The Role of Renal Denervation in the Treatment of Hypertension in Canada: A Case-Based Discussion from the Canadian Hypertension Specialists Society
Bibliographic record
Abstract
Over one-third of Canadians with hypertension do not achieve recommended blood pressure (BP) targets despite availability of effective treatments. Renal sympathetic nerve denervation (RDN) is a recently approved, minimally invasive treatment for hypertension being offered in multiple Canadian centers. How best to implement this procedure in contemporary Canadian clinical practice remains unclear. Herein, we provide a Canadian hypertension specialist viewpoint on use of RDN in Canada. We review the rationale for, and evidence supporting, the use of RDN and discuss, using 2 clinical cases, its potential therapeutic role. We note that RDN has effectively lowered BP in multiple, sham-controlled, randomized clinical trials and has a favorable safety profile. Economic models indicate that it is cost-effective in the Canadian context. Conversely, the BP-lowering effect is relatively modest; no well-established method to pre-identify responders exists; cardiovascular endpoint data supporting use of RDN are lacking; and no clear funding model is currently in place in Canada. Accordingly, we suggest that use of RDN be reserved for willing patients with severely elevated BP despite the use of first-line conventional therapies who have had secondary causes excluded. Examples include patients with resistant hypertension or moderate or severe hypertension and multiple drug intolerance syndrome. In view of its recent approval and known operator-dependency, RDN should be offered solely through programmatic, multidisciplinary collaboration between hypertension specialists and experienced interventionalists using a shared decision-making approach with the patient. Funding deployment should target such programs and sites should carefully monitor their outcomes to confirm comparability to the published literature.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".