Major bleeding in users of direct oral anticoagulants in atrial fibrillation: A pooled analysis of results from multiple population‐based cohort studies
Bibliographic record
Abstract
OBJECTIVE: To establish the risk of major bleeding in direct oral anticoagulant (DOAC) users (overall and by class) versus vitamin K antagonist (VKA) users, using health care databases from four European countries and six provinces in Canada. METHODS: A retrospective cohort study was performed according to a similar protocol. First-users of VKAs or DOACs with a diagnosis of non-valvular atrial fibrillation (NVAF) were included. The main outcome of interest was major bleeding and secondary outcomes included gastrointestinal (GI) bleeding and intracranial haemorrhage (ICH). Incidence rates of events per 1000 person years were calculated. Hazard ratios (HRs) and 95% confidence intervals (95% CI) were estimated using a Cox proportional hazard regression model. Exposure and confounders were measured and analysed in a time-dependant way. Risk estimates were pooled using a random effect model. RESULTS: 421 523 patients were included. The risk of major bleeding for the group of DOACs compared to VKAs showed a pooled HR of 0.94 (95% CI: 0.87-1.02). Rivaroxaban showed a modestly increased risk (HR 1.11, 95% CI: 1.06-1.16). Apixaban and dabigatran showed a decreased risk of respectively HR 0.76 (95% CI: 0.69-0.84) and HR 0.85 (95% CI: 0.75-0.96). CONCLUSIONS: This study confirms that the risk of major bleeding of DOACs compared to VKAs is not increased when combining all DOACs. However, we observed a modest higher risk of major bleeding for rivaroxaban, whereas for apixaban and dabigatran lower risks of major bleeding were observed compared to VKAs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".