Comparative effectiveness and safety of oral anticoagulants in patients with atrial fibrillation using antiarrhythmic drugs: An international cohort study
Bibliographic record
Abstract
Aim Our international cohort study assessed the comparative effectiveness and safety of direct oral anticoagulants (DOACs) and vitamin K antagonists (VKAs) among patients with non‐valvular atrial fibrillation (NVAF) using antiarrhythmic drugs. Methods Using the United Kingdom's (UK's) Clinical Practice Research Datalink Aurum and Québec claims data, we assembled two study cohorts of patients with NVAF who initiated DOACs or VKAs while on antiarrhythmic drugs (2011–2020). Using an as‐treated exposure definition, we assessed the risks of ischemic stroke and major bleeding associated with DOACs vs . VKAs. Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) after propensity score‐based inverse‐probability‐of‐treatment‐weighting. Site‐specific estimates were pooled together using random‐effects models. Secondary analyses assessed effect measure modification by individual DOAC and type of antiarrhythmic drugs (interacting vs . non‐interacting). Results The study cohort included 44 435 patients with NVAF initiating DOACs (n = 29 071) or VKAs (n = 15 364) while using antiarrhythmics. Compared to VKAs, DOACs were not associated with the risk of ischemic stroke (UK: HR, 0.90; 95% CI, 0.61–1.32/ Quebec: HR, 0.85; 95% CI, 0.70–1.03/ pooled: HR, 0.86; 95% CI 0.72–1.02; I 2 = 0%) but with a decreased risk of major bleeding (UK: HR, 0.90; 95% CI, 0.75–1.08/Quebec: HR, 0.83; 95% CI, 0.76–0.91/pooled: HR 0.84; 95% CI 0.78–0.92; I 2 = 0%); the latter was more pronounced with apixaban (pooled HR, 0.71; 95% CI, 0.63–0.81; I 2 = 74%). There was no effect modification by type of antiarrhythmic drugs. Conclusions DOACs were as effective but safer than VKAs among NVAF patients treated with antiarrhythmic drugs.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.002 | 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".