BACKGROUND: Extremes of wall shear stress (WSS) have been associated with plaque progression and transformation, which has raised interest in the clinical assessment of WSS. We hypothesized that calculated coronary WSS is predicted only partially by luminal geometry and that WSS is related to plaque composition. METHODS AND RESULTS: Twenty-seven patients with coronary artery disease underwent virtual histology intravascular ultrasound and Doppler velocity measurement for computational fluid dynamics modeling for WSS calculation in each virtual histology intravascular ultrasound segment (N=3581 segments). We assessed the association of WSS with plaque burden and distribution and with plaque composition. WSS remained relatively constant across the lower 3 quartiles of plaque burden (P=0.08) but increased in the highest quartile of plaque burden (P<0.001). Segments distal to lesions or within bifurcations were more likely to have low WSS (P<0.001). However, the majority of segments distal to lesions (80%) and within bifurcations (89%) did not exhibit low WSS. After adjustment for plaque burden, there was a negative association between WSS and percent necrotic core and calcium. For every 10 dynes/cm(2) increase in WSS, percent necrotic core decreased by 17% (P=0.01), and percent dense calcium decreased by 17% (P<0.001). There was no significant association between WSS and percent of fibrous or fibrofatty plaque components (P=NS). CONCLUSIONS: IN PATIENTS WITH CORONARY ARTERY DISEASE: (1) Luminal geometry predicts calculated WSS only partially, which suggests that detailed computational techniques must be used to calculate WSS. (2) Low WSS is associated with plaque necrotic core and calcium, independent of plaque burden, which suggests a link between WSS and coronary plaque phenotype. (J Am Heart Assoc. 2012;1:e002543 doi: 10.1161/JAHA.112.002543.).

Objective Current understanding of shear sensitive signaling pathways has primarily been studied in vitro largely due to a lack of adequate in vivo models. Our objective was to develop a simple and well characterized murine aortic coarctation model to acutely alter the hemodynamic environment in vivo and test the hypothesis that endothelial inflammatory protein expression is acutely upregulated in vivo in by low magnitude oscillatory WSS. Methods and Results Our model utilizes the shape memory response of nitinol clips to reproducibly induce an aortic coarctation and allow subsequent focal control over WSS in the aorta. We modeled the corresponding hemodynamic environment using computational fluid dynamics and showed that the coarctation produces low magnitude oscillatory WSS distal to the clip. To assess the biological significance of this model, we correlated WSS to inflammatory protein expression and fatty streak formation. VCAM-1 expression and fatty streak formation were both found to increase significantly in regions corresponding to acutely induced low magnitude oscillatory WSS. Conclusions We have developed a novel aortic coarctation model that will be a useful tool for analyzing the in vivo molecular mechanisms of mechanotransduction in various murine models.

Objectives: To investigate the long-term vasomotor response and inflammatory changes in Absorb bioresorbable vascular scaffold (BVS) and metallic drug-eluting stent (DES) implanted artery. Background: Clinical evidence has demonstrated that compared to DES, BVS is associated with higher rates of target lesion failure. However, it is not known whether the higher event rates observed with BVS are related to endothelial dysfunction or inflammation associated with polymer degradation. Methods: Ten Absorb BVS and six Xience V DES were randomly implanted in the main coronaries of six nonatherosclerotic swine. At 4-years, vasomotor response was evaluated in vivo by quantitative coronary angiography response to intracoronary infusion of Ach and ex vivo by the biomechanical response to prostaglandin F2-α (PGF2-α), substance P and bradykinin and gene expression analysis. Results: Absorb BVS implanted arteries showed significantly restored vasoconstrictive responses after Ach compared to in-stent Xience V. The contractility of Absorb BVS treated segments induced by PGF2-α was significantly greater compared to Xience V treated segments and endothelial-dependent vasorelaxation was greater with Absorb BVS compared to Xience V. Gene expression analyses indicated the pro-inflammatory lymphotoxin-beta receptor (LTβR) signaling pathway was significantly upregulated in arteries treated with a metallic stent compared to Absorb BVS treated arterial segments. Conclusions: At 4 years, arteries treated with Absorb BVS compared with Xience V, demonstrate significantly greater restoration of vasomotor responses. Genetic analysis suggests mechanobiologic reparation of Absorb BVS treated arteries at 4 years as opposed to Xience V treated vessels.

Accurate plaque cap thickness quantification and cap stress/strain calculations are of fundamental importance for vulnerable plaque research. To overcome uncertainties due to intravascular ultrasound (IVUS) resolution limitation, IVUS and optical coherence tomography (OCT) coronary plaque image data were combined together to obtain accurate and reliable cap thickness data, stress/strain calculations, and reliable plaque progression predictions. IVUS, OCT, and angiography baseline and follow-up data were collected from nine patients (mean age: 69; m: 5) at Cardiovascular Research Foundation with informed consent obtained. IVUS and OCT slices were coregistered and merged to form IVUS + OCT (IO) slices. A total of 114 matched slices (IVUS and OCT, baseline and follow-up) were obtained, and 3D thin-layer models were constructed to obtain stress and strain values. A generalized linear mixed model (GLMM) and least squares support vector machine (LSSVM) method were used to predict cap thickness change using nine morphological and mechanical risk factors. Prediction accuracies by all combinations (511) of those predictors with both IVUS and IO data were compared to identify optimal predictor(s) with their best accuracies. For the nine patients, the average of minimum cap thickness from IVUS was 0.17 mm, which was 26.08% lower than that from IO data (average = 0.23 mm). Patient variations of the individual errors ranged from ‒58.11 to 20.37%. For maximum cap stress between IO and IVUS, patient variations of the individual errors ranged from ‒30.40 to 46.17%. Patient variations of the individual errors of maximum cap strain values ranged from ‒19.90 to 17.65%. For the GLMM method, the optimal combination predictor using IO data had AUC (area under the ROC curve) = 0.926 and highest accuracy = 90.8%, vs. AUC = 0.783 and accuracy = 74.6% using IVUS data. For the LSSVM method, the best combination predictor using IO data had AUC = 0.838 and accuracy = 75.7%, vs. AUC = 0.780 and accuracy = 69.6% using IVUS data. This preliminary study demonstrated improved plaque cap progression prediction accuracy using accurate cap thickness data from IO slices and the differences in cap thickness, stress/strain values, and prediction results between IVUS and IO data. Large-scale studies are needed to verify our findings.

Several image-based computational models have been used to perform mechanical analysis for atherosclerotic plaque progression and vulnerability investigations. However, differences of computational predictions from those models have not been quantified at multi-patient level. In vivo intravascular ultrasound (IVUS) coronary plaque data were acquired from seven patients. Seven 2D/3D models with/without circumferential shrink, cyclic bending and fluid–structure interactions (FSI) were constructed for the seven patients to perform model comparisons and quantify impact of 2D simplification, circumferential shrink, FSI and cyclic bending plaque wall stress/strain (PWS/PWSn) and flow shear stress (FSS) calculations. PWS/PWSn and FSS averages from seven patients (388 slices for 2D and 3D thin-layer models) were used for comparison. Compared to 2D models with shrink process, 2D models without shrink process overestimated PWS by 17.26%. PWS change at location with greatest curvature change from 3D FSI models with/without cyclic bending varied from 15.07% to 49.52% for the seven patients (average = 30.13%). Mean Max-FSS, Min-FSS and Ave-FSS from the flow-only models under maximum pressure condition were 4.02%, 11.29% and 5.45% higher than those from full FSI models with cycle bending, respectively. Mean PWS and PWSn differences between FSI and structure-only models were only 4.38% and 1.78%. Model differences had noticeable patient variations. FSI and flow-only model differences were greater for minimum FSS predictions, notable since low FSS is known to be related to plaque progression. Structure-only models could provide PWS/PWSn calculations as good approximations to FSI models for simplicity and time savings in calculation.

Assessment and prediction of vulnerable plaque progression and rupture risk are of utmost importance for diagnosis, management and treatment of cardiovascular diseases and possible prevention of acute cardiovascular events such as heart attack and stroke. However, accurate assessment of plaque vulnerability assessment and prediction of its future changes require accurate plaque cap thickness, tissue component and structure quantifications and mechanical stress/strain calculations. Multi-modality intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography image data with follow-up were acquired from ten patients to obtain accurate and reliable plaque morphology for model construction. Three-dimensional thin-slice finite element models were constructed for 228 matched IVUS + OCT slices to obtain plaque stress/strain data for analysis. Quantitative plaque cap thickness and stress/strain indices were introduced as substitute quantitative plaque vulnerability indices (PVIs) and a machine learning method (random forest) was employed to predict PVI changes with actual patient IVUS + OCT follow-up data as the gold standard. Our prediction results showed that optimal prediction accuracies for changes in cap-PVI (C-PVI), mean cap stress PVI (meanS-PVI) and mean cap strain PVI (meanSn-PVI) were 90.3% (AUC = 0.877), 85.6% (AUC = 0.867) and 83.3% (AUC = 0.809), respectively. The improvements in prediction accuracy by the best combination predictor over the best single predictor were 6.6% for C-PVI, 10.0% for mean S-PVI and 8.0% for mean Sn-PVI. Our results demonstrated the potential using multi-modality IVUS + OCT image to accurately and efficiently predict plaque cap thickness and stress/strain index changes. Combining mechanical and morphological predictors may lead to better prediction accuracies.

Introduction: Coronary stenosis due to atherosclerosis restricts blood flow. Stenosis progression would lead to increased clinical risk such as heart attack. Although many risk factors were found to contribute to atherosclerosis progression, factors associated with fatigue is underemphasized. Our goal is to investigate the relationship between fatigue and stenosis progression based on in vivo intravascular ultrasound (IVUS) images and finite element models. Methods: Baseline and follow-up in vivo IVUS and angiography data were acquired from seven patients using Institutional Review Board approved protocols with informed consent obtained. Three hundred and five paired slices at baseline and follow-up were matched and used for plaque modeling and analysis. IVUS-based thin-slice models were constructed to obtain the coronary biomechanics and stress/strain amplitudes (stress/strain variations in one cardiac cycle) were used as the measurement of fatigue. The change of lumen area (DLA) from baseline to follow-up were calculated to measure stenosis progression. Nineteen morphological and biomechanical factors were extracted from 305 slices at baseline. Correlation analyses of these factors with DLA were performed. Random forest (RF) method was used to fit morphological and biomechanical factors at baseline to predict stenosis progression during follow-up. Results: Significant correlations were found between stenosis progression and maximum stress amplitude, average stress amplitude and average strain amplitude (p < 0.05). After factors selection implemented by random forest (RF) method, eight morphological and biomechanical factors were selected for classification prediction of stenosis progression. Using eight factors including fatigue, the overall classification accuracy, sensitivity and specificity of stenosis progression prediction with RF method were 83.61%, 86.25% and 80.69%, respectively.

Background Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Patient follow-up intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography data were acquired to construct 3D fluid–structure interaction (FSI) coronary models and four machine-learning methods were compared to identify optimal method to predict future plaque vulnerability. Methods Baseline and 10-month follow-up in vivo IVUS and OCT coronary plaque data were acquired from two arteries of one patient using IRB approved protocols with informed consent obtained. IVUS and OCT-based FSI models were constructed to obtain plaque wall stress/strain and wall shear stress. Forty-five slices were selected as machine learning sample database for vulnerability prediction study. Thirteen key morphological factors from IVUS and OCT images and biomechanical factors from FSI model were extracted from 45 slices at baseline for analysis. Lipid percentage index (LPI), cap thickness index (CTI) and morphological plaque vulnerability index (MPVI) were quantified to measure plaque vulnerability. Four machine learning methods (least square support vector machine, discriminant analysis, random forest and ensemble learning) were employed to predict the changes of three indices using all combinations of 13 factors. A standard fivefold cross-validation procedure was used to evaluate prediction results. Results For LPI change prediction using support vector machine, wall thickness was the optimal single-factor predictor with area under curve (AUC) 0.883 and the AUC of optimal combinational-factor predictor achieved 0.963. For CTI change prediction using discriminant analysis, minimum cap thickness was the optimal single-factor predictor with AUC 0.818 while optimal combinational-factor predictor achieved an AUC 0.836. Using random forest for predicting MPVI change, minimum cap thickness was the optimal single-factor predictor with AUC 0.785 and the AUC of optimal combinational-factor predictor achieved 0.847. Conclusion This feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.

Background: Detecting coronary vulnerable plaques in vivo and assessing their vulnerability have been great challenges for clinicians and the research community. Intravascular ultrasound (IVUS) is commonly used in clinical practice for diagnosis and treatment decisions. However, due to IVUS limited resolution (about 150–200 µm), it is not sufficient to detect vulnerable plaques with a threshold cap thickness of 65 µm. Optical Coherence Tomography (OCT) has a resolution of 15–20 µm and can measure fibrous cap thickness more accurately. The aim of this study was to use OCT as the benchmark to obtain patient-specific coronary plaque cap thickness and evaluate the differences between OCT and IVUS fibrous cap quantifications. A cap index with integer values 0–4 was also introduced as a quantitative measure of plaque vulnerability to study plaque vulnerability. Methods: Data from 10 patients (mean age: 70.4; m: 6; f: 4) with coronary heart disease who underwent IVUS, OCT, and angiography were collected at Cardiovascular Research Foundation (CRF) using approved protocol with informed consent obtained. 348 slices with lipid core and fibrous caps were selected for study. Convolutional Neural Network (CNN)-based and expert-based data segmentation were performed using established methods previously published. Cap thickness data were extracted to quantify differences between IVUS and OCT measurements. Results: For the 348 slices analyzed, the mean value difference between OCT and IVUS cap thickness measurements was 1.83% (p = 0.031). However, mean value of point-to-point differences was 35.76%. Comparing minimum cap thickness for each plaque, the mean value of the 20 plaque IVUS-OCT differences was 44.46%, ranging from 2.36% to 91.15%. For cap index values assigned to the 348 slices, the disagreement between OCT and IVUS assignments was 25%. However, for the OCT cap index = 2 and 3 groups, the disagreement rates were 91% and 80%, respectively. Furthermore, the observation of cap index changes from baseline to follow-up indicated that IVUS results differed from OCT by 80%. Conclusions: These preliminary results demonstrated that there were significant differences between IVUS and OCT plaque cap thickness measurements. Large-scale patient studies are needed to confirm our findings.

We investigated whether local hemodynamics were associated with sites of plaque erosion and hypothesized that patients with plaque erosion have locally elevated WSS magnitude in regions where erosion has occurred. We generated 3D, patient-specific models of coronary arteries from biplane angiographic images in 3 human patients with plaque erosion diagnosed by optical coherence tomography. Using computational fluid dynamics, we simulated pulsatile blood flow and calculated both wall shear stress (WSS) and oscillatory shear index (OSI). We also investigated anatomic features of plaque erosion sites by examining branching and local curvature in X-ray angiograms of barium-perfused autopsy hearts. Neither high nor low magnitudes of mean WSS were associated with sites of plaque erosion. OSI and local curvature were also not associated with erosion. Anatomically, 8 of 13 hearts had a nearby bifurcation upstream of the site of plaque erosion. This study provides preliminary evidence that neither hemodynamics nor anatomy are predictors of plaque erosion, based upon a very unique dataset. Our sample sizes are small, but this dataset suggests that high magnitudes of WSS, one potential mechanism for inducing plaque erosion, are not necessary for erosion to occur.