Abstract 14997: Icosapent Ethyl Reduces Ischemic Events in Patients With Prior Coronary Artery Bypass Grafting: REDUCE-IT CABG
Bibliographic record
Abstract
Background: Patients with a prior history of coronary artery bypass grafting (CABG) are at high risk for future ischemic events despite statin therapy. Methods: REDUCE-IT, a multicenter, double-blind, placebo-controlled trial, randomized statin-treated patients with elevated triglycerides (135-499 mg/dL), controlled LDL (41-100 mg/dL), and either established cardiovascular disease or diabetes plus other risk factors to receive icosapent ethyl 4g daily or placebo. In the overall trial, the primary composite endpoint (cardiovascular death, myocardial infarction, stroke, coronary revascularization, hospitalization for unstable angina) and the key secondary composite endpoint (cardiovascular death, myocardial infarction, stroke) were significantly reduced. Here we examine the subgroup of patients with a history of CABG. Results: A total of 8,179 randomized patients were followed for 4.9 years (median), 1837 (22.5%) of whom had a prior CABG, with a median of 5.1 years (0.7 months to 33.3 years) from CABG to randomization (in the 1263 patients who had the date of CABG recorded). The rate of the primary endpoint was reduced by 24% [22.0% with icosapent ethyl versus 28.2% with placebo (hazard ratio [HR]=0.76; 95% confidence interval [CI], 0.63-0.92; p=0.004; number needed to treat [NNT]=16)]. The rate of the key secondary endpoint was reduced by 31%: [14.7% with icosapent ethyl versus 20.7% with placebo (HR=0.69; 95% CI, 0.56-0.87; p=0.001; NNT=17)]. Conclusions: In statin-treated patients with a history of prior CABG, the addition of icosapent ethyl significantly reduced ischemic events, with both large relative and absolute risk reductions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".