Concomitant use of direct oral anticoagulants and interacting antiarrhythmic drugs and the risk of stroke and bleeding among patients with non-valvular atrial fibrillation: a multinational cohort study
Bibliographic record
Abstract
Abstract Background Several antiarrhythmic drugs can interact with direct oral anticoagulants (DOACs) through pharmacokinetic mechanisms increasing DOAC levels. Our multinational cohort study assessed the effectiveness and safety of concomitant use of DOACs and interacting antiarrhythmic drugs among patients with non-valvular atrial fibrillation (NVAF). Methods We used United Kingdom’s Clinical Practice Research Datalink and Quebec administrative claims data assembling two cohorts of patients with NVAF who initiated DOACs and added-on antiarrhythmic drugs. We assessed the risk of ischemic stroke and major bleeding associated with concomitant use of DOACs and interacting antiarrhythmic drugs (amiodarone, diltiazem, dronedarone, verapamil) versus concomitant use of DOACs and non-interacting antiarrhythmic drugs (flecainide, propafenone, sotalol) using an as-treated exposure definition. Cox models yielded hazard ratios (HRs) and 95% confidence intervals (CIs) after inverse-probability-of-treatment-weighting. We pooled site-specific estimates together using random-effects models. Secondary analyses stratified by age, sex, and individual DOACs. Results Our study cohort included 54,078 NVAF patients initiating DOACs and adding-on antiarrhythmic drugs. Concomitant use of DOACs and interacting antiarrhythmic drugs versus concomitant use of DOACs and non-interacting antiarrhythmic drugs was not associated with the risk of ischemic stroke (pooled HR, 1.04; 95% CI, 0.88–1.21; I 2 = 0%) but with an increased risk of major bleeding (pooled HR, 1.30; 95% CI, 1.19–1.41; I 2 = 58%), especially among patients < 70 years (pooled HR, 1.56; 95% CI, 1.31–1.86; I 2 = 0%). There was no effect modification by sex or individual DOAC. Conclusions Concomitant use of interacting antiarrhythmic drugs does not seem to affect the effectiveness of DOACs but may increase their risk of 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".