Estimating Vitamin K Antagonist Anticoagulation Benefit in People With Atrial Fibrillation Accounting for Competing Risks: Evidence From 12 Randomized Trials
Bibliographic record
Abstract
ABSTRACT Background Patients with atrial fibrillation (AF) have a high rate of all-cause mortality that is only partially attributable to vascular outcomes. While the competing risk of death may affect expected anticoagulant benefit, guidelines do not account for it. We sought to determine if using a competing risks framework materially affects the guideline-endorsed estimate of absolute risk reduction attributable to anticoagulants. Methods We conducted a secondary analysis of 12 RCTs that randomized patients with AF to oral anticoagulants or either placebo or antiplatelets. For each participant, we estimated the absolute risk reduction (ARR) of anticoagulants to prevent stroke or systemic embolism using two methods. First, we estimated the ARR using a guideline-endorsed model (CHA 2 DS 2 -VASc) and then again using a Competing Risk Model that uses the same inputs as CHA 2 DS 2 -VASc but accounts for the competing risk of death and allows for non-linear growth in benefit over time. We compared the absolute and relative differences in estimated benefit and whether the differences in estimated benefit varied by life expectancy. Results 7933 participants had a median life expectancy of 8 years (IQR 6, 12), determined by comorbidity-adjusted life tables. 43% were randomized to oral anticoagulation (median age 73 years, 36% women). The guideline-endorsed CHA 2 DS 2 -VASc model estimated a larger ARR than the Competing Risk Model (median ARR at 3 years, 6.9% vs. 5.2%). ARR differences varied by life expectancies: for those with life expectancies in the highest decile, 3-year ARR difference (CHA 2 DS 2 -VASc model – Competing Risk Model 3-year risk) was −1.2% (42% relative underestimation); for those with life expectancies in the lowest decile, 3-year ARR difference was 5.9% (91% relative overestimation). Conclusion Anticoagulants were exceptionally effective at reduced stroke risk. However, anticoagulant benefits were misestimated with CHA 2 DS 2 -VASc, which does not account for the competing risk of death nor decelerating treatment benefit over time. Overestimation was most pronounced in patients with the lowest life expectancy and when benefit was estimated over a multi-year horizon.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.078 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.028 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".