Estimating Vitamin K Antagonist Anticoagulation Benefit in People With Atrial Fibrillation Accounting for Competing Risks: Evidence From 12 Randomized Trials
Bibliographic record
Abstract
BACKGROUND: Patients with atrial fibrillation have a high mortality rate that is only partially attributable to vascular outcomes. The competing risk of death may affect the expected anticoagulant benefit. We determined if competing risks materially affect the guideline-endorsed estimate of anticoagulant benefit. METHODS: We conducted a secondary analysis of 12 randomized controlled trials that randomized patients with atrial fibrillation to vitamin K antagonists (VKAs) or either placebo or antiplatelets. For each participant, we estimated the absolute risk reduction (ARR) of VKAs to prevent stroke or systemic embolism using 2 methods—first 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 nonlinear growth in benefit. We compared the absolute and relative differences in estimated benefit and whether the differences varied by life expectancy. RESULTS: A total of 7933 participants (median age, 73 years, 36% women) had a median life expectancy of 8 years (interquartile range, 6–12), determined by comorbidity-adjusted life tables and 43% were randomized to VKAs. The CHA 2 DS 2 -VASc model estimated a larger ARR than the competing risk model (median ARR at 3 years, 6.9% [interquartile range, 4.7%–10.0%] versus 5.2% [interquartile range, 3.5%–7.4%]; P <0.001). 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 −1.3% (95% CI, −1.3% to −1.2%); for those with life expectancies in the lowest decile, 3-year ARR difference was 4.7% (95% CI, 4.5%–5.0%). CONCLUSIONS: VKA anticoagulants were exceptionally effective at reducing stroke risk. However, VKA 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 when life expectancy was low and when the benefit was estimated over a multiyear horizon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".