Abstract 10284: Alcohol Consumption and Cardiovascular Outcomes in Patients With Atrial Fibrillation; a RE-LY AF Registry Analysis
Bibliographic record
Abstract
Introduction: The role of alcohol intake in patients with atrial fibrillation (AF) has been long debated. We aim to assess the association between different alcohol intake levels and cardiovascular outcomes in patients with AF. Methods: We performed a cross-sectional analysis of the RE-LY AF registry, which included 15,400 AF patients who visited emergency departments across 46 countries and were followed for one year after the baseline visit. Alcohol intake level was retrospectively ascertained and categorized into abstainers, light (<7 standard drinks [SD]/week), moderate (7-13 SD/week), and heavy drinkers (≥14 SD/week) based on self-reported intake levels. The outcomes of interest were stroke/systemic embolism, heart failure (HF) hospitalization, all-cause mortality, and major bleeding events. Logistic mixed-effects regression models adjusted for baseline covariates were used to calculate the adjusted odds ratios (aOR) and 95% confidence intervals (CI). Results: A total of 14,058 AF patients (mean age: 65.9 years; 48% women) with available data on alcohol intake level were included. The odds of stroke/systemic embolism were not significantly increased in light (aOR 0.87, 95% CI: 0.60-1.26), moderate (aOR 0.92, 95% CI: 0.54-1.58), nor heavy drinkers (aOR 0.76, 95% CI: 0.39-1.49) compared to abstainers. Light and moderate drinking, compared to alcohol abstinence, were associated with reduced odds of HF hospitalization (light: aOR 0.75, 95% CI: 0.59-0.95; moderate: aOR 0.57, 95% CI: 0.38-0.85) and all-cause mortality (light: aOR 0.42, 95% CI: 0.30-0.58; moderate: aOR 0.42, 95% CI: 0.26-0.68; heavy: aOR 0.58, 95% CI: 0.35-0.97). Alcohol consumption was not associated with an excess risk of major bleeding events. Conclusion: As compared to non-drinkers, light and moderate alcohol drinking in patients with AF are associated with a lower risk of mortality and HF hospitalization, with no difference in thromboembolic and bleeding risk.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".