Coronary computed tomography angiography study on the relationship between the Ramus Intermedius and Atherosclerosis in the bifurcation of the left main coronary artery

OBJECTIVE

This study aimed to explore the relationship between the ramus intermedius (RI) and atherosclerosis in the bifurcation of the left coronary artery (LCA).

METHODS

Screening patients who underwent CCTA from January to September 2021, 100 patients with RI (RI group) and 100 patients without RI (no-RI group) were randomly enrolled, Evaluation of RI distribution characteristics and left main coronary artery(LM),Left anterior descending branch(LAD),left circumflex branch(LCX) proximal segment plaque distribution, measurement of LAD-LCX bifurcation angle(∠LAD-LCX),Comparison of the three distribution characteristics with the incidence of plaques in the left main trunk bifurcation area (LM, LAD, LCX) between groups and within the RI group.

RESULTS

The difference in the incidence of plaques in the proximal LCX and the LM between the RI group and the no-RI group were not statistically significant (P > 0.05). The incidence of plaques in the proximal LAD in the RI group was significantly higher than that in the non-RI group (77% versus 53%, P < 0.05). However, there was no statistically significant difference between the two groups after PSM. A univariate logistic regression analysis revealed that an RI was a risk factor for plaque formation in the proximal LAD (P < 0.001), and a multivariate logistic regression analysis revealed that an RI was not an independent risk factor for plaque formation in the proximal LAD (P > 0.05). When compared within the RI group, the difference in the incidence of plaques in the proximal segment of LAD, the proximal segment of LCX, and the LM among the different distribution groups of RI was not statistically significant, respectively (P > 0.05).

CONCLUSION

RI is not an independent risk factor for atherosclerosis in the left coronary artery bifurcation zone, but it may indirectly increase the risk of atherosclerosis in the proximal segment of the LAD.

A proof-of-concept study for the simulation of blood flow in a post arterial segment for different blood rheology models

Cardiovascular disease (CVD) and especially atherosclerosis are chronic inflammatory diseases which cause the atherosclerotic plaque growth in the arterial vessels and the blood flow reduction. Stents have revolutionized the treatment of this disease to a great extent by restoring the blood flow in the vessel. The present study investigates the performance of the blood flow after stent implantation in patient-specific coronary artery and demonstrates the effect of using Newtonian vs. non-Newtonian blood fluid models in the distribution of endothelial shear stress. In particular, the Navier-Stokes and continuity equations were employed, and three non-Newtonian fluid models were investigated (Carreau, Carreau-Yasuda and the Casson model). Computational finite elements models were used for the simulation of blood flow. The comparison of the results demonstrates that the Newtonian fluid model underestimates the calculation of Endothelial Shear Stress, while the three non-Newtonian fluids present similar distribution of shear stress. Keywords: Blood flow dynamics, stented artery, non-Newtonian fluid. Clinical Relevance- This work demonstrates that when blood flow modeling is performed at stented arteries and predictive models are developed, the non-Newtonian nature of blood must be considered.

Error Propagation in the Simulation of Atherosclerotic Plaque Growth and the Prediction of Atherosclerotic Disease Progression

Assessments of coronary artery disease can be achieved using non-invasive computed tomography coronary angiography (CTCA). CTCA can be further used for the 3D reconstruction of the coronary arteries and the development of computational models. However, image acquisition and arterial reconstruction introduce an error which can be propagated, affecting the computational results and the accuracy of diagnostic and prognostic models. In this work, we investigate the effect of an imaging error, propagated to a diagnostic index calculated using computational modelling of blood flow and then to prognostic models based on plaque growth modelling or binary logistic predictive modelling. The analysis was performed utilizing data from 20 patients collected at two time points with interscan period of six years. The collected data includes clinical and risk factors, biological and biohumoral data, and CTCA imaging. The results demonstrated that the error propagated and may have significantly affected some of the final outcomes. The calculated propagated error seemed to be minor for shear stress, but was major for some variables of the plaque growth model. In parallel, in the current analysis SmartFFR was not considerably affected, with the limitation of only one case located into the gray zone.

A proof-of-concept study for the prediction of the de-novo atherosclerotic plaque development using finite elements. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:4354-4357. [PMID: 34892184 DOI: 10.1109/embc46164.2021.9629792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The type of the atherosclerotic plaque has significant clinical meaning since plaque vulnerability depends on its type. In this work, we present a computational approach which predicts the development of new plaques in coronary arteries. More specifically, we employ a multi-level model which simulates the blood fluid dynamics, the lipoprotein transport and their accumulation in the arterial wall and the triggering of inflammation using convection-diffusion-reaction equations and in the final level, we estimate the plaque volume which causes the arterial wall thickening. The novelty of this work relies on the conceptual approach that using the information from 94 patients with computed tomography coronary angiography (CTCA) imaging at two time points we identify the correlation of the computational results with the real plaque components detected in CTCA. In the next step, we use these correlations to generate two types of de-novo plaques: calcified and non-calcified. Evaluation of the model's performance is achieved using eleven patients, who present de-novo plaques at the follow-up imaging. The results demonstrate that the computationally generated plaques are associated significantly with the real plaques indicating that the proposed approach could be used for the prediction of specific plaque type formation.

Emulating Early Atherosclerosis in a Vascular Microphysiological System Using Branched Tissue-Engineered Blood Vessels

Atherosclerosis begins with the accumulation of cholesterol-carrying lipoproteins on blood vessel walls and progresses to endothelial cell dysfunction, monocyte adhesion, and foam cell formation. Endothelialized tissue-engineered blood vessels (TEBVs) have previously been fabricated to recapitulate artery functionalities, including vasoconstriction, vasodilation, and endothelium activation. Here, the initiation of atherosclerosis is emulated by designing branched TEBVs (brTEBVs) of various geometries treated with enzyme-modified low-density-lipoprotein (eLDL) and TNF-α to induce endothelial cell dysfunction and adhesion of perfused human monocytes. Locations of monocyte adhesion under pulsatile flow are identified, and the hemodynamics in the brTEBVs are characterized using particle image velocimetry (PIV) and computational fluid dynamics (CFD). Monocyte adhesion is greater at the side outlets than at the main outlets or inlets, and is greatest at larger side outlet branching angles (60° or 80° vs 45°). In PIV experiments, the branched side outlets are identified as atherosclerosis-prone areas where fluorescent particles show a transient swirling motion following flow pulses; in CFD simulations, side outlets with larger branching angles show higher vorticity magnitude and greater flow disturbance than other areas. These results suggest that the branched TEBVs with eLDL/TNF-α treatment provide a physiologically relevant model of early atherosclerosis for preclinical studies.

Prediction of the development of coronary atherosclerotic plaques using computational modeling in 3D reconstructed coronary arteries

In this work we present a novel method for the prediction and generation of atherosclerotic plaques. This is performed in a two-step approach, by employing first a multilevel computational plaque growth model and second a correlation between the model's results and the 3D reconstructed follow-up plaques. In particular, computer tomography coronary angiography (CTCA) data and blood tests were collected from patients at two time points. Using the baseline data, the plaque growth is simulated using a multi-level computational model which includes: i) modeling of the blood flow dynamics, ii) modeling of low and high density lipoproteins and monocytes' infiltration in the arterial wall, and the species reactions during the atherosclerotic process, and iii) modeling of the arterial wall thickening. The correlation between the followup plaques and the simulated plaque density distribution resulted to the extraction of a threshold of the plaque density, that can be used to identify plaque areas.Clinical Relevance- The methodology presented in this work is a first step to the prediction of the plaque shape and location of patients with atherosclerosis and could be used as an additional tool for patient-specific risk stratification.

Prediction of atherosclerotic disease progression combining computational modelling with machine learning

Non-invasive serial computed tomography coronary angiography (CTCA) was acquired from 32 patients and 3D reconstruction of 58 coronary arteries was achieved. The arterial geometries were utilized for blood flow and LDL transport modelling. Navier-Stokes and convection-diffusion equations were employed for simulation of blood flow and LDL transport, respectively. Disease progression was assessed comparing the follow-up and baseline arterial models after co-registration using side branches as anatomical landmarks. A machine learning model for predicting disease progression was built using the Gradient Boosted Trees (GBT) algorithm. The Accuracy, Sensitivity, Specificity and AUC of the developed methodology for predicting lumen area decrease equal was 0.68, 0.56, 0.34 and 0.59, respectively. The best results were found for the prediction of plaque area increase by 20%, with 0.73, 0.67, 0.86, and 0.76 accuracy, sensitivity, specificity andAUC, respectively. This approach outperforms significantly the predictive capability of models based on binary logistic regression.

Predictive Models of Coronary Artery Disease Based on Computational Modeling: The SMARTool System

SMARTool aims to the development of Decision Support Systems (DSS) for the risk stratification, diagnosis, prediction and treatment of coronary artery disease (CAD). In this work, we present the results of the prediction DSS, which utilizes clinical data, imaging morphological characteristics and computational modeling results. More specifically, 263 patients were recruited in the SMARTool clinical trial and 196 patients were selected for the DSS development. Traditional risk factors, blood examinations and computed coronary tomography angiography (CCTA) were performed at two different time points with an interscan period 6.22 ± 1.42 years. Computational modeling of blood flow and LDL transport was performed at the baseline. Predictive models are built for the prediction of CAD at the follow-up. The results show that CAD can be predicted with 83% accuracy, when low ESS, high accumulation of LDL and imaging data are included in the model.

A new method for the 3D reconstruction of coronary bifurcations pre and post the angioplasty procedure using the QCA

The aim of this study is to propose a new semi-automated method for three-dimensional (3D) reconstruction of coronary bifurcations arteries using X-ray Coronary Angiographies (CA). Considering two monoplane angiographic views as the input data, the method is based on a four-step approach. In the first step, the image pre-processing and the vessel segmentation is performed. In the second step the 3D centerline is reconstructed by implementing the back-projection algorithm. In the third step, the lumen borders are reconstructed around the centerline to result to the fourth step, during which the 3D point cloud of the side branch is adjusted to the main branch, to produce the final 3D model of the coronary bifurcation artery. Imaging data from seven patients (pre and post-stenting) were reconstructed in the 3D space. The validation of the proposed methodology was based on the comparison of the 3D model with the 2D CA. Blood flow simulations were performed both for the vessels before and after the angioplasty procedure. Decreased Endothelial Shear Stress (ESS) values were observed for the vessels after the Percutaneous Transluminal Coronary Intervention (PTCI).

Shear Stress Estimated by Quantitative Coronary Angiography Predicts Plaques Prone to Progress and Cause Events

OBJECTIVES

This study examined the value of endothelial shear stress (ESS) estimated in 3-dimensional quantitative coronary angiography (3D-QCA) models in detecting plaques that are likely to progress and cause events.

BACKGROUND

Cumulative evidence has shown that plaque characteristics and ESS derived from intravascular ultrasound (IVUS)-based reconstructions enable prediction of lesions that will cause cardiovascular events. However, the prognostic value of ESS estimated by 3D-QCA in nonflow limiting lesions is yet unclear.

METHODS

This study analyzed baseline virtual histology (VH)-IVUS and angiographic data from 28 lipid-rich lesions (i.e., fibroatheromas) that caused major adverse cardiovascular events or required revascularization (MACE-R) at 5-year follow-up and 119 lipid-rich plaques from a control group that remained quiescent. The segments studied by VH-IVUS at baseline were reconstructed using 3D-QCA software. In the obtained geometries, blood flow simulation was performed, and the pressure gradient across the lipid-rich plaque and the mean ESS values in 3-mm segments were estimated. The additive value of these hemodynamic indexes in predicting MACE-R beyond plaque characteristics was examined.

RESULTS

MACE-R lesions were longer, had smaller minimum lumen area, increased plaque burden (PB), were exposed to higher ESS, and exhibited a higher pressure gradient. In multivariable analysis, PB (hazard ratio: 1.08; p = 0.004) and the maximum 3-mm ESS value (hazard ratio: 1.11; p = 0.001) were independent predictors of MACE-R. Lesions exposed to high ESS (>4.95 Pa) with a high-risk anatomy (minimal lumen area <4 mm² and PB >70%) had a higher MACE-R rate (53.8%) than those with a low-risk anatomy exposed to high ESS (31.6%) or those exposed to low ESS who had high- (20.0%) or low-risk anatomy (7.1%; p < 0.001).

CONCLUSIONS

In the present study, 3D-QCA-derived local hemodynamic variables provided useful prognostic information, and, in combination with lesion anatomy, enabled more accurate identification of MACE-R lesions.

A computational multi-level atherosclerotic plaque growth model for coronary arteries

In this work, we present a novel computational approach for the prediction of atherosclerotic plaque growth. In particular, patient-specific coronary computed tomography angiography (CCTA) data were collected from 60 patients at two time points. Additionally, blood samples were collected for biochemical analysis. The CCTA data were used for 3D reconstruction of the coronary arteries, which were then used for computational modeling of plaque growth. The model of plaque growth is based on a multi-level approach: i) the blood flow is modeled in the lumen and the arterial wall, ii) the low and high density lipoprotein and monocytes transport is included, and iii) the major atherosclerotic processes are modeled including the foam cells formation, the proliferation of smooth muscle cells and the formation of atherosclerotic plaque. Validation of the model was performed using the follow-up CCTA. The results show a correlation of the simulated follow-up arterial wall area to be correlated with the corresponding realistic follow-up with r²=0.49, P<; 0.0001.

Evaluation of the Efficacy of Computed Tomographic Coronary Angiography in Assessing Coronary Artery Morphology and Physiology: Rationale and Study Design

Computed tomographic coronary angiography (CTCA) is a non-invasive imaging modality, which allows plaque burden and composition assessment and detection of plaque characteristics associated with increased vulnerability. In addition, CTCA-based coronary artery reconstruction enables local haemodynamic forces assessment, which regulate plaque formation and vascular inflammation and prediction of lesions that are prone to progress and cause events. However, the use of CTCA for vulnerable plaque detection in the clinical arena remains limited. To unlock the full potential of CTCA and enable its broad use, further work is needed to develop user-friendly processing tools that will allow fast and accurate analysis of CTCA, computational fluid dynamic modelling, and evaluation of the local haemodynamic forces. The present study aims to develop a seamless platform that will overcome the limitations of CTCA and enable fast and accurate evaluation of plaque morphology and physiology. We will analyse imaging data from 70 patients with coronary artery disease who will undergo state-of-the-art CTCA and near-infrared spectroscopy-intravascular ultrasound imaging and develop and train algorithms that will take advantage of the intravascular imaging data to optimise vessel segmentation and plaque characterisation. Furthermore, we will design an advanced module that will enable reconstruction of coronary artery anatomy from CTCA, blood flow simulation, shear stress estimation, and comprehensive visualisation of vessel pathophysiology. These advances are expected to facilitate the broad use of CTCA, not only for risk stratification but also for the evaluation of the effect of emerging therapies on plaque evolution.

Angiographic derived endothelial shear stress: a new predictor of atherosclerotic disease progression

AIMS

To examine the efficacy of angiography derived endothelial shear stress (ESS) in predicting atherosclerotic disease progression.

METHODS AND RESULTS

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).

CONCLUSIONS

3D QCA-derived ESS can predict disease progression. Further research is required to examine its value in detecting vulnerable plaques.