Risk of major bleeding and stroke associated with the use of vitamin K antagonists, nonvitamin K antagonist oral anticoagulants and aspirin in patients with atrial fibrillation: a cohort study
Bibliographic record
Abstract
AIMS: Nonvitamin K antagonist oral anticoagulants (NOACs) are now available for the prevention of stroke in patients with atrial fibrillation (AF) as an alternative to vitamin K antagonists (VKA) and aspirin. The comparative effectiveness and safety in daily practice of these different drug classes is still unclear. The objective of this study was to evaluate the risk of major bleeding and stroke in AF patients using NOACs, VKAs or aspirin. METHODS: A retrospective cohort study was conducted among AF patients using the UK Clinical Practice Research Datalink (March 2008-October 2014). New users of VKAs, NOACs and low dose aspirin were followed from the date of first prescription of an antithrombotic drug until the occurrence of stroke or major bleeding. Analyses were adjusted for a history of comorbidities and drug use with Cox regression analysis. RESULTS: A total of 31 497 patients were eligible for the study. The hazard ratio (HR) of major bleeding was 2.07 [95% confidence interval (CI) 1.27-3.38] for NOACs compared with VKAs, which was mainly attributed by the increased risk of gastrointestinal bleeding (HR 2.63, 95% CI 1.50-4.62). This increased bleeding risk was restricted to women (HR 3.14, 95% CI 1.76-5.60). Aspirin showed a similar bleeding risk as VKAs. NOACs showed equal effectiveness as VKA in preventing ischaemic stroke (HR 1.22, 95% CI 0.67-2.19). VKAs were more effective than aspirin (HR 2.18, 95% CI 1.83-2.59). CONCLUSIONS: NOACs were associated with a higher risk on gastrointestinal bleeding, particularly in women. The use of NOACs in patients who are vulnerable for this type of bleeding should be carefully considered. NOACs and VKAs are equally effective in preventing stroke. Aspirin was not effective in the prevention of stroke in AF.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".