Renal Sympathetic Denervation for Hypertension.

Denervation Endovascular Hypertension Radiofrequency Renal Ultrasound

Journal

Kidney international reports
ISSN: 2468-0249
Titre abrégé: Kidney Int Rep
Pays: United States
ID NLM: 101684752

Informations de publication

Date de publication:
Oct 2022
Historique:
received: 17 03 2022
revised: 16 05 2022
accepted: 27 06 2022
entrez: 11 10 2022
pubmed: 12 10 2022
medline: 12 10 2022
Statut: epublish

Résumé

Arterial hypertension is the most prevalent global modifiable risk factor for cardiovascular morbidity and mortality. Despite the availability of numerous pharmacologic treatments, many patients do not achieve guideline-recommended blood pressure targets. Therefore, renal sympathetic denervation (RDN), a process in which catheter-directed techniques are used to ablate portions of the renal artery to reduce sympathetic activity, has been extensively investigated as a complementary and nonpharmacologic approach for the treatment of arterial hypertension. This review seeks to discuss the pathophysiological rationale of this strategy, to survey its history and development, and to highlight the current clinical evidence and possible future directions of its employment. In sum, RDN has demonstrated itself to be a safe and well-tolerated endovascular intervention that can reliably contribute to improved blood pressure control and, perhaps ultimately, significant cardiovascular prognosis.

Identifiants

pubmed: 36217529
doi: 10.1016/j.ekir.2022.06.019
pii: S2468-0249(22)01478-4
pmc: PMC9546727
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

2129-2140

Informations de copyright

© 2022 International Society of Nephrology. Published by Elsevier Inc.

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Auteurs

Kenneth Guber (K)

Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA.

Ajay J Kirtane (AJ)

Division of Cardiology, Columbia University Irving Medical Center/NewYork-Presbyterian Hospital, New York, New York, USA.
Cardiovascular Research Foundation, New York, New York, USA.

Classifications MeSH