Abstract 9885: Overestimation of Anticoagulant Benefit in Patients with Atrial Fibrillation and Low Life Expectancy: Evidence from 12 Randomized Trials
Bibliographic record
Abstract
Introduction: Formative anticoagulant RCTs for stroke prevention in atrial fibrillation (AF) were completed before the advent of competing risk methods. Neglecting the competing risk of death can overestimate treatment benefit. This is noteworthy because many patients with AF are frail with limited life expectancy. We determined the overestimation of anticoagulant benefit as a function of life expectancy. Methods: We used patient-level data from 12 anticoagulant RCTs that compared warfarin vs. aspirin or placebo. We estimated each patient’s life expectancy using CDC life tables modified for age, sex, comorbidities, and trial enrollment year. We predicted cumulative stroke incidence with and without anticoagulants at 3 years using a Cox model treating death as a censoring event (“standard model”) and again using a Fine-Gray model treating death as a competing event (“competing risk model”). For each patient, we plot the absolute and relative standard model overestimation compared to the competing risk model. We describe the relationship between life expectancy and log-transformed benefit overestimation (Figure A, B). Results: For the 9548 patients (35% women, mean age 74yrs), the median life expectancy was 11.4yrs (IQR 8.4, 15.3). The overall estimated reduction in stroke incidence at 3 years was 7.7% using a standard model and 6.3% using a competing risk model. The absolute and relative overestimation of the standard model increased exponentially as life expectancy decreased. For example, with a life expectancy of 3 years, a standard model would overestimate absolute benefit by 2.8% (95%CI 2.7-2.9%) and the relative benefit by 29.4% (95%CI 29.3-29.4%). Conclusions: For patients with low life expectancy, the anticoagulant benefit is substantially overestimated using standard survival methods. While anticoagulants reduce the risk of stroke, decision tools should account for life expectancy when estimating benefits for patients with AF.
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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 | 0.058 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".