Effects of icosapent ethyl on risk and duration of hospitalizations and death in REDUCE-IT
Bibliographic record
Abstract
Abstract Background Among participants with elevated triglycerides and known cardiovascular disease or with diabetes and other risk factors in the REDUCE-IT trial, icosapent ethyl (IPE) significantly reduced the risk of first and total cardiovascular events relative to placebo. Purpose The aim of this post hoc analysis of the trial was to estimate the effects of IPE on total hospitalizations and days lost to hospitalization and death. Methods Randomization to treatment with 2 g twice daily of IPE or matching placebo was performed among 8179 participants receiving statin therapy with established cardiovascular disease or age ≥50 years with diabetes and ≥1 additional risk factor, fasting triglyceride 1.69 – 5.63 mmol/L, and low-density lipoprotein cholesterol 1.06 – 2.59 mmol/L. Total hospitalizations were analyzed with a competing risks marginal proportional hazards model for total events. The likelihood of no days lost to hospitalization and death and the rate of days lost among those who were hospitalized or died during the study were analyzed with a zero-inflated Poisson regression model. Results During a median 5.0 years of follow-up, 6919 total hospitalizations were observed, with median (Q1, Q3) duration of 4 (2, 10) days. IPE reduced total hospitalizations (HR (95% CI) = 0.91 (0.84, 0.98), P=0.017; Figure). Participants randomized to IPE were also more likely to survive until the end of the study without hospitalization (OR (95% CI) = 1.12 (1.02, 1.22), P=0.016) and had a lower rate of days lost among those who were hospitalized or died during follow-up (RR (95% CI) = 0.93 (0.93, 0.94), P<0.001). Normalizing for duration of follow-up, 19.2 total hospitalizations and 21.4 total hospitalizations or deaths were avoided with IPE per 1000 participant-years of assigned treatment. Conclusion Among participants in REDUCE-IT, IPE reduced total hospitalizations and had favorable impacts on measures of days lost due to hospitalization and death. These findings provide additional insights on the effects of IPE on patient-centered measures of total disease burden.
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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 | 0.003 |
| 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) | 0.003 | 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".