Potential effects of icosapent ethyl on cardiovascular outcomes in cigarette smokers: REDUCE-IT smoking
Bibliographic record
Abstract
AIMS: Cigarette smoking is among the most well-established risk factors for adverse cardiovascular outcomes. We sought to determine whether icosapent ethyl (IPE), a highly purified form of eicosapentaenoic acid with antiatherothrombotic properties, may reduce the excessive risk of cardiovascular disease (CVD) attributable to smoking. METHODS AND RESULTS: Reduction of Cardiovascular Events with Icosapent Ethyl Trial (REDUCE-IT) was a multinational, double-blind trial that randomized 8179 statin-treated patients with elevated triglycerides and CV risk to IPE or placebo, with a median follow-up period of 4.9 years. Icosapent ethyl reduced the primary composite endpoint [CV death, non-fatal myocardial infarction (MI), non-fatal stroke, coronary revascularization, or hospitalization for unstable angina] by 25% (P < 0.0001). In the current analyses, the effect of IPE was evaluated in REDUCE-IT using post hoc analyses based on smoking history. Groups were classified as current smokers (n = 1241), former smokers (n = 3672), and never smokers (n = 3264). Compared with placebo, IPE use in combined current and former smokers (n = 4913) was associated with significant reductions in time to the primary composite endpoint {hazard ratio: 0.77 [95% confidence interval (CI): 0.68-0.87]; P < 0.0001} and in total events [rate ratio: 0.71 (95% CI: 0.61-0.82); P < 0.0001]. These benefits remained significant when subdivided into current and former smokers (P = 0.04, P = 0.005), with reductions in the key secondary composite endpoint (P < 0.0001) and in the individual components of CV death or non-fatal MI (P = 0.04, P = 0.01) and fatal or non-fatal MI (P = 0.009, P = 0.01), respectively. Benefits were consistent and significant in non-smokers as well. Overall, there were similar estimated rates of first occurrences of primary CVD endpoints in current smokers (23.8%) and former smokers (23.0%) assigned to IPE compared with never smokers on placebo (25.7%). CONCLUSION: In REDUCE-IT, IPE treatment was associated with a reduced risk of CV events in current and former smokers to levels observed in never smokers. While smoking cessation should always be recommended, these data raise the possibility that IPE treatment may attenuate CV hazards attributable to smoking.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".