Efficacy and Safety of Vorapaxar as Approved for Clinical Use in the United States
Bibliographic record
Abstract
BACKGROUND: Vorapaxar is a protease-activated receptor-1 antagonist approved by the U.S. Food and Drug Administration (FDA) for the reduction of thrombotic cardiovascular (CV) events in patients with a history of myocardial infarction (MI) and peripheral artery disease (PAD), without a previous stroke or transient ischemic attack (TIA). METHODS AND RESULTS: We examined the efficacy and safety of vorapaxar in the intended use population, considering 20,170 patients randomized in the multinational, double-blinded, placebo-controlled TRA 2°P-TIMI 50 trial. Of these, 16,897 qualified with a history of MI in the prior 2 weeks to 1 year and 3273 with PAD. At baseline 97% of the patients were treated with aspirin, 71% with a thienopyridine, and 93% a statin. At 3 years, the endpoint of CV death, MI, or stroke was significantly reduced with vorapaxar compared with placebo (7.9% versus 9.5%, HR, 0.80; 95% CI 0.73 to 0.89; P<0.001). Vorapaxar also significantly reduced the composite of CV death, MI, stroke, and urgent coronary revascularization (10.1% versus 11.8%, HR, 0.83; 95% CI 0.76 to 0.90; P<0.001), as well as the rate of CV death or MI (P<0.001). The safety endpoint of GUSTO moderate or severe bleeding, was increased in the vorapaxar group (3.7 versus 2.4, HR, 1.55; 95% CI 1.30 to 1.86, P<0.001). Intracranial bleeding (ICH) was 0.6% versus 0.4%, P=0.10 with vorapaxar versus placebo, with fatal bleeding 0.2% versus 0.2%; P=0.70. CONCLUSIONS: In patients with prior MI or PAD who have not had a previous stroke or TIA, vorapaxar added to standard therapy is effective for long-term secondary prevention of thrombotic CV events, while increasing moderate or severe bleeding. CLINICAL TRIAL REGISTRATION: URL: clinicaltrials.gov Unique Identifier: NCT00526474.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 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".