Cost‐Effectiveness of Icosapent Ethyl in REDUCE‐IT USA: Results From Patients Randomized in the United States
Bibliographic record
Abstract
BACKGROUND: In 3146 REDUCE-IT USA (Reduction of Cardiovascular Events With Icosapent Ethyl Intervention Trial USA) participants, icosapent ethyl (IPE) reduced first and total cardiovascular events by 31% and 36%, respectively, over 4.9 years of follow-up. METHODS AND RESULTS: We used participant-level data from REDUCE-IT USA, 2021 US costs, and IPE costs ranging from $4.59 to $11.48 per day, allowing us to examine a range of possible medication costs. The in-trial analysis was participant-level, whereas the lifetime analysis used a Markov model. Both analyses considered value from a US health sector perspective. The incremental cost-effectiveness ratio (incremental costs divided by incremental quality-adjusted life-years) of IPE compared with standard care (SC) was the primary outcome measure. There was incremental gain in quality-adjusted life-years with IPE compared with SC using in-trial (3.28 versus 3.13) and lifetime (10.36 versus 9.83) horizons. Using an IPE cost of $4.59 per day, health care costs were lower with IPE compared with SC for both in-trial ($29 420 versus $30 947) and lifetime ($216 243 versus $219 212) analyses. IPE versus SC was a dominant strategy in trial and over the lifetime, with 99.7% lifetime probability of an incremental cost-effectiveness ratio <$50 000 per quality-adjusted life-year gained. At a medication cost of $11.48 per day, the cost per quality-adjusted life-year gained was $36 208 in trial and $9582 over the lifetime. CONCLUSIONS: In this analysis, at $4.59 per day, IPE offers better outcomes than SC at lower costs in trial and over a lifetime and is cost-effective at $11.48 per day for conventional willingness-to-pay thresholds. Treatment with IPE should be strongly considered in US patients like those enrolled in REDUCE-IT USA. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01492361.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".