Literature DB >> 29274686

Fluid-structure interaction models based on patient-specific IVUS at baseline and follow-up for prediction of coronary plaque progression by morphological and biomechanical factors: A preliminary study.

Liang Wang1, Dalin Tang2, Akiko Maehara3, Zheyang Wu4, Chun Yang4, David Muccigrosso5, Jie Zheng5, Richard Bach6, Kristen L Billiar7, Gary S Mintz3.   

Abstract

Plaque morphology and biomechanics are believed to be closely associated with plaque progression. In this paper, we test the hypothesis that integrating morphological and biomechanical risk factors would result in better predictive power for plaque progression prediction. A sample size of 374 intravascular ultrasound (IVUS) slices was obtained from 9 patients with IVUS follow-up data. 3D fluid-structure interaction models were constructed to obtain both structural stress/strain and fluid biomechanical conditions. Data for eight morphological and biomechanical risk factors were extracted for each slice. Plaque area increase (PAI) and wall thickness increase (WTI) were chosen as two measures for plaque progression. Progression measure and risk factors were fed to generalized linear mixed models and linear mixed-effect models to perform prediction and correlation analysis, respectively. All combinations of eight risk factors were exhausted to identify the optimal predictor(s) with highest prediction accuracy defined as sum of sensitivity and specificity. When using a single risk factor, plaque wall stress (PWS) at baseline was the best predictor for plaque progression (PAI and WTI). The optimal predictor among all possible combinations for PAI was PWS + PWSn + Lipid percent + Min cap thickness + Plaque Area (PA) + Plaque Burden (PB) (prediction accuracy = 1.5928) while Wall Thickness (WT) + Plaque Wall Strain (PWSn) + Plaque Area (PA) was the best for WTI (1.2589). This indicated that PAI was a more predictable measure than WTI. The combination including both morphological and biomechanical parameters had improved prediction accuracy, compared to predictions using only morphological features.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Coronary; Fluid–structure interaction; Follow-up study; IVUS; Plaque progression

Mesh:

Year:  2017        PMID: 29274686      PMCID: PMC5783767          DOI: 10.1016/j.jbiomech.2017.12.007

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  33 in total

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Journal:  Arterioscler Thromb Vasc Biol       Date:  2000-05       Impact factor: 8.311

2.  Adapting the Lagrangian speckle model estimator for endovascular elastography: theory and validation with simulated radio-frequency data.

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Journal:  J Acoust Soc Am       Date:  2004-08       Impact factor: 1.840

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Journal:  Am J Physiol       Date:  1992-02

4.  Hemodynamic shear stress and its role in atherosclerosis.

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Journal:  JAMA       Date:  1999-12-01       Impact factor: 56.272

5.  Influence of microcalcifications on vulnerable plaque mechanics using FSI modeling.

Authors:  Danny Bluestein; Yared Alemu; Idit Avrahami; Morteza Gharib; Kris Dumont; John J Ricotta; Shmuel Einav
Journal:  J Biomech       Date:  2008-02-07       Impact factor: 2.712

6.  Combination of plaque burden, wall shear stress, and plaque phenotype has incremental value for prediction of coronary atherosclerotic plaque progression and vulnerability.

Authors:  Michel T Corban; Parham Eshtehardi; Jin Suo; Michael C McDaniel; Lucas H Timmins; Emad Rassoul-Arzrumly; Charles Maynard; Girum Mekonnen; Spencer King; Arshed A Quyyumi; Don P Giddens; Habib Samady
Journal:  Atherosclerosis       Date:  2013-12-01       Impact factor: 5.162

7.  Pulsatile flow and atherosclerosis in the human carotid bifurcation. Positive correlation between plaque location and low oscillating shear stress.

Authors:  D N Ku; D P Giddens; C K Zarins; S Glagov
Journal:  Arteriosclerosis       Date:  1985 May-Jun

8.  In Vivo/Ex Vivo MRI-Based 3D Non-Newtonian FSI Models for Human Atherosclerotic Plaques Compared with Fluid/Wall-Only Models.

Authors:  Chun Yang; Dalin Tang; Chun Yuan; Thomas S Hatsukami; Jie Zheng; Pamela K Woodard
Journal:  Comput Model Eng Sci       Date:  2007-01-01       Impact factor: 1.593

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

Authors:  Liang Wang; Zheyang Wu; Chun Yang; Jie Zheng; Richard Bach; David Muccigrosso; Kristen Billiar; Akiko Maehara; Gary S Mintz; Dalin Tang
Journal:  Ann Biomed Eng       Date:  2014-09-23       Impact factor: 3.934

10.  Morphological and Stress Vulnerability Indices for Human Coronary Plaques and Their Correlations with Cap Thickness and Lipid Percent: An IVUS-Based Fluid-Structure Interaction Multi-patient Study.

Authors:  Liang Wang; Jie Zheng; Akiko Maehara; Chun Yang; Kristen L Billiar; Zheyang Wu; Richard Bach; David Muccigrosso; Gary S Mintz; Dalin Tang
Journal:  PLoS Comput Biol       Date:  2015-12-09       Impact factor: 4.475

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  4 in total

1.  A Multi-Modality Image-Based FSI Modeling Approach for Prediction of Coronary Plaque Progression Using IVUS and OCT Data with Follow-Up.

Authors:  Xiaoya Guo; Don Giddens; David Molony; Chun Yang; Habib Samady; Jie Zheng; Mitsuaki Matsumura; Gary Mintz; Akiko Maehara; Liang Wang; Dalin Tang
Journal:  J Biomech Eng       Date:  2019-05-29       Impact factor: 2.097

2.  Combining morphological and biomechanical factors for optimal carotid plaque progression prediction: An MRI-based follow-up study using 3D thin-layer models.

Authors:  Qingyu Wang; Dalin Tang; Liang Wang; Gador Canton; Zheyang Wu; Thomas S Hatsukami; Kristen L Billiar; Chun Yuan
Journal:  Int J Cardiol       Date:  2019-07-04       Impact factor: 4.164

3.  In Vivo Intravascular Optical Coherence Tomography (IVOCT) Structural and Blood Flow Imaging Based Mechanical Simulation Analysis of a Blood Vessel.

Authors:  Cuiru Sun; Hang Pan; Junjie Jia; Haofei Liu; Jinlong Chen
Journal:  Cardiovasc Eng Technol       Date:  2022-02-02       Impact factor: 2.495

4.  Patient-Specific CT-Based Fluid-Structure-Interaction Aorta Model to Quantify Mechanical Conditions for the Investigation of Ascending Aortic Dilation in TOF Patients.

Authors:  Heng Zuo; Yunfei Ling; Peng Li; Qi An; Xiaobo Zhou
Journal:  Comput Math Methods Med       Date:  2020-08-08       Impact factor: 2.238

  4 in total

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