Abstract 16486: Consistent Cost-effectiveness of Icosapent Ethyl Across Patient Profiles From REDUCE-IT
Bibliographic record
Abstract
Background: The Reduction of Cardiovascular Events with Icosapent Ethyl–Intervention Trial (REDUCE-IT) showed that patients with elevated baseline triglycerides (TG) and well-controlled LDL-C levels on statins had a 30% lower risk of total cardiovascular events with 4g of icosapent ethyl (IPE) daily compared to standard care (SC) during a median 4.9 year follow-up. The purpose of this study was to conduct subgroup analyses of lifetime cost-effectiveness (CE) of IPE compared to SC alone. Methods: Applying treatment effects from REDUCE-IT, health care costs from the National Inpatient Sample (NIS), and net costs for IPE of $4.16 a day, we conducted a combination CE analysis utilizing patient level in-trial cost and clinical outcomes with long-term costs, events, and life expectancy derived from Markov simulation models. The model projected lifetime health care costs, cardiovascular events, survival, and quality-adjusted life-years (QALYs) for IPE vs. SC from a payer perspective among overall trial-eligible patients and in key subgroups. Results: The lifetime mean costs for IPE and SC were $196,080 and $197,064, and the lifetime QALYs for IPE and SC were 10.61 and 10.35, respectively (Table 1). IPE was a dominant strategy over the lifetime in 69.7% of simulations with the probability of CE at the nominal $50,000, $100,000, and $150,000 thresholds being replicated in 87.9%, 98.6%, and 99.9% of simulations, respectively. In the subgroups of age <65 years, male sex, subjects with or without diabetes, secondary prevention cohort, TG levels ≥200 or ≥150 mg/dL, and baseline LDL≥70 mg/dL, IPE was a dominant strategy over the lifetime. In women or the primary prevention cohort, IPE was cost-effective with an ICER of $16,660 or $21,890 per QALY gained, respectively. Conclusions: In all subgroups, IPE at a cost of $4.16/day was shown to be cost-effective at a willingness-to-pay threshold of $50,000 per QALY and was a dominant treatment strategy in most subgroups.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".