Abstract 202: Cost-effectiveness Of Carotid Stenting In Canada
Bibliographic record
Abstract
BACKGROUND To estimate the cost-effectiveness of carotid stenting (CAS) compared to endarterectomy (CEA) in symptomatic patients. METHODS A cost-utility analysis from the Canadian health system perspective was performed using a Markov analytic model. Clinical estimates were based on a recent meta-analysis. Procedural costs for CAS and CEA were derived from a local cohort. The costs for hospitalization and rehabilitation for minor and major strokes were based on the Burden of Ischemic Stroke (BURST) study. Utility scores were based on SAPPHIRE trial. A Monte Carlo simulation using a hypothetical cohort of 10,000 and sensitivity analyses were performed to investigate the model assumptions and uncertainties. RESULTS CAS was more expensive (incremental cost of $6106.84) and had a lower effectiveness (- 0.12 QALYs). The model was sensitive to the risk of annual death. At a threshold odds ratio (OR) of 0.85, CAS was associated with an incremental cost-effectiveness ratio (ICER) of $32,839.04. Using estimates from SAPPHIRE trial, CAS dominated CEA. When estimates from CREST or EVA-3S trials were used, CEA dominated CAS. Only after simultaneously reducing CAS costs and risks of periprocedural and annual minor strokes, CAS had a favorable ICER. This was achieved at a threshold CAS procedural cost of $4350, a threshold OR of periprocedural minor stroke of 1, and a threshold OR of annual minor stroke of 1.15or less; resulting in an ICER of $577.5. The figure shows CAS cost-effectiveness plane. CONCLUSIONS In this analysis, CAS was associated with higher costs and lower effectiveness compared to CEA in symptomatic carotid stenosis patients. The results were driven by the costs of periprocedural major and minor stroke. The costs associated with MI did not impact the results. For CAS to be more effective, it needs to be performed in patients with longer survival, in patients at a high surgical-risk, or at a lower procedural costs plus lower rates of periprocedural and annual minor strokes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".