Evaluation of the cost-effectiveness of evolocumab in the FOURIER study: a Canadian analysis
Bibliographic record
Abstract
BACKGROUND: Evolocumab, a proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitor, has been shown to reduce low-density lipoprotein levels by up to 60%. Despite the absence of a reduction in overall or cardiovascular mortality in the Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk (FOURIER) trial, some believe that, with longer treatment, such a benefit might eventually be realized. Our aim was to estimate the potential mortality benefit over a patient's lifetime and the cost per year of life saved (YOLS) for an average Canadian with established coronary artery disease. We also sought to estimate the price threshold at which evolocumab might be considered cost-effective for secondary prevention in Canada. METHODS: We calibrated the Cardio-metabolic Model, a well-validated tool for predicting cardiovascular events and life expectancy, to the reduction in nonfatal events seen in the FOURIER trial. Assuming that long-term treatment will eventually result in mortality benefits, we estimated YOLSs and cost per YOLS with evolocumab treatment plus a statin compared to a statin alone. We then estimated the annual drug costs that would provide a 50% chance of being cost-effective at willingness-to-pay values of $50 000 and $100 000. RESULTS: In secondary prevention in patients similar to those in the FOURIER study, evolocumab treatment would save an average of 0.34 (95% confidence interval [CI] 0.27-0.41) life-years at a cost of $101 899 (95% CI $97 325-$106 473), yielding a cost per YOLS of $299 482. We estimate that to have a 50% probability of achieving a cost per YOLS below $50 000 and $100 000 would require annual drug costs below $1200 and $2300, respectively. INTERPRETATION: At current pricing, the use of evolocumab for secondary prevention is unlikely to be cost-effective in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".