Choice of P2Y12 inhibitor and clinical outcomes in the AUGUSTUS study: support for an individualised approach
Bibliographic record
Abstract
Abstract Background AUGUSTUS randomized patients with atrial fibrillation and ACS and/or PCI to apixaban or VKA and aspirin or placebo for 6 months on background P2Y12 inhibitor. Purpose To characterise the clinical outcomes in patients receiving clopidogrel or ticagrelor. Methods Patients enrolled in AUGUSTUS (n=4614) were grouped by P2Y12 inhibitor at randomization. Baseline characteristics were compared among groups. Rates of ISTH major or CRNM bleeding, definite/probable stent thrombosis (ST), stroke, MI, and death or ischaemic event were quantified and treatment groups were compared according to treatment with clopidogrel or ticagrelor. Results At randomization, patients were treated with clopidogrel (n=4165), ticagrelor (n=280), prasugrel (n=51) or no P2Y12 inhibitor (n=118). Median ages were 71, 69, 66 and 72 years (P<0.001). Ticagrelor and prasugrel were more commonly used in PCI-managed ACS (53% and 55%) whilst clopidogrel or no P2Y12 inhibitor was more common in medically-managed ACS (25% and 36%); elective PCI was the index event in 37–41% for each group. Irrespective of P2Y12 inhibitor used, bleeding risk was lower with apixaban vs. VKA and higher with aspirin vs. placebo (Table). ST rate was lowest but major/CRNM bleeding rate highest with ticagrelor + aspirin vs ticagrelor without aspirin or clopidogrel with or without aspirin. Conclusions Apixaban is safer than VKA regardless of P2Y12 inhibitor used. Dropping aspirin reduces bleeding but is associated with numerically higher ST rates regardless of P2Y12 inhibitor used. These data support current recommendations for preferential use of NOAC vs. VKA and individualised choice of P2Y12 inhibitor and timing of aspirin cessation after PCI according to the patient's risks of bleeding and stent thrombosis. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Bristol-Myers Squibb/Pfizer
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".