Abstract 19049: Net Clinical Benefit of Vorapaxar in NSTE ACS: Role of Ischemic and Bleeding Risk Stratification
Bibliographic record
Abstract
Background: In 12,944 NSTE ACS pts included in the TRACER trial, platelet protease-activated-receptor 1 (PAR-1) antagonist vorapaxar, on top of standard of care with high thienopyridine use, was associated with an 11% reduction in CV death, MI, or stroke and an increased risk of bleeding. The trial was broadly inclusive and did not exclude pts based on bleeding risk. We evaluated the net clinical benefit of vorapaxar in the randomized population and according to patients’ predicted risks of ischemic events and major bleeding. Methods: Using multivariable models developed in the TRACER placebo group to predict 1-yr major bleeding (GUSTO severe or TIMI major bleeding) and recurrent ischemic events (composite of CV death, MI, stroke), patients were stratified into 2 bleeding risk categories (predicted rate >5% vs ≤5%) and 2 ischemic risk categories (predicted rate >13% vs ≤13%). Net clinical outcome was measured as the difference in treatment effects on the composite ischemic endpoint vs bleeding outcome. Results: Overall at 1-yr, vorapaxar resulted in a 1.30% absolute reduction in CV death, MI, and stroke and a 0.94% absolute increase in GUSTO severe bleeding (net benefit +0.34%). In pts with an increased risk of bleeding, vorapaxar had negative net clinical benefit (Figure). Among pts with high risk of ischemic events but low risk of bleeding (26% of the population), vorapaxar produced a favorable 2.8% absolute net benefit. Conclusion: Vorapaxar was associated with an improved net benefit in a selected but large group of NSTE ACS patients with high risk of recurrent ischemic events and low risk of bleeding, identified through the application of multivariable risk stratification strategies. Appropriate selection of patients in NSTE ACS, with the risk of bleeding balanced against that of recurrent ischemic events, appears to be a determinant of improved outcome with vorapaxar added to standard of care and high use of dual antiplatelet therapy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".