Wall shear stress estimated by 3D-QCA can predict cardiovascular events in lesions with borderline negative fractional flow reserve

Computational fluid dynamics model utilizing proper orthogonal decomposition to assess coronary physiology and wall shear stress

BACKGROUND

Percutaneous coronary intervention (PCI) to alleviate symptoms and improve outcomes in patients with symptomatic coronary artery disease. However, conventional assessments like coronary angiography may not fully capture the hemodynamic significance of coronary lesions. This study explores the utility of Proper Orthogonal Decomposition (POD) in elucidating coronary flow dynamics pre- and post-stent placement.

OBJECTIVES

Through the utilization of POD modes, we aim to analyze the intricate geometries of individual patients, extracting dominant POD modes both pre- and post-PCI. By engaging these modes, our objective is to discern changes in velocity patterns and wall shear stress, offering insight into the physiological outcomes of stent interventions in coronary arteries.

METHODS

The POD method with QR-decomposition was employed to generate POD modes, decomposing the vector field of interest into spatial functions modulated by time coefficients. Patients with prior coronary artery bypass surgery, myocardial bridging, collateral arteries, or recent myocardial infarction within 48 h were excluded from the study.

RESULTS

Results demonstrated improved hemodynamic parameters post-PCI, with significant enhancements in coronary flow reserve and reduced wall shear stress. POD analysis revealed that the first five modes effectively characterized flow features, highlighting stenosis, stent deployment, and branch dynamics.

CONCLUSION

This exploratory study demonstrates POD's potential for real-time assessment of coronary lesion significance and post-intervention outcomes. Its efficiency in capturing key flow characteristics offers a promising tool for personalized decision-making in interventional cardiology, enhancing our understanding of coronary hemodynamics and optimizing treatment strategies.

Predicting vulnerable coronary arteries: A combined radiomics-biomechanics approach

BACKGROUND AND OBJECTIVE

Nowadays, vulnerable coronary plaque detection from coronary computed tomography angiography (CCTA) is suboptimal, although being crucial for preventing major adverse cardiac events. Moreover, despite the suggestion of various vulnerability biomarkers, encompassing image and biomechanical factors, accurate patient stratification remains elusive, and a comprehensive approach integrating multiple markers is lacking. To this aim, this study introduces an innovative approach for assessing vulnerable coronary arteries and patients by integrating radiomics and biomechanical markers through machine learning methods.

METHODS

The study included 40 patients (7 high-risk and 33 low-risk) who underwent both CCTA and coronary optical coherence tomography (OCT). The dataset comprised 49 arteries (with 167 plaques), 7 of which (with 28 plaques) identified as vulnerable by OCT. Following image preprocessing and segmentation, CCTA-based radiomic features were extracted and a finite element analysis was performed to compute the biomechanical features. A novel machine learning pipeline was implemented to stratify coronary arteries and patients. For each stratification task, three independent predictive models were developed: a radiomic, a biomechanical and a combined radiomic-biomechanical model. Both k-nearest neighbors (KNN) and decision tree (DT) classifiers were considered.

RESULTS

The best radiomic model (KNN) detected all 7 vulnerable arteries and patients and was associated with a balanced accuracy of 0.86 (sensitivity=1, specificity=0.71) for the artery model and of 0.83 (sensitivity=1, specificity=0.67) for the patient model. The best biomechanical model (DT) detected 6 over 7 vulnerable arteries and patients and remarkably increased the specificity, resulting in a balanced accuracy of 0.89 (sensitivity=0.86, specificity=0.93) for the artery model and of 0.88 (sensitivity=0.86, specificity=0.91) for the patient model. Notably, the combined approach optimized the performance, with an increase in the balance accuracy up to 0.94 for the artery model and up to 0.92 for the patient model, being associated with sensitivity=1 and high specificity (0.88 and 0.85 for artery and patient models, respectively).

CONCLUSION

This investigation highlights the promise of radio-mechanical coronary artery phenotyping for patient stratification. If confirmed from larger studies, our approach enables a more personalized management of the disease, with the early identification of high-risk individuals and the reduction of unnecessary interventions for low-risk individuals.

Can fast wall shear stress computation predict adverse cardiac events in patients with intermediate non-flow limiting stenoses?

BACKGROUND AND AIMS

Coronary angiography-derived wall shear stress (WSS) may enable identification of vulnerable plaques and patients. A new recently introduced software allows seamless three-dimensional quantitative coronary angiography (3D-QCA) reconstruction and WSS computation within a single user-friendly platform carrying promise for clinical applications. This study examines for the first time the efficacy of this software in detecting vulnerable lesions in patients with intermediate non-flow limiting stenoses.

METHODS

This multicentre retrospective study included patients who had coronary angiography showing at least one lesion with borderline negative fractional flow reserve (FFR: 0.81-0.85). In these lesions, 3D-QCA reconstruction and blood flow simulation were performed using the CAAS Workstation WSS prototype (Pie Medical Imaging, Maastricht, Netherlands). Time averaged and multidirectional WSS were extracted across the lesion at every 3 mm segments. The primary endpoint of the study was lesion-oriented clinical events (LOCE), defined as the composite of cardiac death, target lesion related myocardial infarction (MI) or clinically indicated target lesion revascularization.

RESULTS

352 patients (355 lesions) were included in the analysis. Over a median follow-up of 4.1 years, 57 LOCE were recorded. Lesions causing events had a larger area stenosis (AS) [59.4 (54.6-67.7)% vs 52.8 (43.8-60.1)%, p < 0.001], maximum time averaged WSS (TAWSS) [11.56 (8.25-13.64)Pa vs 7.73 (5.41-11.51)Pa, p < 0.001], mean TAWSS at the minimum lumen area (MLA) [9.30 (5.44-11.94)Pa vs 6.19 (3.96-9.00)Pa, p < 0.001] and maximum transverse WSS [0.30 (0.21-0.45)Pa vs 0.23 (0.17-0.32)Pa, p=0.002] than those remaining quiescent. In multivariable models, AS was the only independent predictor of LOCE. Kaplan-Meier curves demonstrated that lesions with elevated maximum TAWSS and AS had a higher rate of LOCE than those with low TAWSS and AS values (26 % vs 7 %, p < 0.001).

CONCLUSIONS

For non-flow limiting lesions with borderline negative FFR, fast WSS computation using a dedicated software is feasible and holds potential for cardiovascular risk stratification.

Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning

There is increasing evidence that coronary artery wall shear stress (WSS) measurement provides useful prognostic information that allows prediction of adverse cardiovascular events. Computational Fluid Dynamics (CFD) has been extensively used in research to measure vessel physiology and examine the role of the local haemodynamic forces on the evolution of atherosclerosis. Nonetheless, CFD modelling remains computationally expensive and time-consuming, making its direct use in clinical practice inconvenient. A number of studies have investigated the use of deep learning (DL) approaches for fast WSS prediction. However, in these reports, patient data were limited and most of them used synthetic data generation methods for developing the training set. In this paper, we implement 2 approaches for synthetic data generation and combine their output with real patient data in order to train a DL model with a U-net architecture for prediction of WSS in the coronary arteries. The model achieved 6.03% Normalised Mean Absolute Error (NMAE) with inference taking only 0.35 s; making this solution time-efficient and clinically relevant.

Influence of intracoronary hemodynamic forces on atherosclerotic plaque phenotypes

BACKGROUND AND AIMS

Coronary hemodynamics impact coronary plaque progression and destabilization. The aim of the present study was to establish the association between focal vs. diffuse intracoronary pressure gradients and wall shear stress (WSS) patterns with atherosclerotic plaque composition.

METHODS

Prospective, international, single-arm study of patients with chronic coronary syndromes and hemodynamic significant lesions (fractional flow reserve [FFR] ≤ 0.80). Motorized FFR pullback pressure gradient (PPG), optical coherence tomography (OCT), and time-average WSS (TAWSS) and topological shear variation index (TSVI) derived from three-dimensional angiography were obtained.

RESULTS

One hundred five vessels (median FFR 0.70 [Interquartile range (IQR) 0.56-0.77]) had combined PPG and WSS analyses. TSVI was correlated with PPG (r = 0.47, [95% Confidence Interval (95% CI) 0.30-0.65], p < 0.001). Vessels with a focal CAD (PPG above the median value of 0.67) had significantly higher TAWSS (14.8 [IQR 8.6-24.3] vs. 7.03 [4.8-11.7] Pa, p < 0.001) and TSVI (163.9 [117.6-249.2] vs. 76.8 [23.1-140.9] m-1, p < 0.001). In the 51 vessels with baseline OCT, TSVI was associated with plaque rupture (OR 1.01 [1.00-1.02], p = 0.024), PPG with the extension of lipids (OR 7.78 [6.19-9.77], p = 0.003), with the presence of thin-cap fibroatheroma (OR 2.85 [1.11-7.83], p = 0.024) and plaque rupture (OR 4.94 [1.82 to 13.47], p = 0.002).

CONCLUSIONS

Focal and diffuse coronary artery disease, defined using coronary physiology, are associated with differential WSS profiles. Pullback pressure gradients and WSS profiles are associated with atherosclerotic plaque phenotypes. Focal disease (as identified by high PPG) and high TSVI are associated with high-risk plaque features.

CLINICAL TRIAL REGISTRATION

https://clinicaltrials,gov/ct2/show/NCT03782688.

Comprehensive biomechanical and anatomical atherosclerotic plaque metrics predict major adverse cardiovascular events: A new tool for clinical decision making

BACKGROUND AND AIMS

Anatomical imaging alone of coronary atherosclerotic plaques is insufficient to identify risk of future adverse events and guide management of non-culprit lesions. Low endothelial shear stress (ESS) and high plaque structural stress (PSS) are associated with events, but individually their predictive value is insufficient for risk prediction. We determined whether combining multiple complementary, biomechanical and anatomical plaque characteristics improves outcome prediction sufficiently to inform clinical decision-making.

METHODS

We examined baseline ESS, ESS gradient (ESSG), PSS, and PSS heterogeneity index (HI), and plaque burden in 22 lesions that developed subsequent events and 64 control lesions that remained quiescent from the PROSPECT study.

RESULTS

86 fibroatheromas were analysed from 67 patients. Lesions with events showed higher PSS HI (0.32 vs. 0.24, p<0.001), lower local ESS (0.56Pa vs. 0.91Pa, p = 0.007), and higher ESSG (3.82 Pa/mm vs. 1.96 Pa/mm, p = 0.007), while high PSS HI (hazard ratio [HR] 3.9, p = 0.006), high ESSG (HR 3.4, p = 0.007) and plaque burden>70 % (HR 2.6, p = 0.02) were independent outcome predictors in multivariate analysis. Combining low ESS, high ESSG, and high PSS HI gave both high positive predictive value (80 %), which increased further combined with plaque burden>70 %, and negative predictive value (81.6 %). Low ESS, high ESSG, and high PSS HI co-localised spatially within 1 mm in lesions with events, and importantly, this cluster was distant from the minimum lumen area site.

CONCLUSIONS

Combining complementary biomechanical and anatomical metrics significantly improves risk-stratification of individual coronary lesions. If confirmed from larger prospective studies, our results may inform targeted revascularisation vs. conservative management strategies.

Blood Flow Energy Identifies Coronary Lesions Culprit of Future Myocardial Infarction

The present study establishes a link between blood flow energy transformations in coronary atherosclerotic lesions and clinical outcomes. The predictive capacity for future myocardial infarction (MI) was compared with that of established quantitative coronary angiography (QCA)-derived predictors. Angiography-based computational fluid dynamics (CFD) simulations were performed on 80 human coronary lesions culprit of MI within 5 years and 108 non-culprit lesions for future MI. Blood flow energy transformations were assessed in the converging flow segment of the lesion as ratios of kinetic and rotational energy values (KER and RER, respectively) at the QCA-identified minimum lumen area and proximal lesion sections. The anatomical and functional lesion severity were evaluated with QCA to derive percentage area stenosis (%AS), vessel fractional flow reserve (vFFR), and translesional vFFR (ΔvFFR). Wall shear stress profiles were investigated in terms of topological shear variation index (TSVI). KER and RER predicted MI at 5 years (AUC = 0.73, 95% CI 0.65-0.80, and AUC = 0.76, 95% CI 0.70-0.83, respectively; p < 0.0001 for both). The predictive capacity for future MI of KER and RER was significantly stronger than vFFR (p = 0.0391 and p = 0.0045, respectively). RER predictive capacity was significantly stronger than %AS and ΔvFFR (p = 0.0041 and p = 0.0059, respectively). The predictive capacity for future MI of KER and RER did not differ significantly from TSVI. Blood flow kinetic and rotational energy transformations were significant predictors for MI at 5 years (p < 0.0001). The findings of this study support the hypothesis of a biomechanical contribution to the process of plaque destabilization/rupture leading to MI.

Relationship of Coronary Angiography-Derived Radial Wall Strain With Functional Significance, Plaque Morphology, and Clinical Outcomes

BACKGROUND

Coronary angiography-derived radial wall strain (RWS) is a newly developed index that can be readily accessed and describes the biomechanical features of a lesion.

OBJECTIVES

The authors sought to investigate the association of RWS with fractional flow reserve (FFR) and high-risk plaque (HRP), and their relative prognostic implications.

METHODS

We included 484 vessels (351 patients) deferred after FFR measurement with available RWS data and coronary computed tomography angiography. On coronary computed tomography angiography, HRP was defined as a lesion with both minimum lumen area <4 mm2 and plaque burden ≥70%. The primary outcome was target vessel failure (TVF), a composite of target vessel revascularization, target vessel myocardial infarction, or cardiac death.

RESULTS

The mean FFR and RWSmax were 0.89 ± 0.07 and 11.2% ± 2.5%, respectively, whereas 27.7% of lesions had HRP, 15.1% had FFR ≤0.80. An increase in RWSmax was associated with a higher risk of FFR ≤0.80 and HRP, which was consistent after adjustment for clinical or angiographic characteristics (all P < 0.05). An increment of RWSmax was related to a higher risk of TVF (HR: 1.23 [95% CI: 1.03-1.47]; P = 0.022) with an optimal cutoff of 14.25%. RWSmax >14% was a predictor of TVF after adjustment for FFR or HRP components (all P < 0.05) and showed a direct prognostic effect on TVF, not mediated by FFR ≤0.80 or HRP in the mediation analysis. When high RWSmax was added to FFR ≤0.80 or HRP, there were increasing outcome trends (all P for trend <0.001).

CONCLUSIONS

RWS was associated with coronary physiology and plaque morphology but showed independent prognostic significance.

Computational fluid dynamics as supporting technology for coronary artery disease diagnosis and treatment: an international survey

BACKGROUND

Computational fluid dynamics (CFD) is emerging as an effective technology able to improve procedural outcomes and enhance clinical decision-making in patients with coronary artery disease (CAD). The present study aims to assess the state of knowledge, use and clinical acceptability of CFD in the diagnosis and treatment of CAD.

METHODS

We realized a 20-questions international, anonymous, cross-sectional survey to cardiologists to test their knowledge and confidence on CFD as a technology applied to patients suffering from CAD. Responses were recorded between May 18, 2022, and June 12, 2022.

RESULTS

A total of 466 interventional cardiologists (mean age 48.4 ± 8.3 years, males 362), from 42 different countries completed the survey, for a response rate of 45.9%. Of these, 66.6% declared to be familiar with the term CFD, especially for optimization of existing interventional techniques (16.1%) and assessment of hemodynamic quantities related with CAD (13.7%). About 30% of respondents correctly answered to the questions exploring their knowledge on the pathophysiological role of some CFD-derived quantities such as wall shear stress and helical flow in coronary arteries. Among respondents, 85.9% would consider patient-specific CFD-based analysis in daily interventional practice while 94.2% declared to be interested in receiving a brief foundation course on the basic CFD principles. Finally, 87.7% of respondents declared to be interested in a cath-lab software able to conduct affordable CFD-based analyses at the point-of-care.

CONCLUSIONS

Interventional cardiologists reported to be profoundly interested in adopting CFD simulations as a technology supporting decision making in the treatment of CAD in daily practice.

Mechanism Analysis of Vascular Calcification Based on Fluid Dynamics

Vascular calcification is the abnormal deposition of calcium phosphate complexes in blood vessels, which is regarded as the pathological basis of multiple cardiovascular diseases. The flowing blood exerts a frictional force called shear stress on the vascular wall. Blood vessels have different hydrodynamic properties due to discrepancies in geometric and mechanical properties. The disturbance of the blood flow in the bending area and the branch point of the arterial tree produces a shear stress lower than the physiological magnitude of the laminar shear stress, which can induce the occurrence of vascular calcification. Endothelial cells sense the fluid dynamics of blood and transmit electrical and chemical signals to the full-thickness of blood vessels. Through crosstalk with endothelial cells, smooth muscle cells trigger osteogenic transformation, involved in mediating vascular intima and media calcification. In addition, based on the detection of fluid dynamics parameters, emerging imaging technologies such as 4D Flow MRI and computational fluid dynamics have greatly improved the early diagnosis ability of cardiovascular diseases, showing extremely high clinical application prospects.

Agreement of wall shear stress distribution between two core laboratories using three-dimensional quantitative coronary angiography

Wall shear stress (WSS) estimated in models reconstructed from intravascular imaging and 3-dimensional-quantitative coronary angiography (3D-QCA) data provides important prognostic information and enables identification of high-risk lesions. However, these analyses are time-consuming and require expertise, limiting WSS adoption in clinical practice. Recently, a novel software has been developed for real-time computation of time-averaged WSS (TAWSS) and multidirectional WSS distribution. This study aims to examine its inter-corelab reproducibility. Sixty lesions (20 coronary bifurcations) with a borderline negative fractional flow reserve were processed using the CAAS Workstation WSS prototype to estimate WSS and multi-directional WSS values. Analysis was performed by two corelabs and their estimations for the WSS in 3 mm segments across each reconstructed vessel was extracted and compared. In total 700 segments (256 located in bifurcated vessels) were included in the analysis. A high intra-class correlation was noted for all the 3D-QCA and TAWSS metrics between the estimations of the two corelabs irrespective of the presence (range: 0.90-0.92) or absence (range: 0.89-0.90) of a coronary bifurcation, while the ICC was good-moderate for the multidirectional WSS (range: 0.72-0.86). Lesion level analysis demonstrated a high agreement of the two corelabls for detecting lesions exposed to an unfavourable haemodynamic environment (WSS > 8.24 Pa, κ = 0.77) that had a high-risk morphology (area stenosis > 61.3%, κ = 0.71) and were prone to progress and cause events. The CAAS Workstation WSS enables reproducible 3D-QCA reconstruction and computation of WSS metrics. Further research is needed to explore its value in detecting high-risk lesions.

Is spontaneous coronary artery dissection (SCAD) related to local anatomy and hemodynamics? An exploratory study

AIMS

Spontaneous coronary artery dissection (SCAD) is an increasingly diagnosed cause of myocardial infarction with unclear pathophysiology. The aim of the study was to test if vascular segments site of SCAD present distinctive local anatomy and hemodynamic profiles.

METHODS

Coronary arteries with spontaneously healed SCAD (confirmed by follow-up angiography) underwent three-dimensional reconstruction, morphometric analysis with definition of vessel local curvature and torsion, and computational fluid dynamics (CFD) simulations with derivation of time-averaged wall shear stress (TAWSS) and topological shear variation index (TSVI). The (reconstructed) healed proximal SCAD segment was visually inspected for co-localization with curvature, torsion, and CFD-derived quantities hot spots.

RESULTS

Thirteen vessels with healed SCAD underwent the morpho-functional analysis. Median time between baseline and follow-up coronary angiograms was 57 (interquartile range [IQR] 45-95) days. In seven cases (53.9%), SCAD was classified as type 2b and occurred in the left anterior descending artery or near a bifurcation. In all cases (100%), at least one hot spot co-localized within the healed proximal SCAD segment, in 9 cases (69.2%) ≥3 hot spots were identified. Healed SCAD in proximity of a coronary bifurcation presented lower TAWSS peak values (6.65 [IQR 6.20-13.2] vs. 3.81 [2.53-5.17] Pa, p = 0.008) and hosted less frequently TSVI hot spots (100% vs. 57.1%, p = 0.034).

CONCLUSION

Vascular segments of healed SCAD were characterized by high curvature/torsion and WSS profiles reflecting increased local flow disturbances. Hence, a pathophysiological role of the interaction between vessel anatomy and shear forces in SCAD is hypothesized.

Long-term prognostic implications of hemodynamic and plaque assessment using coronary CT angiography

BACKGROUND AND AIMS

Hemodynamic and plaque characteristics can be analyzed using coronary CT angiography (CTA). We aimed to explore long-term prognostic implications of hemodynamic and plaque characteristics using coronary CT angiography (CTA).

METHODS

Invasive fractional flow reserve (FFR) and CTA-derived FFR (FFR_CT) were undertaken for 136 lesions in 78 vessels and followed-up to 10 years until December 2020. FFR_CT, wall shear stress (WSS), change in FFR_CT across the lesion (ΔFFR_CT), total plaque volume (TPV), percent atheroma volume (PAV), and low-attenuation plaque volume (LAPV) for target lesions [L] and vessels [V] were obtained by independent core laboratories. Their collective influence was evaluated for the clinical endpoints of target vessel failure (TVF) and target lesion failure (TLF).

RESULTS

During a median follow-up of 10.1 years, PAV[V] (per 10% increase, HR 2.32 [95% CI 1.11-4.86], p = 0.025), and FFR_CT[V] (per 0.1 increase, HR 0.56 [95% CI 0.37-0.84], p = 0.006) were independent predictors of TVF for the per-vessel analysis, and WSS[L] (per 100 dyne/cm² increase, HR 1.43 [1.09-1.88], p = 0.010), LAPV[L] (per 10 mm³ increase, HR 3.81 [1.16-12.5], p = 0.028), and ΔFFR_CT[L] (per 0.1 increase, HR 1.39 [1.02-1.90], p = 0.040) were independent predictors of TLF for the per-lesion analysis after adjustment for clinical and lesion characteristics. The addition of both plaque and hemodynamic predictors improved the predictability for 10-year TVF and TLF of clinical and lesion characteristics (all p < 0.05).

CONCLUSIONS

Vessel- and lesion-level hemodynamic characteristics, and vessel-level plaque quantity, and lesion-level plaque compositional characteristics assessed by CTA offer independent and additive long-term prognostic value.

Transient wall shear stress estimation in coronary bifurcations using convolutional neural networks

BACKGROUND AND OBJECTIVE

Haemodynamic metrics, such as blood flow induced shear stresses at the inner vessel lumen, are associated with the development and progression of coronary artery disease. Understanding these metrics may therefore improve the assessment of an individual's coronary disease risk. However, the calculation of such luminal Wall Shear Stress (WSS) using traditional Computational Fluid Dynamics (CFD) methods is relatively slow and computationally expensive. As a result, CFD based haemodynamic computation is not suitable for integrated and large-scale use in clinical settings.

METHODS

In this work, deep learning techniques are proposed as an alternative method to CFD, whereby luminal WSS magnitude can be predicted in coronary bifurcations throughout the cardiac cycle based on the steady state solution (which takes <120 seconds to calculate including preprocessing), vessel geometry and additional global features. The deep learning model is trained on a dataset of 101 patient-specific and 2626 synthetic left main bifurcation models with 26 separate patient-specific cases used as the test set.

RESULTS

The model showed high fidelity predictions with <5% (normalised against mean WSS magnitude) deviation to CFD derived values as the gold-standard method, while being orders of magnitude faster with on average <2 minutes versus 3 hours computation for transient CFD.

CONCLUSIONS

This method therefore offers a new approach to substantially reduce the computational cost involved in, for example, large-scale population studies of coronary haemodynamic metrics, and may therefore open the pathway for future clinical integration.

An automated software for real-time quantification of wall shear stress distribution in quantitative coronary angiography data

BACKGROUND

Wall shear stress (WSS) estimated in 3D-quantitative coronary angiography (QCA) models appears to provide useful prognostic information and identifies high-risk patients and lesions. However, conventional computational fluid dynamics (CFD) analysis is cumbersome limiting its application in the clinical arena. This report introduces a user-friendly software that allows real-time WSS computation and examines its reproducibility and accuracy in assessing WSS distribution against conventional CFD analysis.

METHODS

From a registry of 414 patients with borderline negative fractional flow reserve (0.81-0.85), 100 lesions were randomly selected. 3D-QCA and CFD analysis were performed using the conventional approach and the novel CAAS Workstation WSS software, and QCA as well as WSS estimations of the two approaches were compared. The reproducibility of the two methodologies was evaluated in a subgroup of 50 lesions.

RESULTS

A good agreement was noted between the conventional approach and the novel software for 3D-QCA metrics (ICC range: 0.73-0-93) and maximum WSS at the lesion site (ICC: 0.88). Both methodologies had a high reproducibility in assessing lesion severity (ICC range: 0.83-0.97 for the conventional approach; 0.84-0.96 for the CAAS Workstation WSS software) and WSS distribution (ICC: 0.85-0.89 and 0.83-0.87, respectively). Simulation time was significantly shorter using the CAAS Workstation WSS software compared to the conventional approach (4.13 ± 0.59 min vs 23.14 ± 2.56 min, p < 0.001).

CONCLUSION

CAAS Workstation WSS software is fast, reproducible, and accurate in assessing WSS distribution. Therefore, this software is expected to enable the broad use of WSS metrics in the clinical arena to identify high-risk lesions and vulnerable patients.

Modelling coronary flows: impact of differently measured inflow boundary conditions on vessel-specific computational hemodynamic profiles

BACKGROUND AND OBJECTIVES

The translation of hemodynamic quantities based on wall shear stress (WSS) or intravascular helical flow into clinical biomarkers of coronary atherosclerotic disease is still hampered by the assumptions/idealizations required by the computational fluid dynamics (CFD) simulations of the coronary hemodynamics. In the resulting budget of uncertainty, inflow boundary conditions (BCs) play a primary role. Accordingly, in this study we investigated the impact of the approach adopted for in vivo coronary artery blood flow rate assessment on personalized CFD simulations where blood flow rate is used as inflow BC.

METHODS

CFD simulations were carried out on coronary angiograms by applying personalized inflow BCs derived from four different techniques assessing in vivo surrogates of flow rate: continuous thermodilution, intravascular Doppler, frame count-based 3D contrast velocity, and diameter-based scaling law. The impact of inflow BCs on coronary hemodynamics was evaluated in terms of WSS- and helicity-based quantities.

RESULTS

As main findings, we report that: (i) coronary flow rate values may differ based on the applied flow derivation technique, as continuous thermodilution provided higher flow rate values than intravascular Doppler and diameter-based scaling law (p = 0.0014 and p = 0.0023, respectively); (ii) such intrasubject differences in flow rate values lead to different surface-averaged values of WSS magnitude and helical blood flow intensity (p<0.0020); (iii) luminal surface areas exposed to low WSS and helical flow topological features showed robustness to the flow rate values.

CONCLUSIONS

Although the absence of a clinically applicable gold standard approach prevents a general recommendation for one coronary blood flow rate derivation technique, our findings indicate that the inflow BC may impact computational hemodynamic results, suggesting that a standardization would be desirable to provide comparable results among personalized CFD simulations of the coronary hemodynamics.

The Association Between Time-Varying Wall Shear Stress and the Development of Plaque Ulcerations in Carotid Arteries From the Plaque at Risk Study

Background and Purpose: Shear stress (WSS) is involved in the pathophysiology of atherosclerotic disease and might affect plaque ulceration. In this case-control study, we compared carotid plaques that developed a new ulcer during follow-up and plaques that remained silent for their exposure to time-dependent oscillatory shear stress parameters at baseline.

Materials and Methods: Eighteen patients who underwent CTA and MRI of their carotid arteries at baseline and 2 years follow-up were included. These 18 patients consisted of six patients who demonstrated a new ulcer and 12 control patients selected from a larger cohort with similar MRI-based plaque characteristics as the ulcer group. (Oscillatory) WSS parameters [time average WSS, oscillatory shear index (OSI), and relative residence time (RRT)] were calculated using computational fluid dynamics applying the MRI-based geometry of the carotid arteries and compared among plaques (wall thickness>2 mm) with and without ulceration (Mann–Whitney U test) and ulcer-site vs. non-ulcer-site within the plaque (Wilcoxon signed rank test). More detailed analysis on ulcer cases was performed and the predictive value of oscillatory WSS parameters was calculated using linear and logistic mixed-effect regression models.

Results: The ulcer group demonstrated no difference in maximum WSS [9.9 (6.6–18.5) vs. 13.6 (9.7–17.7) Pa, p = 0.349], a lower maximum OSI [0.04 (0.01–0.10) vs. 0.12 (0.06–0.20) p = 0.019] and lower maximum RRT [1.25 (0.78–2.03) Pa⁻¹ vs. 2.93 (2.03–5.28) Pa⁻¹, p = 0.011] compared to controls. The location of the ulcer (ulcer-site) within the plaque was not always at the maximal WSS, but demonstrated higher average WSS, lower average RRT and OSI at the ulcer-site compared to the non-ulcer-sites. High WSS (WSS>4.3 Pa) and low RRT (RRT < 0.25 Pa) were associated with ulceration with an odds ratio of 3.6 [CI 2.1–6.3] and 2.6 [CI 1.54–4.44] respectively, which remained significant after adjustment for wall thickness.

Conclusion: In this explorative study, ulcers were not exclusively located at plaque regions exposed to the highest WSS, OSI, or RRT, but high WSS and low RRT regions had a significantly higher odds to present ulceration within the plaque even after adjustment for wall thickness.

Risk of myocardial infarction based on endothelial shear stress analysis using coronary angiography

BACKGROUND AND AIMS

Wall shear stress (WSS) has been associated with atherogenesis and plaque progression. The present study assessed the value of WSS analysis derived from conventional coronary angiography to detect lesions culprit for future myocardial infarction (MI).

METHODS AND RESULTS

Three-dimensional quantitative coronary angiography (3DQCA), was used to calculate WSS and pressure drop in 80 patients. WSS descriptors were compared between 80 lesions culprit of future MI and 108 non-culprit lesions (controls). Endothelium-blood flow interaction was assessed by computational fluid dynamics (10.8 ± 1.41 min per vessel). Median time between baseline angiography and MI was 25.9 (21.9-29.8) months. Mean patient age was 70.3 ± 12.7. Clinical presentation was STEMI in 35% and NSTEMI in 65%. Culprit lesions showed higher percent area stenosis (%AS), translesional vFFR difference (ΔvFFR), time-averaged WSS (TAWSS) and topological shear variation index (TSVI) compared to non-culprit lesions (p < 0.05 for all). TSVI was superior to TAWSS in predicting MI (AUC-TSVI = 0.77, 95%CI 0.71-0.84 vs. AUC-TAWSS = 0.61, 95%CI 0.53-0.69, p < 0.001). The addition of TSVI increased predictive and reclassification abilities compared to a model based on %AS and ΔvFFR (NRI = 1.04, p < 0.001, IDI = 0.22, p < 0.001).

CONCLUSIONS

A 3DQCA-based WSS analysis was feasible and can identify lesions culprit for future MI. The combination of area stenoses, pressure gradients and WSS predicted the occurrence of MI. TSVI, a novel WSS descriptor, showed strong predictive capacity to detect lesions prone to cause MI.

CT-based fractional flow reserve: development and expanded application

Computations of fractional flow reserve, based on CT coronary angiography and computational fluid dynamics (CT-based FFR) to assess the severity of coronary artery stenosis, was introduced around a decade ago and is now one of the most successful applications of computational fluid dynamic modelling in clinical practice. Although the mathematical modelling framework behind this approach and the clinical operational model vary, its clinical efficacy has been demonstrated well in general. In this review, technical elements behind CT-based FFR computation are summarised with some key assumptions and challenges. Examples of these challenges include the complexity of the model (such as blood viscosity and vessel wall compliance modelling), whose impact has been debated in the research. Efforts made to address the practical challenge of processing time are also reviewed. Then, further application areas—myocardial bridge, renal stenosis and lower limb stenosis—are discussed along with specific challenges expected in these areas.

Investigation of Wall Shear Stress in Cardiovascular Research and in Clinical Practice-From Bench to Bedside

In the 1900s, researchers established animal models experimentally to induce atherosclerosis by feeding them with a cholesterol-rich diet. It is now accepted that high circulating cholesterol is one of the main causes of atherosclerosis; however, plaque localization cannot be explained solely by hyperlipidemia. A tremendous amount of studies has demonstrated that hemodynamic forces modify endothelial athero-susceptibility phenotypes. Endothelial cells possess mechanosensors on the apical surface to detect a blood stream-induced force on the vessel wall, known as "wall shear stress (WSS)", and induce cellular and molecular responses. Investigations to elucidate the mechanisms of this process are on-going: on the one hand, hemodynamics in complex vessel systems have been described in detail, owing to the recent progress in imaging and computational techniques. On the other hand, investigations using unique in vitro chamber systems with various flow applications have enhanced the understanding of WSS-induced changes in endothelial cell function and the involvement of the glycocalyx, the apical surface layer of endothelial cells, in this process. In the clinical setting, attempts have been made to measure WSS and/or glycocalyx degradation non-invasively, for the purpose of their diagnostic utilization. An increasing body of evidence shows that WSS, as well as serum glycocalyx components, can serve as a predicting factor for atherosclerosis development and, most importantly, for the rupture of plaques in patients with high risk of coronary heart disease.