Very late vasomotor responses and gene expression with bioresorbable scaffolds and metallic drug-eluting stents.

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 (LTßR) 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.

Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid-structure interaction models and machine learning methods with patient follow-up data: a feasibility study.

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.

Optical Coherence Tomography-Based Patient-Specific Residual Multi-Thrombus Coronary Plaque Models With Fluid-Structure Interaction for Better Treatment Decisions: A Biomechanical Modeling Case Study.

Intracoronary thrombus from plaque erosion could cause fatal acute coronary syndrome (ACS). A conservative antithrombotic therapy has been proposed to treat ACS patients in lieu of stenting. It is speculated that the residual thrombus after aspiration thrombectomy would influence the prognosis of this treatment. However, biomechanical mechanisms affecting intracoronary thrombus remodeling and clinical outcome remain largely unknown. in vivo optical coherence tomography (OCT) data of a coronary plaque with two residual thrombi after antithrombotic therapy were acquired from an ACS patient with consent obtained. Three OCT-based fluid-structure interaction (FSI) models with different thrombus volumes, fluid-only, and structure-only models were constructed to simulate and compare the biomechanical interplay among blood flow, residual thrombus, and vessel wall mimicking different clinical situations. Our results showed that residual thrombus would decrease coronary volumetric flow rate by 9.3%, but elevate wall shear stress (WSS) by 29.4% and 75.5% at thrombi 1 and 2, respectively. WSS variations in a cardiac cycle from structure-only model were 12.1% and 13.5% higher at the two thrombus surfaces than those from FSI model. Intracoronary thrombi were subjected to compressive forces indicated by negative thrombus stress. Tandem intracoronary thrombus might influence coronary hemodynamics and solid mechanics differently. Computational modeling could be used to quantify biomechanical conditions under which patients could receive patient-specific treatment plan with optimized outcome after antithrombotic therapy. More patient studies with follow-up data are needed to continue the investigation and better understand mechanisms governing thrombus remodeling process.

Using optical coherence tomography and intravascular ultrasound imaging to quantify coronary plaque cap thickness and vulnerability: a pilot study.

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.

A Multimodality Image-Based Fluid-Structure Interaction Modeling Approach for Prediction of Coronary Plaque Progression Using IVUS and Optical Coherence Tomography Data With Follow-Up.

Medical image resolution has been a serious limitation in plaque progression research. A modeling approach combining intravascular ultrasound (IVUS) and optical coherence tomography (OCT) was introduced and patient follow-up IVUS and OCT data were acquired to construct three-dimensional (3D) coronary models for plaque progression investigations. Baseline and follow-up in vivo IVUS and OCT coronary plaque data were acquired from one patient with 105 matched slices selected for model construction. 3D fluid-structure interaction (FSI) models based on IVUS and OCT data (denoted as IVUS + OCT model) were constructed to obtain stress/strain and wall shear stress (WSS) for plaque progression prediction. IVUS-based IVUS50 and IVUS200 models were constructed for comparison with cap thickness set as 50 and 200 µm, respectively. Lumen area increase (LAI), plaque area increase (PAI), and plaque burden increase (PBI) were chosen to measure plaque progression. The least squares support vector machine (LS-SVM) method was employed for plaque progression prediction using 19 risk factors. For IVUS + OCT model with LAI, PAI, and PBI, the best single predictor was plaque strain, local plaque stress, and minimal cap thickness, with prediction accuracy as 0.766, 0.838, and 0.890, respectively; the prediction accuracy using best combinations of 19 factors was 0.911, 0.881, and 0.905, respectively. Compared to IVUS + OCT model, IVUS50, and IVUS200 models had errors ranging from 1% to 66.5% in quantifying cap thickness, stress, strain and prediction accuracies. WSS showed relatively lower prediction accuracy compared to other predictors in all nine prediction studies.

Combining IVUS and Optical Coherence Tomography for More Accurate Coronary Cap Thickness Quantification and Stress/Strain Calculations: A Patient-Specific Three-Dimensional Fluid-Structure Interaction Modeling Approach.

Accurate cap thickness and stress/strain quantifications are of fundamental importance for vulnerable plaque research. Virtual histology intravascular ultrasound (VH-IVUS) sets cap thickness to zero when cap is under resolution limit and IVUS does not see it. An innovative modeling approach combining IVUS and optical coherence tomography (OCT) is introduced for cap thickness quantification and more accurate cap stress/strain calculations. In vivo IVUS and OCT coronary plaque data were acquired with informed consent obtained. IVUS and OCT images were merged to form the IVUS + OCT data set, with biplane angiography providing three-dimensional (3D) vessel curvature. For components where VH-IVUS set zero cap thickness (i.e., no cap), a cap was added with minimum cap thickness set as 50 and 180 µm to generate IVUS50 and IVUS180 data sets for model construction, respectively. 3D fluid-structure interaction (FSI) models based on IVUS + OCT, IVUS50, and IVUS180 data sets were constructed to investigate cap thickness impact on stress/strain calculations. Compared to IVUS + OCT, IVUS50 underestimated mean cap thickness (27 slices) by 34.5%, overestimated mean cap stress by 45.8%, (96.4 versus 66.1 kPa). IVUS50 maximum cap stress was 59.2% higher than that from IVUS + OCT model (564.2 versus 354.5 kPa). Differences between IVUS and IVUS + OCT models for cap strain and flow shear stress (FSS) were modest (cap strain <12%; FSS <6%). IVUS + OCT data and models could provide more accurate cap thickness and stress/strain calculations which will serve as basis for further plaque investigations.

An assessment of intra-patient variability on observed relationships between wall shear stress and plaque progression in coronary arteries.

BACKGROUND: Wall shear stress (WSS) has been associated with sites of plaque localization and with changes in plaque composition in human coronary arteries. Different values have been suggested for categorizing WSS as low, physiologic or high; however, uncertainties in flow rates, both across subjects and within a given individual, can affect the classification of WSS and thus influence the observed relationships between local hemodynamics and plaque changes over time. This study examines the effects of uncertainties in flow rate boundary conditions upon WSS values and investigates the influence of this variability on the observed associations of WSS with changes in VH-IVUS derived plaque components. METHODS: Three patients with coronary artery disease underwent baseline and 12 month follow-up angiography and virtual histology-intravascular ultrasound (VH-IVUS) measurements. Coronary artery models were reconstructed from the data and models with and without side-branches were created. Patient-specific Doppler ultrasound (DUS) data were employed as inflow boundary conditions and computational fluid dynamics was used to calculate the WSS in each model. Further, the influence of representative coronary artery flow waveforms upon WSS values was investigated and the concept of treating WSS using relative, rather than actual, values was explored. RESULTS: Models that included side-branch outflows and subject-specific DUS velocities were considered to be the reference cases. Hemodynamic differences were caused by the exclusion of side-branches and by imposing alternative velocity waveforms. One patient with fewer side-branches and a scaled generic waveform had little deviation from the reference case, while another patient with several side-branches excluded showed much larger departures from the reference situation. Differences between models and the respective reference cases were reduced when data were analyzed using relative, rather than actual, WSS. CONCLUSIONS: When considering individual subjects, large variations in patient-specific flow rates and exclusion of multiple side-branches in computational models can cause significant differences in observed associations between plaque evolution and ranges of computed WSS. These differences may contribute to the large variability typically found among subjects in pooled populations. Relative WSS may be more useful than actual WSS as a correlative variable when there is a large degree of uncertainty in flow rate data.

Mass transport of low density lipoprotein in reconstructed hemodynamic environments of human carotid arteries: the role of volume and solute flux through the endothelium.

The accumulation of low density lipoprotein (LDL) in the arterial intima is a critical step in the initiation and progression of atheromatous lesions. In this study we examine subject-specific LDL transport into the intima of carotid bifurcations in three human subjects using a three-pore model for LDL mass transfer. Subject-specific carotid artery computational models were derived using magnetic resonance imaging (MRI) to obtain the geometry and phase-contract MRI (PC-MRI) to acquire pulsatile inflow and outflow boundary conditions for each subject. The subjects were selected to represent a wide range of anatomical configurations and different stages of atherosclerotic development from mild to moderate intimal thickening. A fluid-solid interaction (FSI) model was implemented in the computational fluid dynamics (CFD) approach in order to consider the effects of a compliant vessel on wall shear stress (WSS). The WSS-dependent response of the endothelium to LDL mass transfer was modeled by multiple pathways to include the contributions of leaky junctions, normal junctions, and transcytosis to LDL solute and plasma volume flux from the lumen into the intima. Time averaged WSS (TAWSS) over the cardiac cycle was computed to represent the spatial WSS distribution, and wall thickness (WTH) was determined from black blood MRI (BBMRI) so as to visualize intimal thickening patterns in the bifurcations. The regions which are exposed to low TAWSS correspond to elevated WTH and higher mass and volume flux via the leaky junctions. In all subjects, the maximum LDL solute flux was observed to be immediately downstream of the stenosis, supporting observations that existing atherosclerotic lesions tend to progress in the downstream direction of the stenosis.

Magnetic targeting of human mesenchymal stem cells with internalized superparamagnetic iron oxide nanoparticles.

Cell therapies offer exciting new opportunities for effectively treating many human diseases. However, delivery of therapeutic cells by intravenous injection, while convenient, relies on the relatively inefficient process of homing of cells to sites of injury. To address this limitation, a novel strategy has been developed to load cells with superparamagnetic iron oxide nanoparticles (SPIOs), and to attract them to specific sites within the body by applying an external magnetic field. The feasibility of this approach is demonstrated using human mesenchymal stem cells (hMSCs), which may have a significant potential for regenerative cell therapies due to their ease of isolation from autologous tissues, and their ability to differentiate into various lineages and modulate their paracrine activity in response to the microenvironment. The efficient loading of hMSCs with polyethylene glycol-coated SPIOs is achieved, and it is found that SPIOs are localized primarily in secondary lysosomes of hMSCs and are not toxic to the cells. Further, the key stem cell characteristics, including the immunophenotype of hMSCs and their ability to differentiate, are not altered by SPIO loading. Through both experimentation and mathematical modeling, it is shown that, under applied magnetic field gradients, SPIO-containing cells can be localized both in vitro and in vivo. The results suggest that, by loading SPIOs into hMSCs and applying appropriate magnetic field gradients, it is possible to target hMSCs to particular vascular networks.

Combining IVUS + OCT Data, Biomechanical Models and Machine Learning Method for Accurate Coronary Plaque Morphology Quantification and Cap Thickness and Stress/Strain Index Predictions.

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.

Coronary artery wall shear stress is associated with progression and transformation of atherosclerotic plaque and arterial remodeling in patients with coronary artery disease.

BACKGROUND: Experimental studies suggest that low wall shear stress (WSS) promotes plaque development and high WSS is associated with plaque destabilization. We hypothesized that low-WSS segments in patients with coronary artery disease develop plaque progression and high-WSS segments develop necrotic core progression with fibrous tissue regression. METHODS AND RESULTS: Twenty patients with coronary artery disease underwent baseline and 6-month radiofrequency intravascular ultrasound (virtual histology intravascular ultrasound) and computational fluid dynamics modeling for WSS calculation. For each virtual histology intravascular ultrasound segment (n=2249), changes in plaque area, virtual histology intravascular ultrasound-derived plaque composition, and remodeling were compared in low-, intermediate-, and high-WSS categories. Compared with intermediate-WSS segments, low-WSS segments developed progression of plaque area (P=0.027) and necrotic core (P<0.001), whereas high-WSS segments had progression of necrotic core (P<0.001) and dense calcium (P<0.001) and regression of fibrous (P<0.001) and fibrofatty (P<0.001) tissue. Compared with intermediate-WSS segments, low-WSS segments demonstrated greater reduction in vessel (P<0.001) and lumen area (P<0.001), and high-WSS segments demonstrated an increase in vessel (P<0.001) and lumen (P<0.001) area. These changes resulted in a trend toward more constrictive remodeling in low- compared with high-WSS segments (73% versus 30%; P=0.06) and more excessive expansive remodeling in high- compared with low-WSS segments (42% versus 15%; P=0.16). CONCLUSIONS: Compared with intermediate-WSS coronary segments, low-WSS segments develop greater plaque and necrotic core progression and constrictive remodeling, and high-WSS segments develop greater necrotic core and calcium progression, regression of fibrous and fibrofatty tissue, and excessive expansive remodeling, suggestive of transformation to a more vulnerable phenotype. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT00576576.

Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach.

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. Conclusion: Fatigue correlated positively with stenosis progression. Factors associated with fatigue could contribute to better prediction for atherosclerosis progression.

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.

Localization of culprit lesions in coronary arteries of patients with ST-segment elevation myocardial infarctions: relation to bifurcations and curvatures.

BACKGROUND: Although culprit lesions in ST-segment elevation myocardial infarction (STEMI) cluster in the proximal coronary arteries, their relationship to bifurcations and curvatures, where blood flow is disturbed, is unknown. We hypothesized that (a) culprit lesions localize to disturbed flow distal to bifurcations and curvatures and (b) the distribution of culprit lesions in the left (LCA) and right coronary arteries (RCA) and resulting infarct size are related to the location of bifurcations and curvatures. METHODS: Emory University's contribution to the National Cardiovascular Data Registry was queried for STEMIs. Using quantitative coronary angiography, the distances from the vessel ostium, major bifurcations, and major curvatures to the culprit lesion were measured in 385 patients. RESULTS: Culprit lesions were located within 20 mm of a bifurcation in 79% of patients and closer to the bifurcation in the LCA compared with the RCA (7.4 ± 7.3 vs 17.7 ± 14.8 mm, P < .0001). Of RCA culprit lesions, 45% were located within 20 mm of a major curvature. Compared with those in the RCA, culprit lesions in the LCA were located more proximally (24.4 ± 16.5 vs 44.7 ± 28.8 mm, P = .0003) and were associated with larger myocardial infarctions as assessed by peak creatine kinase-MB (208 ± 222 vs 140 ± 153 ng/dL, P = .001) and troponin I (59 ± 62 vs 40 ± 35 ng/dL, P = .0006) and with higher in-hospital mortality (5.2% vs 1.1%, P = .04). CONCLUSIONS: In patients with STEMI, culprit lesions are frequently located immediately distal to bifurcations and in proximity to major curvatures where disturbed flow is known to occur. This supports the role of wall shear stress in the pathogenesis of STEMI.

An in vivo murine model of low-magnitude oscillatory wall shear stress to address the molecular mechanisms of mechanotransduction--brief report.

OBJECTIVE: Current understanding of shear-sensitive signaling pathways has primarily been studied in vitro largely because of 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 by low-magnitude oscillatory wall shear stress (WSS). METHODS AND RESULTS: Our model uses 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. Vascular cell adhesion molecule-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.

Multi-patient study for coronary vulnerable plaque model comparisons: 2D/3D and fluid-structure interaction simulations.

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.

Using Optical Coherence Tomography and Intravascular Ultrasound Imaging to Quantify Coronary Plaque Cap Stress/Strain and Progression: A Follow-Up Study Using 3D Thin-Layer Models.

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.

In vivo assessment of blood flow patterns in abdominal aorta of mice with MRI: implications for AAA localization.

Abdominal aortic aneurysms (AAA) localize in the infrarenal aorta in humans, while they are found in the suprarenal aorta in mouse models. It has been shown previously that humans experience a reversal of flow during early diastole in the infrarenal aorta during each cardiac cycle. This flow reversal causes oscillatory wall shear stress (OWSS) to be present in the infrarenal aorta of humans. OWSS has been linked to a variety of proatherogenic and proinflammatory factors. The presence of reverse flow in the mouse aorta is unknown. In this study we investigated blood flow in mice, using phase-contrast magnetic resonance (PCMR) imaging. We measured blood flow in the suprarenal and infrarenal abdominal aorta of 18 wild-type C57BL/6J mice and 15 apolipoprotein E (apoE)-/- mice. Although OWSS was not directly evaluated, results indicate that, unlike humans, there is no reversal of flow in the infrarenal aorta of wild-type or apoE-/- mice. Distensibility of the mouse aortic wall in both the suprarenal and infrarenal segments is higher than reported values for the human aorta. We conclude that normal mice do not experience the reverse flow in the infrarenal aorta that is observed in humans.

Multi-factor decision-making strategy for better coronary plaque burden increase prediction: a patient-specific 3D FSI study using IVUS follow-up data.

Plaque progression and vulnerability are influenced by many risk factors. Our goal is to find a simple method to combine multiple risk factors for better plaque development prediction. Intravascular ultrasound data at baseline and follow-up were acquired from nine patients, and fluid-structure interaction models were constructed to obtain plaque wall stress/strain (PWS/PWSn) and wall shear stress (WSS). Two hundred fifty-four slices with noticeable change in plaque burden were selected for analyses. Data of six key morphological and biomechanical factors were extracted from each slice at baseline to predict plaque development measured by plaque burden increase (PBI) from baseline to follow-up. A multi-factor decision-making strategy was proposed to assign a binary predictive outcome YW (W represents any combination of these six factors) based on simple "threshold value" idea to predict the ground truth YPBI: YPBI = 1 if PBI > 0; YPBI = 0 otherwise. A fivefold cross-validation procedure was employed to identify the optimal predictor among all possible combinations. The results showed that PWS was the best single-factor predictor for PBI with a prediction accuracy of 63.0%. Among all 63 combinations, combining lipid percent, PWS and WSS gave the optimal predictor, achieving a prediction accuracy of 68.1%. This demonstrated that compared to single factor alone, integrating morphological and biomechanical factors would lead to higher prediction accuracy. The simple method could be extended to combine factors from different sources to improve prediction accuracy. Efforts in mechanical analysis and modeling automation are needed to bring this strategy closer to potential clinical applications.