Cost-effectiveness of Interventional therapies for management of Treatment-resistant hypertension: systematic review of pharmacoeconomic studies
Bibliographic record
Abstract
Abstract Background Treatment resistant hypertension (TRH) is defined as uncontrolled blood pressure (>140/90 mm Hg) after treatment with the intensified dose of three standard antihypertensive drugs. Management of TRH involves addition of fourth line drugs on standard care or interventional therapies (Renal denervation, Baroreceptor activation, Central venous anastomosis). However, evidence concerning cost-effectiveness of interventional therapies is inconclusive. Objective: This systematic review was conducted to extract the level of evidence on cost-effectiveness of interventional therapies for TRH. Method We systematically searched articles written in English language since January 2000 to January 2020 from the following databases: PubMed/Medline, Ovid/Medline, Embase, Scopus, Web of Science, Google scholar and other relevant sources. Key findings Twelve pharmacoeconomic studies were included in this systematic review. Renal denervation (RDN) is the most commonly studied intervention therapy for treatment of TRH. Participants included in the study vary from age 18-99 years. The incremental cost-effectiveness ratio (ICER) of RDN ranged from $1,709.84 per QALY gained in Netherlands to 66,380.3 per QALY gained in Australia. RDN was cost-effective in high-risk patients in UK, Australia, Canada, Netherlands, USA, Germany, Russia and Korea. The cost-effectiveness was influenced by the magnitude of effect of RDN on systolic blood pressure, the rate of RDN nonresponders, and the procedure costs of RDN and assumption of long-term time horizon. However, the ICER of RDN in Mexico was above MXN$ 139,000 GDP/capita of the country. The ICER of implantable carotid body stimulator was $64,400 per QALYs gained. The cost-effectiveness of baroreceptor activation didn’t improve with age. Conclusion Overall cost-effectiveness of interventional therapies for treatment of TRH was inconclusive based on the current available evidence. Therefore, strong clinical trials and pharmacoeconomic evaluations from different perspectives in various candidate populations are needed to generate adequate clinical and cost-effectiveness evidence for using interventional therapies in treatment of treatment resistant hypertension.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".