Total coronary atherosclerotic plaque burden is associated with myocardial ischemia in non-obstructive coronary artery disease

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.

Comparison of Coronary Artery Calcium and Quantitative Coronary Plaque in Predicting Obstructive Coronary Artery Disease: Subgroup Analysis of the CLARIFY Study

Background: Agatston coronary artery calcium (CAC) score is a strong predictor of mortality. However, the relationship between CAC and quantitative calcified plaque volume (CPV), which is measured on coronary computed tomography angiography (CCTA), is not well understood. Furthermore, there is limited evidence evaluating the difference between CAC versus CPV and CAC versus total plaque volume (TPV) in predicting obstructive coronary artery disease (CAD). Methods: This study included 147 subjects from the CLARIFY registry, a multicentered study of patients undergoing assessment using CCTA and CAC score as part of acute and stable chest pain evaluation. Automated software service (Cleerly.Inc, Denver, CO, USA) was used to evaluate the degree of vessel stenosis and plaque quantification on CCTA. CAC was measured using the standard Agatston method. Spearman correlation and receiver operating characteristic curve analysis was performed to evaluate the diagnostic ability of CAC, CPV and TPV in detecting obstructive CAD. Results: Results demonstrated a very strong positive correlation between CAC and CPV (r=0.76, p=0.0001) and strong correlation between CAC and TPV (r=0.72, p<0.001) at per-patient level analysis. At per-patient level analysis, the sensitivity of CAC (68%) is lower than CPV (77%) in predicting >50% stenosis, but negative predictive value is comparable. However, the sensitivity of TPV is higher compared with CAC in predicting >50% stenosis, and the negative predictive value of TPV is also higher. Conclusion: CPV and TPV are more sensitive in predicting the severity of obstructive CAD compared with the CAC score. However, the negative predictive value of CAC is comparable to CPV, but is lower than TPV. This study elucidates the relationship between CAC and quantitative plaque types, and especially emphasizes the differences between CAC and CPV which are two distinct plaque measurement techniques that are utilized in predicting obstructive CAD.

Association between coronary plaque volume and myocardial ischemia detected by dynamic perfusion CT imaging

Introduction

We aimed to evaluate the relationship between quantitative plaque metrics derived from coronary CT angiography (CTA) and segmental myocardial ischemia using dynamic perfusion CT (DPCT).

Methods

In a prospective single-center study, patients with > 30% stenosis on rest CTA underwent regadenoson stress DPCT. 480 myocardium segments of 30 patients were analyzed. Quantitative plaque assessment included total plaque volume (PV), area stenosis, and remodeling index (RI). High-risk plaque (HRP) was defined as low-attenuation plaque burden > 4% or RI > 1.1. Absolute myocardial blood flow (MBF) and relative MBF (MBFi: MBF/75th percentile of all MBF values) were quantified. Linear and logistic mixed models correcting for intra-patient clustering and clinical factors were used to evaluate the association between total PV, area stenosis, HRP and MBF or myocardial ischemia (MBF < 101 ml/100 g/min).

Results

Median MBF and MBFi were 111 ml/100 g/min and 0.94, respectively. The number of ischemic segments were 164/480 (34.2%). Total PV of all feeding vessels of a given myocardial territory differed significantly between ischemic and non-ischemic myocardial segments (p = 0.001). Area stenosis and HRP features were not linked to MBF or MBFi (all p > 0.05). Increase in PV led to reduced MBF and MBFi after adjusting for risk factors including hypertension, diabetes, and statin use (per 10 mm³; β = −0.035, p < 0.01 for MBF; β = −0.0002, p < 0.01 for MBFi). Similarly, using multivariate logistic regression total PV was associated with ischemia (OR = 1.01, p = 0.033; per 10 mm³) after adjustments for clinical risk factors, area stenosis and HRP.

Conclusion

Total PV was independently associated with myocardial ischemia based on MBF, while area stenosis and HRP were not.