Cost-effectiveness analysis of radiofrequency renal denervation for uncontrolled hypertension in Canada
Bibliographic record
Abstract
AIMS: Catheter-based radiofrequency renal denervation (RF RDN) is an interventional treatment for uncontrolled hypertension. This analysis explored the therapy's lifetime cost-effectiveness in a Canadian healthcare setting. MATERIALS AND METHODS: A decision-analytic Markov model was used to project health events, costs, and quality-adjusted life years over a lifetime horizon. Seven primary health states were modeled, including hypertension alone, stroke, myocardial infarction (MI), other symptomatic coronary artery disease, heart failure (HF), end-stage renal disease (ESRD), and death. Multivariate risk equations and a meta-regression of hypertension trials informed transition probabilities. Contemporary clinical evidence from the SPYRAL HTN-ON MED trial informed the base case treatment effect (-4.9 mmHg change in office systolic blood pressure (oSBP) observed vs. sham control). Costs were sourced from published literature. A 1.5% discount rate was applied to costs and effects, and the resulting incremental cost-effectiveness ratio (ICER) was evaluated against a willingness-to-pay threshold of $50,000 per QALY gained. Extensive scenario and sensitivity analyses were performed. RESULTS: Over 10 years, RF RDN resulted in relative risk reduction in clinical events (0.80 for stroke, 0.88 for MI, and 0.72 for HF). Under the base case assumptions, RF RDN was found to add 0.51 (15.81 vs. 15.30) QALYs at an incremental cost of $6,031 ($73,971 vs. $67,040) over a lifetime, resulting in an ICER of $11,809 per QALY gained. Cost-effectiveness findings were found robust in sensitivity analyses, with the 95% confidence interval for the ICER based on 10,000 simulations ranging from $4,489 to $22,587 per QALY gained. LIMITATIONS AND CONCLUSION: Model projections suggest RF RDN, under assumed maintained treatment effect, is a cost-effective treatment strategy for uncontrolled hypertension in the Canadian healthcare system based on meaningful reductions in clinical events.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".