-BLED) was evaluated on 1-year thromboembolic risk prediction (Concordance index) (Concordance index difference 0.051, 95% CI 0.038-0.062). Dynamic prediction models outperformed static CHA2DS2-VASc and HAS-BLED scores for predicting thromboembolic and major bleeding risks by a concordance index of 0.051 and 0.070, respectively.
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