Abstract 9368: Association Between Concurrent Use of Amiodarone and DOACs and Bleeding Risk in Atrial Fibrillation Patients
Bibliographic record
Abstract
Introduction: Amiodarone is a commonly used pharmacotherapy in patients with atrial fibrillation (AF) with potential for drug-drug interactions (DDIs) with direct oral anticoagulants (DOACs). We aimed to assess the bleeding risk after co-prescription of amiodarone and DOACs among adults with AF. Methods: We conducted a population-based, nested case-control study in Ontario, Canada. The study population included all patients with AF > 66 years on a DOAC between April 1, 2011-March 31, 2018. Cases were patients admitted with major bleeding (index date). Controls were matched in a 2:1 ratio to cases. We categorized exposure to amiodarone before the index date as: a) current users (amiodarone within 60 days); b) past users (amiodarone within 61 to 140 days); and c) unexposed (no amiodarone prescription or amiodarone prescription >140 days before index date). Conditional logistic regression models were used to examine the association between bleeding and amiodarone co-prescription. Results: Among 86,679 AF patients on a DOAC, we identified 2,766 cases (3.2%) admitted with major bleeding. The median age of AF patients was 80 years (interquartile range 75-85); 48.3% were women. After multivariable adjustment, there was a significant association between major bleeding and current use of amiodarone (adjusted odds ratio (aOR) 1.53; 95% confidence interval (CI) 1.24-1.89, p<0.001) but no significant association between major bleeding and past use of amiodarone (aOR 1.13, 95% CI 0.76-1.68, p=0.545) as compared with the unexposed group. Conclusions: Among older patients with AF on a DOAC, there was 53% increased odds of current use of amiodarone in those with versus without major bleeding.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".