Benefits of Icosapent Ethyl Across the Range of Kidney Function in Patients With Established Cardiovascular Disease or Diabetes: REDUCE-IT RENAL
Bibliographic record
Abstract
Background: Chronic kidney disease is associated with adverse outcomes among patients with established cardiovascular disease (CVD) or diabetes. Commonly used medications to treat CVD are less effective among patients with reduced kidney function. Methods: REDUCE-IT (Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial) was a multicenter, double-blind, placebo-controlled trial that randomly assigned statin-treated patients with elevated triglycerides (135–499 mg/dL) who had CVD or diabetes and 1 additional risk factor to treatment with icosapent ethyl (4 g daily) or placebo. Patients from REDUCE-IT were categorized by prespecified estimated glomerular filtration rate (eGFR) categories to analyze the effect of icosapent ethyl on the primary end point (composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, or unstable angina) and key secondary end point (a composite of cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke). Results: Among the 8179 REDUCE-IT patients, median baseline eGFR was 75 mL·min –1 ·1.73 m –2 (range, 17–123 mL·min –1 ·1.73 m –2 ). There were no meaningful changes in median eGFR for icosapent ethyl versus placebo across study visits. Treatment with icosapent ethyl led to consistent reduction in both the primary and key secondary composite end points across baseline eGFR categories. Patients with eGFR <60 mL·min –1 ·1.73 m –2 treated with icosapent ethyl had the largest absolute and similar relative risk reduction for the primary composite end point (icosapent ethyl versus placebo, 21.8% versus 28.9%; hazard ratio [HR], 0.71 [95% CI, 0.59–0.85]; P =0.0002) and key secondary composite end point (16.8% versus 22.5%; HR 0.71 [95% CI, 0.57–0.88]; P =0.001). The numeric reduction in cardiovascular death was greatest in the eGFR <60 mL·min –1 ·1.73 m –2 group (icosapent ethyl: 7.6%; placebo: 10.6%; HR, 0.70 [95% CI, 0.51–0.95]; P =0.02). Although patients with eGFR <60 mL·min –1 ·1.73 m –2 treated with icosapent ethyl had the highest numeric rates of atrial fibrillation/flutter (icosapent ethyl: 4.2%; placebo 3.0%; HR 1.42 [95% CI, 0.86–2.32]; P =0.17) and serious bleeding (icosapent ethyl: 5.4%; placebo 3.6%; HR, 1.40 [95% CI, 0.90–2.18]; P =0.13), HRs for atrial fibrillation/flutter and serious bleeding were similar across eGFR categories ( P -interaction for atrial fibrillation/flutter=0.92; P -interaction for serious bleeding=0.76). Conclusions: In REDUCE-IT, icosapent ethyl reduced fatal and nonfatal ischemic events across the broad range of baseline eGFR categories. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01492361
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".