Ticagrelor and aspirin for the prevention of cardiovascular events after coronary artery bypass graft surgery
Bibliographic record
Abstract
BACKGROUND: Ticagrelor was shown to reduce mortality in patients who underwent coronary artery bypass grafting (CABG), but its effect on graft patency is unknown. METHODS: We performed a prospective, randomised, double-blind, placebo-controlled trial, comparing ticagrelor 90 mg twice daily versus placebo for 3 months added to aspirin 81 mg/day, following isolated CABG. Aspirin was started within 12 h, and study medication within 72 h after CABG. Primary outcome was graft occlusion on CT angiography (CTA) performed 3 months post CABG. Patients were followed to 12 months for death, myocardial infarction, stroke, repeat revascularisation and bleeding. RESULTS: The study was terminated prematurely after randomising 70 patients between September 2011 and August 2014 because of slow recruitment. CTA was performed in 56 patients who completed >1 month of study drug. Graft occlusion occurred in 7/25 (28.0%) patients on ticagrelor and 17/31 (48.3%) on placebo, p=0.044. Of 207 analysable grafts, graft occlusion occurred in 9/87 (10.3%) with ticagrelor and 22/120 (18.3%) with placebo, p=0.112. Graft occlusion or stenosis ≥50% occurred in 10/87 (11.5%) ticagrelor vs 32/120 (26.7%) placebo, p=0.007. There was no major bleeding, but minor bleeding was higher with ticagrelor (31.4% vs 2.9%, p=0.003). In univariate analysis, ticagrelor use reduced graft occlusion (OR 0.32, 95% CI 0.10 to 0.97, p=0.047), which remained significant on multivariable analysis (OR 0.25, 95% CI 0.073 to 0.873, p=0.03). CONCLUSIONS: Ticagrelor added to aspirin after CABG reduced the proportion of patients with graft occlusion, and was a significant univariate and multivariable predictor of graft occlusion. These results are hypothesis-generating and should be confirmed in larger studies. TRIAL REGISTRATION NUMBER: NCT01373411: Results.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".