Angiographic derived endothelial shear stress: a new predictor of atherosclerotic disease progression
European Heart Journal - Cardiovascular Imaging 20(3): 314-322
Article 2018 English
Authors
CB
Christos V. Bourantas
AR
Anantharaman Ramasamy
AK
Alexios Karagiannis
Abstract
1 min read
To examine the efficacy of angiography derived endothelial shear stress (ESS) in predicting atherosclerotic disease progression. Thirty-five patients admitted with ST-elevation myocardial infarction that had three-vessel intravascular ultrasound (IVUS) immediately after revascularization and at 13 months follow-up were included. Three dimensional (3D) reconstruction of the non-culprit vessels were performed using (i) quantitative coronary angiography (QCA) and (ii) methodology involving fusion of IVUS and biplane angiography. In both models, blood flow simulation was performed and the minimum predominant ESS was estimated in 3 mm segments. Baseline plaque characteristics and ESS were used to identify predictors of atherosclerotic disease progression defied as plaque area increase and lumen reduction at follow-up. Fifty-four vessels were included in the final analysis. A moderate correlation was noted between ESS estimated in the 3D QCA and the IVUS-derived models (r = 0.588, P < 0.001); 3D QCA accurately identified segments exposed to low (<1 Pa) ESS in the IVUS-based reconstructions (AUC: 0.793, P < 0.001). Low 3D QCA-derived ESS (<1.75 Pa) was associated with an increase in plaque area, burden, and necrotic core at follow-up. In multivariate analysis, low ESS estimated either in 3D QCA [odds ratio (OR): 2.07, 95% confidence interval (CI): 1.17–3.67; P = 0.012) or in IVUS (<1 Pa; OR: 2.23, 95% CI: 1.23–4.03; P = 0.008) models, and plaque burden were independent predictors of atherosclerotic disease progression; 3D QCA and IVUS-derived models had a similar accuracy in predicting disease progression (AUC: 0.826 vs. 0.827, P = 0.907). 3D QCA-derived ESS can predict disease progression. Further research is required to examine its value in detecting vulnerable plaques.
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Antonis I. Sakellarios, Christos V. Bourantas, Stella‐Lida Papadopoulou, Pieter Kitslaar, Chrysafios Girasis, Gregg W. Stone, Johan H. C. Reiber, Lampros K. Michalis, Patrick W. Serruys, Pim J. de Feyter, Héctor M. García‐García, Dimitrios I. Fotiadis
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