Cost-Effectiveness Analysis of Evolocumab in Adult Patients with Atherosclerotic Cardiovascular Disease in Canada
Bibliographic record
Abstract
INTRODUCTION: To evaluate the cost-effectiveness of evolocumab when added to standard of care lipid-lowering treatment (LLT) for patients with atherosclerotic cardiovascular disease (ASCVD) who cannot adequately control their low-density lipoprotein cholesterol (LDL-C) despite optimized LLT in Canada. METHODS: An incremental cost-utility analysis was conducted using a Markov cohort state transition model adapted to the Canadian setting. Analyses were conducted from a public health and societal perspective using a lifetime time horizon for Canada. Scenario analyses were conducted on the basis of recommendations from the 2021 Canadian Cardiovascular Society (CCS) dyslipidemia guidelines. RESULTS: In ASCVD patients with prior myocardial infarction (MI) and baseline LDL-C ≥ 1.8 mmol/L, adding evolocumab to optimized statin therapy with or without ezetimibe is associated with an incremental cost per quality-adjusted life year (QALY) gained of $66,453 CAD. Furthermore, for every 100 patients treated with evolocumab for lifetime, adding evolocumab to optimized LLT will prevent approximately 52 cardiovascular (CV) events, of which seven would be fatal. The results are generally robust using univariate and simultaneous variation in model input parameters. Scenario analyses for patient populations as per the CCS guidelines suggest that evolocumab added to optimized LLT may be considered cost-effective, given an incremental cost-effectiveness ratio (ICER) threshold of CAD$100,000 per QALY gained. Limitations associated with this analysis should be interpreted in the context of data and modeling assumptions used. CONCLUSION: Overall, this analysis supports reimbursement of evolocumab by payers in patients with ASCVD who cannot reach LDL-C thresholds despite optimized LLT to reduce unnecessary fatal and non-fatal CV events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".