Impact of prior oral anticoagulant use and outcomes on patients from secondary analysis in the AUGUSTUS trial
Bibliographic record
Abstract
OBJECTIVE: Managing antithrombotic therapy in patients with atrial fibrillation (AF) and an acute coronary syndrome (ACS) and/or percutaneous coronary intervention (PCI) is challenging and can be affected by prior oral anticoagulant (OAC) treatment. We examined the relationship between prior OAC use and outcomes in the AUGUSTUS trial. METHODS: This prespecified secondary analysis is from AUGUSTUS, an open-label, 2-by-2 factorial, RCT to evaluate the safety of apixaban versus vitamin K antagonist (VKA) and aspirin versus placebo in patients with AF and ACS and/or PCI. The primary endpoint, major or clinically relevant non-major bleeding and clinical outcomes were compared in patients receiving (n=2262) or not receiving (n=2352) an OAC prior to enrolment. RESULTS: -VASC and HAS-BLED scores, and were more likely enrolled following elective PCI. There was no difference in major or clinically relevant non-major bleeding with or without prior OAC (30 days: 5.1% vs 5.9% (adjusted HR (aHR) 0.82, 95% CI 0.63 to 1.06); 180 days: 13.5% vs 13.5% (aHR 0.98, 95% CI 0.83 to 1.16)). Patients with prior OAC use had a lower risk of death or ischaemic events (30 days: 1.7% vs 2.8% (aHR 0.61, 95% CI 0.41 to 0.92); 180 days: 5.4% vs 7.6% (aHR 0.70, 95% CI 0.55 to 0.88)). No interactions between randomised treatment (apixaban vs VKA, aspirin vs placebo) and prior OAC status were observed for outcomes, apart from apixaban (vs VKA) being associated with a lower risk of myocardial infarction with prior OAC use (180 days: 2.0% vs 3.7% (aHR 0.56, 95% CI 0.33 to 0.91(). CONCLUSIONS: In AUGUSTUS, prior OAC use was associated with fewer ischaemic events but not more bleeding. In patients with AF and ACS and/or undergoing PCI, clinicians can be assured that the trial results can be applied to patients regardless of their prior OAC status. TRIAL REGISTRATION NUMBER: NCT02415400.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".