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基于血管内超声的血流动力学模拟模型用于人类冠状动脉斑块进展研究:组成部分、相关性及预测分析

IVUS-based FSI models for human coronary plaque progression study: components, correlation and predictive analysis.

作者信息

Wang Liang, Wu Zheyang, Yang Chun, Zheng Jie, Bach Richard, Muccigrosso David, Billiar Kristen, Maehara Akiko, Mintz Gary S, Tang Dalin

机构信息

Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA, USA.

出版信息

Ann Biomed Eng. 2015 Jan;43(1):107-21. doi: 10.1007/s10439-014-1118-1. Epub 2014 Sep 23.

Abstract

Atherosclerotic plaque progression is believed to be associated with mechanical stress conditions. Patient follow-up in vivo intravascular ultrasound coronary plaque data were acquired to construct fluid-structure interaction (FSI) models with cyclic bending to obtain flow wall shear stress (WSS), plaque wall stress (PWS) and strain (PWSn) data and investigate correlations between plaque progression measured by wall thickness increase (WTI), cap thickness increase (CTI), lipid depth increase (LDI) and risk factors including wall thickness (WT), WSS, PWS, and PWSn. Quarter average values (n = 178-1016) of morphological and mechanical factors from all slices were obtained for analysis. A predictive method was introduced to assess prediction accuracy of risk factors and identify the optimal predictor(s) for plaque progression. A combination of WT and PWS was identified as the best predictor for plaque progression measured by WTI. Plaque WT had best overall correlation with WTI (r = -0.7363, p < 1E-10), cap thickness (r = 0.4541, p < 1E-10), CTI (r = -0.4217, p < 1E-8), LD (r = 0.4160, p < 1E-10), and LDI (r = -0.4491, p < 1E-10), followed by PWS (with WTI: (r = -0.3208, p < 1E-10); cap thickness: (r = 0.4541, p < 1E-10); CTI: (r = -0.1719, p = 0.0190); LD: (r = -0.2206, p < 1E-10); LDI: r = 0.1775, p < 0.0001). WSS had mixed correlation results.

摘要

动脉粥样硬化斑块进展被认为与机械应力状况有关。获取患者体内血管内超声冠状动脉斑块数据进行随访,构建具有周期性弯曲的流固耦合(FSI)模型,以获得血流壁面切应力(WSS)、斑块壁面应力(PWS)和应变(PWSn)数据,并研究通过壁厚度增加(WTI)、帽厚度增加(CTI)、脂质深度增加(LDI)测量的斑块进展与包括壁厚度(WT)、WSS、PWS和PWSn在内的危险因素之间的相关性。获取所有切片形态学和力学因素的四分之一平均值(n = 178 - 1016)进行分析。引入一种预测方法来评估危险因素的预测准确性,并确定斑块进展的最佳预测指标。WT和PWS的组合被确定为通过WTI测量的斑块进展的最佳预测指标。斑块WT与WTI(r = -0.7363,p < 1E - 10)、帽厚度(r = 0.4541,p < 1E - 10)、CTI(r = -0.4217,p < 1E - 8)、脂质深度(LD,r = 0.4160,p < 1E - 10)和LDI(r = -0.4491,p < 1E - 10)总体相关性最佳,其次是PWS(与WTI:(r = -0.3208,p < 1E - 10);帽厚度:(r = 0.4541,p < 1E - 10);CTI:(r = -0.1719,p = 0.0190);LD:(r = -0.2206,p < 1E - 10);LDI:r = 0.1775,p < 0.0001)。WSS的相关性结果好坏参半。

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