HEALTH TECHNOLOGY ASSESSMENTS REPORTING COST-EFFECTIVENESS OF TRANSCATHETER AORTIC VALVE IMPLANTATION
Bibliographic record
Abstract
OBJECTIVES: Made available since 2002, transcatheter aortic valve implantation (TAVI) is a minimally invasive new intervention which can provide significant survival improvement to patients with aortic stenosis. However, TAVI is expensive and currently not reimbursed by many governments. Some governments and institutions have been conducting health technology assessments (HTAs) to inform their reimbursement decisions. The aim of the present study is to review HTAs that have relied on a cost-effectiveness analysis to inform reimbursement decisions of TAVI. METHODS: A systematic literature review was conducted among published literature as well as reports released by HTA agencies. Predetermined inclusion and exclusion criteria, following the Preferred Reporting System for Systematic Reviews and Meta-Analysis guidelines, were used to select relevant HTAs. The selected papers were assessed against the Consolidated Health Economic Evaluation Reporting Standards. RESULTS: HTAs on TAVI from three countries were available for this review: Canada, Belgium, and the United Kingdom. All three HTAs used the Placement of Aortic Transcatheter Valve (PARTNER) trial data with Markov models to estimate the incremental cost effectiveness ratio. The three HTAs recommended conditional reimbursement for TAVI for otherwise inoperable patients. The HTAs did not use clear methods to estimate the health-related utility which ultimately affected their cost-effectiveness results. The UK HTA showed the best value for money (US$20,416 per quality-adjusted life-year). CONCLUSION: All studies found TAVI to be more costly and less effective for high-risk patients suitable for surgery, whereas TAVI was consistently found to be cost effective for otherwise 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 | 0.070 | 0.390 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.031 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".