Risk of myocardial infarction in patients with atrial fibrillation using vitamin K antagonists, aspirin or direct acting oral anticoagulants
Bibliographic record
Abstract
AIM: Direct-acting oral anticoagulants (DOACs) have become available for the prevention of stroke in patients with atrial fibrillation (AF). Conflicting results have been published on the risk of acute myocardial infarction (AMI) with the use of DOACs in comparison with vitamin K antagonists (VKAs). The objective of the present study was to evaluate the risk of AMI in patients with AF who are exposed to either VKAs, DOACs or low-dose (< 325 mg) aspirin. METHODS: We conducted a population-based cohort study using data from the Clinical Practice Research Datalink (2008-2014). The study population (n = 30 146) consisted of all patients ≥18 years with a diagnosis of AF who were new users of VKAs, DOACs (rivaroxaban and dabigatran) or aspirin. Cox proportional hazards models were used to estimate the hazard ratio (HR) of AMI for users of DOACs or aspirin vs. VKA. Adjustments were made for age, gender, lifestyle, risk factors, comorbidity and other drugs. RESULTS: The risk of AMI was doubled when we compared current use of DOACs with current use of VKAs [adjusted HR 2.11; 95% confidence interval (CI) 1.08, 4.12] and for current users of aspirin vs. current VKA users (adjusted HR 1.91; 95% CI 1.45, 2.51). CONCLUSIONS: There is a twofold increase in the risk of AMI for users of DOACs, in comparison with VKAs, in AF therapy. In addition, the results suggested that in patients with AF, the incidence of AMI is higher during aspirin monotherapy than during the use of VKAs.
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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 | 0.001 |
| 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 | 0.001 | 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".