Cost-Effectiveness of Canakinumab From a Canadian Perspective for Recurrent Cardiovascular Events
Bibliographic record
Abstract
Background: Cardiovascular (CV) disease is a condition with high levels of morbidity and mortality. Canakinumab is a novel monoclonal antibody therapy that has been shown to reduce CV events but is associated with side effects and high cost. The main objective for this analysis is to determine whether canakinumab use is cost-effective for the prevention of recurrent CV events. Methods: A decision model was developed to estimate the direct costs and outcomes among patients who have suffered a myocardial infarction and are treated with canakinumab. A lifetime study horizon was used to analyze the base-case costs and utilities from the perspective of the Canadian publicly funded healthcare system. Markov modeling was used in combination with Monte Carlo simulation to derive expected values for costs and quality-adjusted life years (QALYs), permitting the calculation of incremental cost-effectiveness ratios. Results: Canakinumab was associated with higher average lifetime costs per patient ($457,982 vs $82,565) and higher average QALYs per patient (14.90 vs 14.20), compared with standard of care. Thus, the incremental cost per QALY gained for canakinumab treatment vs standard-of-care therapy was $535,365. The probability that canakinumab treatment is cost-effective was 0%. Results were consistent over a range of scenario analyses. Conclusions: Treatment of patients post-myocardial infarction with canakinumab is not cost-effective, compared with standard-of-care therapy at the current price. Based on currently accepted willingness-to-pay thresholds in Canada, a reduction in price of 91% is required to yield a cost per patient that would be considered appropriate.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".