Cost-effectiveness of drug-eluting coronary stents in Quebec, Canada
Bibliographic record
Abstract
OBJECTIVES: The aim of this investigation was to assess the incremental cost-effectiveness of replacing bare metal coronary stents (BMS) with drug-eluting stents (DES) in the Province of Quebec, Canada. METHODS: The strategy used was a cost-effectiveness analysis from the perspective of the health-care provider, in the province of Quebec, Canada (population 7.5 million). The main outcome measure was the cost per avoided revascularization intervention. RESULTS: Based on the annual Quebec rate of 14,000 angioplasties with an average of 1.7 stents per procedure and a purchase cost of $2,600 Canadian dollar (CDN) for DES, 100 percent substitution of BMS with DES would require an additional $45.1 million CDN of funding. After the benefits of reduced repeat revascularization interventions are included, the incremental cost would be $35.2 million CDN. The cost per avoided revascularization intervention (18 percent coronary artery bypass graft, 82 percent percutaneous coronary intervention [PCI]) would be $23,067 CDN. If DES were offered selectively to higher risk populations, for example, a 20 percent subgroup with a relative restenosis risk of 2.5 times the current bare metal rate, the incremental cost of the program would be $4.9 million CDN at a cost of $7,800 per avoided revascularization procedure. Break-even costs for the program would occur at DES purchase cost of $1,161 for 100 percent DES use and $1,627 for selective 20 percent DES use for high-risk patients for restenosis (RR = 2.5). Univariate and Monte Carlo sensitivity analyses indicate that the parameters most affecting the analysis are the capacity to select patients at high risk of restenosis, the average number of stents used per PCI, baseline restenosis rates for BMS, the effectiveness ratio of restenosis prevention for DES versus BMS, the cost of DES, and the revascularization rate after initial PCI. Sensitivity analyses suggest little additional health benefits but escalating cost-effectiveness ratios once a DES penetration of 40 percent has been attained. CONCLUSIONS: Under current conditions in Quebec, Canada, selective use of DES in high-risk patients is the most acceptable strategy in terms of cost-effectiveness. Results of such an analysis would be expected to be similar in other countries with key model parameters similar to those used in this model. This model provides an example of how to evaluate the cost-effectiveness of selective use of a new technology in high-risk patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".