Benefits of Icosapent Ethyl Across a Range of Baseline Renal Function in Patients with Established Cardiovascular Disease or Diabetes: Results of REDUCE-IT RENAL
Bibliographic record
Abstract
Background: Chronic kidney disease is associated with adverse outcomes among patients with established cardiovascular disease (CVD) or diabetes. Medications for treatment of CVD among patients with low estimated glomerular filtration rate (eGFR) may be ineffective. Methods: The Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial (REDUCE-IT) randomized patients with CVD or diabetes and one additional risk factor to treatment with icosapent ethyl or placebo. Patients from REDUCE-IT were categorized by prespecified eGFR categories for analysis of the effect of icosapent ethyl (IPE) on the primary endpoint (composite of cardiovascular (CV) death, nonfatal myocardial infarction (MI), nonfatal stroke, coronary revascularization, or unstable angina) and key secondary endpoint (a composite of CV death, nonfatal MI, or nonfatal stroke). In post hoc analysis, patients were categorized by additional eGFR cutoffs consistent with current medical guidelines. Results: Among the 8179 REDUCE-IT patients, median baseline eGFR was 75 mL/min/1.73m2 (range: 17 to 123 mL/min/1.73m2). There were no meaningful changes in median eGFR for IPE versus placebo across study visits. IPE benefit was consistent across baseline eGFR for the primary (Figure) and key secondary endpoints. The numerical reduction in CV death was greatest in the eGFR <60 mL/min/1.73m2 group (IPE: 7.6%; placebo: 10.6%; HR 0.70, 95%CI 0.51, 0.95, p=0.02). The rate of microalbuminuria in adverse event reporting was lower among IPE-treated patients (0.1% versus 0.3%, p=0.01). Conclusions: In REDUCE-IT, icosapent ethyl reduced fatal and nonfatal ischemic events across the broad range of baseline eGFR categories. Funding: Commercial Support - Amarin
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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 | 0.002 |
| 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 | 0.000 | 0.002 |
| 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".