Prediction of coronary plaque progression using biomechanical factors and vascular characteristics based on computed tomography angiography

Impact of endothelial shear stress on coronary atherosclerotic plaque progression and composition: A meta-analysis and systematic review

BACKGROUND AND AIMS

Intracoronary pressure gradients and translesional flow patterns have been correlated with coronary plaque progression and lesion destabilization. In this study, we aimed to determine the relationship between endothelial shear stress and plaque progression and to evaluate the effect of shear forces on coronary plaque features.

METHODS

A systematic review was conducted in medical on-line databases. Selected were studies including human participants who underwent coronary anatomy assessment with computational fluid dynamics (CFD)-based wall shear stress (WSS) calculation at baseline with anatomical evaluation at follow-up. A total of six studies were included for data extraction and analysis.

RESULTS

The meta-analysis encompassed 31'385 arterial segments from 136 patients. Lower translesional WSS values were significantly associated with a reduction in lumen area (mean difference -0.88, 95% CI -1.13 to -0.62), an increase in plaque burden (mean difference 4.32, 95% CI 1.65 to 6.99), and an increase in necrotic core area (mean difference 0.02, 95% CI 0.02 to 0.03) at follow-up imaging. Elevated WSS values were associated with an increase in lumen area (mean difference 0.78, 95% CI 0.34 to 1.21) and a reduction in both fibrofatty (mean difference -0.02, 95% CI -0.03 to -0.01) and fibrous plaque areas (mean difference -0.03, 95% CI -0.03 to -0.03).

CONCLUSION

This meta-analysis shows that WSS parameters were related to vulnerable plaque features at follow-up. These results emphasize the impact of endothelial shear forces on coronary plaque growth and composition. Future studies are warranted to evaluate the role of WSS in guiding clinical decision-making.

Plaque Ruptures Are Related to High Plaque Stress and Strain Conditions: Direct Verification by Using In Vivo OCT Rupture Data and FSI Models

BACKGROUND

While it has been hypothesized that high plaque stress and strain may be related to plaque rupture, its direct verification using in vivo coronary plaque rupture data and full 3-dimensional fluid-structure interaction models is lacking in the current literature due to difficulty in obtaining in vivo plaque rupture imaging data from patients with acute coronary syndrome. This case-control study aims to use high-resolution optical coherence tomography-verified in vivo plaque rupture data and 3-dimensional fluid-structure interaction models to seek direct evidence for the high plaque stress/strain hypothesis.

METHODS

In vivo coronary plaque optical coherence tomography data (5 ruptured plaques, 5 no-rupture plaques) were acquired from patients using a protocol approved by the local institutional review board with informed consent obtained. The ruptured caps were reconstructed to their prerupture morphology using neighboring plaque cap and vessel geometries. Optical coherence tomography-based 3-dimensional fluid-structure interaction models were constructed to obtain plaque stress, strain, and flow shear stress data for comparative analysis. The rank-sum test in the nonparametric test was used for statistical analysis.

RESULTS

Our results showed that the average maximum cap stress and strain values of ruptured plaques were 142% (457.70 versus 189.22 kPa; P=0.0278) and 48% (0.2267 versus 0.1527 kPa; P=0.0476) higher than that for no-rupture plaques, respectively. The mean values of maximum flow shear stresses for ruptured and no-rupture plaques were 145.02 dyn/cm2 and 81.92 dyn/cm2 (P=0.1111), respectively. However, the flow shear stress difference was not statistically significant.

CONCLUSIONS

This preliminary case-control study showed that the ruptured plaque group had higher mean maximum stress and strain values. Due to our small study size, larger scale studies are needed to further validate our findings.

Patient-Specific Numerical Simulations of Coronary Artery Hemodynamics and Biomechanics: A Pathway to Clinical Use

PURPOSE

Numerical models that simulate the behaviors of the coronary arteries have been greatly improved by the addition of fluid-structure interaction (FSI) methods. Although computationally demanding, FSI models account for the movement of the arterial wall and more adequately describe the biomechanical conditions at and within the arterial wall. This offers greater physiological relevance over Computational Fluid Dynamics (CFD) models, which assume the walls do not move or deform. Numerical simulations of patient-specific cases have been greatly bolstered by the use of imaging modalities such as Computed Tomography Angiography (CTA), Magnetic Resonance Imaging (MRI), Optical Coherence Tomography (OCT), and Intravascular Ultrasound (IVUS) to reconstruct accurate 2D and 3D representations of artery geometries. The goal of this study was to conduct a comprehensive review on CFD and FSI models on coronary arteries, and evaluate their translational potential.

METHODS

This paper reviewed recent work on patient-specific numerical simulations of coronary arteries that describe the biomechanical conditions associated with atherosclerosis using CFD and FSI models. Imaging modality for geometry collection and clinical applications were also discussed.

RESULTS

Numerical models using CFD and FSI approaches are commonly used to study biomechanics within the vasculature. At high temporal and spatial resolution (compared to most cardiac imaging modalities), these numerical models can generate large amount of biomechanics data.

CONCLUSIONS

Physiologically relevant FSI models can more accurately describe atherosclerosis pathogenesis, and help to translate biomechanical assessment to clinical evaluation.

3D patient-specific modeling and structural finite element analysis of atherosclerotic carotid artery based on computed tomography angiography

The assessment of carotid plaque vulnerability is a relevant clinical information that can help prevent adverse cerebrovascular events. To this aim, in this study, we propose a patient-specific computational workflow to quantify the stress distribution in an atherosclerotic carotid artery, by means of geometric modeling and structural simulation of the plaque and vessel wall. Ten patients were involved in our study. Starting with segmentation of the lumen, calcific and lipid plaque components from computed tomography angiography images, the fibrous component and the vessel wall were semi-automatically reconstructed with an ad-hoc procedure. Finite element analyses were performed using local pressure values derived from ultrasound imaging. Simulation outputs were analyzed to assess how mechanical factors influence the stresses within the atherosclerotic wall. The developed reconstruction method was first evaluated by comparing the results obtained using the automatically generated fibrous component model and the one derived from image segmentation. The high-stress regions in the carotid artery wall around plaques suggest areas of possible rupture. In mostly lipidic and heterogeneous plaques, the highest stresses are localized at the interface between the lipidic components and the lumen, in the fibrous cap.

A spatiotemporal analysis of the left coronary artery biomechanics using fluid-structure interaction models

Biomechanics plays a critical role in coronary artery disease development. FSI simulation is commonly used to understand the hemodynamics and mechanical environment associated with atherosclerosis pathology. To provide a comprehensive characterization of patient-specific coronary biomechanics, an analysis of FSI simulation in the spatial and temporal domains was performed. In the current study, a three-dimensional FSI model of the LAD coronary artery was built based on a patient-specific geometry using COMSOL Multiphysics. The effect of myocardial bridging was simulated. Wall shear stress and its derivatives including time-averaged wall shear stress, wall shear stress gradient, and OSI were calculated across the cardiac cycle in multiple locations. Arterial wall strain (radial, circumferential, and longitudinal) and von Mises stress were calculated. To assess perfusion, vFFR was calculated. The results demonstrated the FSI model could identify regional and transient differences in biomechanical parameters within the coronary artery. The addition of myocardial bridging caused a notable change in von Mises stress and an increase in arterial strain during systole. The analysis performed in this manner takes greater advantage of the information provided in the space and time domains and can potentially assist clinical evaluation.

Machine Learning Coronary Artery Disease Prediction Based on Imaging and Non-Imaging Data

The prediction of obstructive atherosclerotic disease has significant clinical meaning for the decision making. In this study, a machine learning predictive model based on gradient boosting classifier is presented, aiming to identify the patients of high CAD risk and those of low CAD risk. The machine learning methodology includes five steps: the preprocessing of the input data, the class imbalance handling applying the Easy Ensemble algorithm, the recursive feature elimination technique implementation, the implementation of gradient boosting classifier, and finally the model evaluation, while the fine tuning of the presented model was implemented through a randomized search optimization of the model’s hyper-parameters over an internal 3-fold cross-validation. In total, 187 participants with suspicion of CAD previously underwent CTCA during EVINCI and ARTreat clinical studies and were prospectively included to undergo follow-up CTCA. The predictive model was trained using imaging data (geometrical and blood flow based) and non-imaging data. The overall predictive accuracy of the model was 0.81, using both imaging and non-imaging data. The innovative aspect of the proposed study is the combination of imaging-based data with the typical CAD risk factors to provide an integrated CAD risk-predictive model.

Image-based biomechanical modeling for coronary atherosclerotic plaque progression and vulnerability prediction

Atherosclerotic plaque progression and rupture play an important role in cardiovascular disease development and the final drastic events such as heart attack and stroke. Medical imaging and image-based computational modeling methods advanced considerably in recent years to quantify plaque morphology and biomechanical conditions and gain a better understanding of plaque evolution and rupture process. This article first briefly reviewed clinical imaging techniques for coronary thin-cap fibroatheroma (TCFA) plaques used in image-based computational modeling. This was followed by a summary of different types of biomechanical models for coronary plaques. Plaque progression and vulnerability prediction studies based on image-based computational modeling were reviewed and compared. Much progress has been made and a reasonable high prediction accuracy has been achieved. However, there are still some inconsistencies in existing literature on the impact of biomechanical and morphological factors on future plaque behavior, and it is very difficult to perform direct comparison analysis as differences like image modality, biomechanical factors selection, predictive models, and progression/vulnerability measures exist among these studies. Encouraging data and model sharing across the research community would partially resolve these differences, and possibly lead to clearer assertive conclusions. In vivo image-based computational modeling could be used as a powerful tool for quantitative assessment of coronary plaque vulnerability for potential clinical applications.

A 2D FSI mathematical model of blood flow to analyze the hyper-viscous effects in atherosclerotic COVID patients

Recent researches on COVID 19 has been extended to analyze the various morphological and anatomical changes in a patient's body due to the invasion of the virus. These latest studies have concluded that there happens a high rise in the viscosity of the blood in a COVID 19 patient, supported by the extensive analysis of the clinical data. In the present paper, a mathematical model in the form of a differential equation system has been proposed to disclose the various changes that occur in the flow across the stenosis of an arterial segment. The consequences of the hyperviscosity of blood on the blood flow characteristics in a stenosed artery are analyzed by solving the model using a finite element method (FEM) solver. A laminar flow coupled with solid mechanics through the Fluid-Structure Interaction (FSI) interface has been studied using an Arbitrary Lagrangian-Eulerian (ALE) method. For the first time, the mathematical model was used to analyze the hyper-viscous flow condition in COVID 19 patients. The present research is mainly based on the numerous clinical reports enlisting the various morphological, hematological, and rheological changes in the blood.

Serum Biomarkers in Carotid Artery Disease

Carotid artery disease is considered a major cause of strokes and there is a need for early disease detection and management. Although imaging techniques have been developed for the diagnosis of carotid artery disease and different imaging-based markers have been proposed for the characterization of atherosclerotic plaques, there is still need for a definition of high-risk plaques in asymptomatic patients who may benefit from surgical intervention. Measurement of circulating biomarkers is a promising method to assist in patient-specific disease management, but the lack of robust clinical evidence limits their use as a standard of care. The purpose of this review paper is to present circulating biomarkers related to carotid artery diagnosis and prognosis, which are mainly provided by statistical-based clinical studies. The result of our investigation showed that typical well-established inflammatory biomarkers and biomarkers related to patient lipid profiles are associated with carotid artery disease. In addition to this, more specialized types of biomarkers, such as endothelial and cell adhesion, matrix degrading, and metabolic biomarkers seem to be associated with different carotid artery disease outputs, assisting vascular specialists in selecting patients at high risk for stroke and in need of intervention.

In Vivo Based Fluid-Structure Interaction Biomechanics of the Left Anterior Descending Coronary Artery

A fluid-structure interaction-based biomechanical model of the entire left anterior descending coronary artery is developed from in vivo imaging via the finite element method in this paper. Included in this investigation is ventricle contraction, three-dimensional motion, all angiographically visible side branches, hyper/viscoelastic artery layers, non-Newtonian and pulsatile blood flow, and the out-of-phase nature of blood velocity and pressure. The fluid-structure interaction model is based on in vivo angiography of an elite athlete's entire left anterior descending coronary artery where the influence of including all alternating side branches and the dynamical contraction of the ventricle is investigated for the first time. Results show the omission of side branches result in a 350% increase in peak wall shear stress and a 54% decrease in von Mises stress. Peak von Mises stress is underestimated by up to 80% when excluding ventricle contraction and further alterations in oscillatory shear indices are seen, which provide an indication of flow reversal and has been linked to atherosclerosis localization. Animations of key results are also provided within a video abstract. We anticipate that this model and results can be used as a basis for our understanding of the interaction between coronary and myocardium biomechanics. It is hoped that further investigations could include the passive and active components of the myocardium to further replicate in vivo mechanics and lead to an understanding of the influence of cardiac abnormalities, such as arrythmia, on coronary biomechanical responses.

Site specific prediction of atherosclerotic plaque progression using computational biomechanics and machine learning

Atheromatic plaque progression is considered as a typical pathological condition of arteries and although atherosclerosis is considered as a systemic inflammatory disorder, atheromatic plaque is not uniformly distributed in the arterial tree. Except for the systematic atherosclerosis risk factors, biomechanical forces, LDL concentration and artery geometry contribute to the atherogenesis and atherosclerotic plaque evolution. In this study, we calculate biomechanical forces acting within the artery and we develop a machine learning model for the prediction of atheromatic plaque progression. 1018 coronary sites of 3 mm, derived by 40 individuals, are utilized to develop the model and after the implementation of 4 different tree based prediction schemes, we achieve a prediction accuracy of 0.84. The best accuracy was achieved by the implementation of a tree-based classifier, the Random Forest classifier, after a ranking feature selection methodology. The novel aspect of the proposed methodology is the implementation of machine learning models in order to address the cardiovascular data modeling, aiming to predict the occurrence of an outcome and not to investigate the association of input features.

Biomechanical Forces and Atherosclerosis: From Mechanism to Diagnosis and Treatment

The article provides an overview of current views on the role of biomechanical forces in the pathogenesis of atherosclerosis. The importance of biomechanical forces in maintaining vascular homeostasis is considered. We provide descriptions of mechanosensing and mechanotransduction. The roles of wall shear stress and circumferential wall stress in the initiation, progression and destabilization of atherosclerotic plaque are described. The data on the possibilities of assessing biomechanical factors in clinical practice and the clinical significance of this approach are presented. The article concludes with a discussion on current therapeutic approaches based on the modulation of biomechanical forces.