Evidence map›Paper›PMID 38979821›Full record

SynthesisJournal of the American Heart Association2024

Patient-Specific Factors Predicting Renal Denervation Response in Patients With Hypertension: A Systematic Review and Meta-Analysis.

Xin-Ru Hu, Guang-Zhi Liao, Jun-Wen Wang, Yu-Yang Ye, Xue-Feng Chen, Lin Bai, Fan-Fan Shi, Kai Liu, Yong Peng

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Journal of the American Heart Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Renal Denervation in Cardiovascular Diseases: Mechanisms, Evidence, and Expanding Applications.Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions · 2026
    Review
  2. Overcoming variable response in renal denervation: Key strategies for reliable clinical implementation.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
    Review
  3. Patient Selection and Renal Denervation.Current hypertension reports · 2026
    Review
  4. Article
  5. Review
  6. Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Xin-Ru HuDepartment of Cardiology West China Hospital, Sichuan University Chengdu Sichuan People's Republic of China.ORCID 0009-0006-7720-402X
Guang-Zhi LiaoDepartment of Cardiology West China Hospital, Sichuan University Chengdu Sichuan People's Republic of China.
Jun-Wen WangDepartment of Cardiology West China Hospital, Sichuan University Chengdu Sichuan People's Republic of China.ORCID 0000-0003-2426-8944
Yu-Yang YeDepartment of Cardiology West China Hospital, Sichuan University Chengdu Sichuan People's Republic of China.ORCID 0000-0003-3142-1951
Xue-Feng ChenDepartment of Cardiology West China Hospital, Sichuan University Chengdu Sichuan People's Republic of China.ORCID 0009-0001-0756-6766
Lin BaiDepartment of Cardiology West China Hospital, Sichuan University Chengdu Sichuan People's Republic of China.ORCID 0000-0003-2424-2947
Fan-Fan ShiDepartment of Clinical Research and Management, Center of Biostatistics, Design, Measurement and Evaluation (CBDME) West China Hospital, Sichuan University Chengdu Sichuan People's Republic of China.ORCID 0009-0006-5121-6831
Kai LiuDepartment of Cardiology West China Hospital, Sichuan University Chengdu Sichuan People's Republic of China.ORCID 0000-0002-7439-1572
Yong PengDepartment of Cardiology West China Hospital, Sichuan University Chengdu Sichuan People's Republic of China.ORCID 0000-0001-9562-4622

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe accurate selection of patients likely to respond to renal denervation (RDN) is crucial for optimizing treatment outcomes in patients with hypertension. This systematic review was designed to evaluate patient-specific factors predicting the RDN response. METHODS AND

resultsWe focused on individuals with hypertension who underwent RDN. Patients were categorized based on their baseline characteristics. The primary outcome was blood pressure (BP) reduction after RDN. Both randomized controlled trials and nonrandomized studies were included. We assessed the risk of bias using corresponding tools and further employed the Grading of Recommendations Assessment, Development, and Evaluation approach to assess the overall quality of evidence. A total of 50 studies were ultimately included in this systematic review, among which 17 studies were for meta-analysis. Higher baseline heart rate and lower pulse wave velocity were shown to be associated with significant antihypertensive efficacy of RDN on 24-hour systolic BP reduction (weighted mean difference, -4.05 [95% CI, -7.33 to -0.77]; weighted mean difference, -7.20 [95% CI, -9.79 to -4.62], respectively). In addition, based on qualitative analysis, higher baseline BP, orthostatic hypertension, impaired baroreflex sensitivity, and several biomarkers are also reported to be associated with significant BP reduction after RDN.

conclusionsIn patients with hypertension treated with the RDN, higher heart rate, and lower pulse wave velocity were associated with significant BP reduction after RDN. Other factors, including higher baseline BP, hypertensive patients with orthostatic hypertension, BP variability, impaired cardiac baroreflex sensitivity, and some biomarkers are also reported to be associated with a better BP response to RDN.

Indexed as

Blood PressureHypertensionKidneyBaroreflexHeart RateHumansPulse Wave AnalysisRenal ArterySympathectomyTreatment Outcomehypertensionnonresponderspredictionrenal denervationresponders

Identifiers

PMID38979821
PMCPMC11292764

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.