Predicting Renal Denervation Response in Resistant High Blood Pressure by Arterial Stiffness Assessment: A Systematic Review
Background: Accurately selecting hypertensive candidates for renal denervation (RDN) therapy is required, as one-third of patients who undergo RDN are non-responders. We aimed to systematically review the literature on RDN response prediction using arterial stiffness assessment, optimizing the selec...
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Veröffentlicht in: | Journal of clinical medicine 2022-08, Vol.11 (16), p.4837 |
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Sprache: | eng |
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Zusammenfassung: | Background: Accurately selecting hypertensive candidates for renal denervation (RDN) therapy is required, as one-third of patients who undergo RDN are non-responders. We aimed to systematically review the literature on RDN response prediction using arterial stiffness assessment, optimizing the selection of patients referred for interventional blood pressure lowering procedures. Methods: A literature search was performed in MEDLINE, Embase, Scopus, and Cochrane databases to retrieve potential eligible studies from the inception to 30 June 2022. Results: Ten studies were finally included in this systematic review. Studies consistently documented that invasive pulse wave velocity (PWV) was correlated with RDN’s significant success. Nevertheless, non-invasive ambulatory arterial stiffness index and PWV derived from ambulatory blood pressure monitoring were independent predictors of blood pressure response (p = 0.04 and p < 0.0001). In some studies, magnetic resonance imaging parameters of arterial stiffness (ascending aortic distensibility, total arterial compliance) were correlated with blood pressure reduction (AUC = 0.828, p = 0.006). Conclusions: Assessing arterial stiffness prior to RDN predicted procedural success, since stiffness parameters were strongly correlated with a significant blood pressure response. Our endeavor should be tackled as a step forward in selecting appropriate hypertensive patients scheduled for RDN therapy. Non-invasive measurements could be an alternative to invasive parameters for response prediction. |
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ISSN: | 2077-0383 2077-0383 |
DOI: | 10.3390/jcm11164837 |