Abstract 17121: Reduction in Total Cardiovascular Events With Long-term Use of Ticagrelor in Patients With Prior Myocardial Infarction in the PEGASUS-TIMI 54 Trial
Bibliographic record
Abstract
Background: Long-term dual antiplatelet therapy with aspirin plus ticagrelor, a direct-acting reversibly-binding P2Y12 receptor antagonist, was recently shown in the PEGASUS-TIMI 54 trial (NCT01225562) to be superior to aspirin alone in preventing major adverse cardiovascular events in patients with a prior MI. Whereas prior analyses focused on time to first event, we hypothesized that total events including those beyond the first event would also be reduced with ticagrelor. Methods: We evaluated all primary endpoint (PEP) events (CV death, MI, stroke) during a median 33 month follow-up in patients randomized to ticagrelor (60 mg or 90 mg) vs placebo in the PEGASUS-TIMI 54 trial. Negative binomial regression & other sensitivity models were used. Results: Among 21,162 patients, there were 1,830 PEP events (85% first events, n=1,558 and 15% subsequent events, n=272). Of the total PEP events, 1,041 were MIs (56.9%), 338 were strokes (18.5%), and 451 were CV deaths (24.6%). In patients with a PEP event, 86.3% had 1 event, 11.0% had 2 events, and 2.8% had 3 or more events (maximum 6). Total PEP events were reduced 17% with ticagrelor 60 mg vs placebo (incidence-rate ratio [IRR] 0.83, 95% CI 0.72-0.95, p=0.006) and 17% with ticagrelor 90 mg vs placebo (IRR 0.83, 95% CI 0.73-0.95, p=0.007). A Wei-Lin-Weissfeld time-to-event model showed similar reductions. In terms of irreversible harm events, for every 1000 patients treated for 3 years, ticagrelor would prevent 18 primary endpoint events, including (depending on the dose) 5 CV deaths, 8-11 MIs, and 3-4 strokes, and cause only 0 or 1 excess fatal bleeds or ICH (Figure). Conclusions: Patients with prior MI remain at high risk for recurrent vascular events. The addition of ticagrelor to aspirin significantly reduces the burden of total vascular events.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".