Predicting Disease Progression and Mortality in Aortic Stenosis: A Systematic Review of Imaging Biomarkers and Meta-Analysis.

Nchimi Longang, Alain; Dibato, John E.; DAVIN, Laurent; SCHOYSMAN, Laurent; Oury, Cécile; Lancellotti, Patrizio

doi:10.3389/fcvm.2018.00112

Article (Scientific journals)

Predicting Disease Progression and Mortality in Aortic Stenosis: A Systematic Review of Imaging Biomarkers and Meta-Analysis.

Nchimi Longang, Alain; Dibato, John E.; DAVIN, Laurent et al.

2018 • In Frontiers in Cardiovascular Medicine, 5, p. 112

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/228945

DOI
10.3389/fcvm.2018.00112

PubMed
30186838

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Keywords :

aortic stenosis; calcification; imaging biomarker; meta-analysis; myocardial fibrosis; remodeling

Abstract :

[en] Background: Detecting among patients with aortic stenosis (AS) those who are likely to rapidly progress, yet potentially benefiting from prophylactic aortic valve replacement, is needed for improved patient care. The objective of this study was to evaluate the role of imaging biomarkers in predicting the progression to clinical symptoms and death in patients with AS. Methods: We searched the Pubmed and the International Clinical Trials Registry Platform databases for studies including patients with AS, and investigating imaging techniques, published in any language until Jan 1, 2018. Eligible sets of data include effect of imaging biomarkers relative to: (1) Overall mortality, (2) Cardiac mortality, and (3) Overall events (Symptom onset and Major Adverse Cardiovascular Events). Meta-analysis was used to examine associations between the imaging biomarkers and outcomes of AS using Random Effect models. Results: Eight studies and 1,639 patients were included after systematic review. Four studies investigated aortic valve calcification (AVC) whereas the remaining investigated biomarkers provided by cardiac magnetic resonance (CMR). Four articles investigated the presence of midwall fibrosis on late-gadolinium enhancement imaging, three reported its extent (LGE%) and two, the myocardial extracellular volume (ECV). By decreasing strength of association, there were significant associations between cardiac mortality and LGE% [Relative Risk (RR) = 1.05, 95% Confidence Interval (CI) 1.01-1.10]; overall mortality and AVC (RR = 1.19, 95%CI: 1.05-1.36); overall events and ECV (RR = 1.68, 95%CI: 1.17-2.41); cardiac mortality and midwall fibrosis (RR = 2.88, 95%CI: 1.12-7.39). Conclusion: AVC and myocardial fibrosis imaging biomarkers predict the outcomes in AS, and help understanding AS pathophysiology and setting therapeutic targets.

Disciplines :

Cardiovascular & respiratory systems

Author, co-author :

Nchimi Longang, Alain

Dibato, John E.

DAVIN, Laurent

SCHOYSMAN, Laurent

Oury, Cécile ; Université de Liège - ULiège > Département des sciences biomédicales et précliniques > GIGA-R : GIGA - Cardiovascular Sciences

Lancellotti, Patrizio ; Université de Liège - ULiège > Département des sciences cliniques > Cardiologie - Pathologie spéciale et réhabilitation

Language :

English

Title :

Predicting Disease Progression and Mortality in Aortic Stenosis: A Systematic Review of Imaging Biomarkers and Meta-Analysis.

Publication date :

2018

Journal title :

Frontiers in Cardiovascular Medicine

eISSN :

2297-055X

Publisher :

Frontiers Research Foundation, Lausanne, Switzerland

Volume :

Pages :

112

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 29 October 2018

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