Abstract 57: Reduction in Ischemic Stroke With Icosapent Ethyl - Insights From REDUCE-IT
Bibliographic record
Abstract
Background: In patients at elevated cardiovascular risk, statins reduce the occurrence of ischemic stroke. However, residual stroke risk persists. Methods: REDUCE-IT, a multinational, double-blind trial, randomized 8179 statin-treated patients with controlled low-density lipoprotein cholesterol, elevated triglycerides, and risk for, or evidence of, atherosclerosis to icosapent ethyl (IPE), a purified, stable ethyl ester of eicosapentaenoic acid (4 grams/day), or placebo. IPE reduced the primary composite endpoint (CV death, myocardial infarction (MI), stroke, coronary revascularization, hospitalization for unstable angina) and the key secondary composite endpoint (CV death, MI, stroke) by 25% and 26%, respectively, (each p<0.000001). Total (first and recurrent) ischemic events were reduced by 32% (p<0.000001). We examined additional prespecified and post hoc stroke endpoints. Results: Event rates for time to first fatal or nonfatal stroke were 2.4% vs. 3.3% for IPE vs. placebo; hazard ratio (HR) (95% CI) = 0.72 (0.55-0.93); P=0.01; the relative risk reduction (RRR) was 28%, absolute risk reduction (ARR) 0.9%, and number needed to treat (NNT) 114. For every 1,000 patients treated for 5 years with IPE, approximately 14 strokes (fatal or nonfatal) were averted; rate ratio (RR) (95%CI) = 0.68 (0.52-0.91); P=0.008 (Figure). Ischemic stroke time to first event rates were 2.0% vs 3.0% for IPE vs placebo a 36% reduction [HR=0.64 (0.49-0.85); P=0.002]. Hemorrhagic stroke occurred at low rates with no significant difference for IPE vs. placebo (0.3% vs 0.2%; P=0.55). Conclusions: In REDUCE-IT, icosapent ethyl significantly reduced the risk of ischemic stroke, with no excess in hemorrhagic stroke, in statin-treated patients with elevated triglycerides and atherosclerosis or diabetes.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".