Economic evaluation of drug-eluting stents compared to bare metal stents using a large prospective study in Ontario
Bibliographic record
Abstract
OBJECTIVES: To determine the cost-effectiveness (CE) and cost-utility (CU) of drug-eluting stents (DES) compared to bare metal stents (BMS) in Ontario using a large prospective "real-world" cohort study and determine the extent to which results vary by patient risk subgroups. METHODS: A field evaluation was conducted based on all stent procedures in the province of Ontario between December 1, 2003, and March 31, 2005, with a minimum subject follow-up of 1 year. Effectiveness data from the study using a propensity-score matched cohort were combined with resource utilization and cost data and quality of life (QOL) data from the published literature in a decision analytic modeling framework to determine 2-year cost-effectiveness (cost per revascularization avoided) and cost-utility (cost per quality-adjusted life-year ([QALY] gained). Stochastic model parameter uncertainty was expressed using probability distributions and analyzed using a probabilistic model. Modeling assumptions were assessed using traditional deterministic sensitivity analysis. RESULTS: Significant differences in revascularization rates were found for patients with two or more high risk factors. Despite these differences, the CE and CU of DES remained high (e.g., $419,000 per QALY gained in the most favorable patient risk subgroup). In sensitivity analysis, the difference in cost between DES and BMS had an impact on the CE and CU results. For example, at a price differential of $500, the CU of DES was $20,000/QALY for one patient subgroup and DES was dominant (i.e., less costly and more effective) in another. CONCLUSIONS: At current prices, the CE/CU of DES compared with BMS is high even in patient high risk subgroups. As the relative price of DES decrease, the value for money attractiveness of DES increases, especially for selected high risk patients.
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 | 0.001 | 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 | 0.000 |
| 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".