Abstract 210: Stroke-Related Specific Mortality and Influencing Factors in Patients with Atrial Fibrillation in the RE-LY Trial
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is associated with increased mortality, but the specific causes of death and their predictors have not been described. Methods and results: The Randomized Evaluation of Long-Term Anticoagulant Therapy (RE-LY) trial randomized 18,113 patients (71.5 ± 9 years, 64% males, CHADS 2 score 2.1 ± 1) to receive dabigatran or warfarin. Median follow-up was 2 years and complete follow-up was achieved in 99.9% of patients. All deaths were categorized by the investigators using pre-specified definitions followed by central adjudication. Overall, 1,371 deaths occurred (annual mortality rate of 3.84%, 95% CI 3.64-4.05). Cardiovascular death (sudden cardiac death [SCD] and progressive heart failure) accounted for 37.4% of all deaths, whereas stroke and hemorrhage-related represented only 9.8% of the total mortality. When examining causes of death according to dabigatran or warfarin, dabigatran only significantly affected stroke/hemorrhage-related mortality (RR 0.71, 95% CI 0.51-0.98, P = 0.04). The strongest independent predictor of cardiovascular death was heart failure (HR 2.45, 95% CI 2.10-2.88, P < 0.0001), of stroke-related deaths was non-fatal major hemorrhage (HR 4.35, 95% CI 2.58-7.34, P < 0.0001) and of hemorrhage-related deaths was non-fatal stroke (HR 3.80, 95% CI 1.07-13.5, P = 0.039). Conclusions: Among AF patients under antithrombotics, the majority of death (up to 90%) is due to causes other than stroke and bleeding. These results emphasize the need to identify interventions other than effective anticoagulation, which target SCD and progressive heart failure deaths in order to substantially reduce overall mortality, in AF patients. Clinical trial registration: ( http://www.clinicaltrials.gov ): Unique identifier NCT00262600 .
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".