Cost effectiveness of transcatheter aortic valve replacement compared to medical management in inoperable patients with severe aortic stenosis: Canadian analysis based on the PARTNER Trial Cohort B findings
Bibliographic record
Abstract
OBJECTIVE: The only effective treatment for severe aortic stenosis (AS) is valve replacement. However, many patients with co-existing conditions are ineligible for surgical valve replacement, historically leaving medical management (MM) as the only option which has a poor prognosis. Transcatheter Aortic Valve Replacement (TAVR) is a less invasive replacement method. The objective was to estimate cost-effectiveness of TAVR via transfemoral access vs MM in surgically inoperable patients with severe AS from the Canadian public healthcare system perspective. METHODS: A cost-effectiveness analysis of TAVR vs MM was conducted using a deterministic decision analytic model over a 3-year time horizon. The PARTNER randomized controlled trial results were used to estimate survival, utilities, and some resource utilization. Costs included the valve replacement procedure, complications, hospitalization, outpatient visits/tests, and home/nursing care. Resources were valued (2009 Canadian dollars) using costs from the Ontario Case Costing Initiative (OCCI), Ontario Ministry of Health and Long-Term Care and Ontario Drug Benefits Formulary, or were estimated using relative costs from a French economic evaluation or clinical experts. Costs and outcomes were discounted 5% annually. The effect of uncertainty in model parameters was explored in deterministic and probabilistic sensitivity analysis. RESULTS: The incremental cost-effectiveness ratio (ICER) was $32,170 per quality-adjusted life year (QALY) gained for TAVR vs MM. When the time horizon was shortened to 24 and 12 months, the ICER increased to $52,848 and $157,429, respectively. All other sensitivity analysis returned an ICER of less than $50,000/QALY gained. LIMITATIONS: A limitation was lack of availability of Canadian-specific resource and cost data for all resources, leaving one to rely on clinical experts and data from France to inform certain parameters. CONCLUSIONS: Based on the results of this analysis, it can be concluded that TAVR is cost-effective compared to MM for the treatment of severe AS in surgically inoperable patients.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".