Cost-Effectiveness of Icosapent Ethyl (IPE) for the Reduction of the Risk of Ischemic Cardiovascular Events in Canada
Bibliographic record
Abstract
Background: Despite the use of statins, many patients with cardiovascular disease (CVD) have persistent residual risk. In a large Phase III trial (REDUCE-IT), icosapent ethyl (IPE) was shown to reduce the first occurrence of the primary composite endpoint of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, or hospitalization for unstable angina. Methods: We conducted a cost-utility analysis comparing IPE to placebo in statin-treated patients with elevated triglycerides, from a publicly funded, Canadian healthcare payer perspective, using a time-dependent Markov transition model over a 20-year time horizon. We obtained efficacy and safety data from REDUCE-IT, and costs and utilities from provincial formularies and databases, manufacturer sources, and Canadian literature sources. Results: In the probabilistic base-case analysis, IPE was associated with an incremental cost of $12,523 and an estimated 0.29 more quality-adjusted life years (QALYs), corresponding to an incremental cost-effectiveness ratio (ICER) of $42,797/QALY gained. At a willingness-to-pay of $50,000 and $100,000/QALY gained, there is a probability of 70.4% and 98.8%, respectively, that IPE is a cost-effective strategy over placebo. The deterministic model yielded similar results. In the deterministic sensitivity analyses, the ICER varied between $31,823-$70,427/QALY gained. Scenario analyses revealed that extending the timeframe of the model to a lifetime horizon resulted in an ICER of $32,925/QALY gained. Conclusion: IPE represents an important new treatment for the reduction of ischemic CV events in statin-treated patients with elevated triglycerides. Based on the clinical trial evidence, we found that IPE could be a cost-effective strategy for treating these patients in Canada.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".