Low Wall Shear Stress Is Associated with Saphenous Vein Graft Stenosis in Patients with Coronary Artery Bypass Grafting.

Biomechanical forces may play a key role in saphenous vein graft (SVG) disease after coronary artery bypass graft (CABG) surgery. Computed tomography angiography (CTA) of 430 post-CABG patients were evaluated and 15 patients were identified with both stenosed and healthy SVGs for paired analysis. The stenosis was virtually removed, and detailed 3D models were reconstructed to perform patient-specific computational fluid dynamic (CFD) simulations. Models were processed to compute anatomic parameters, and hemodynamic parameters such as local and vessel-averaged wall shear stress (WSS), normalized WSS (WSS*), low shear area (LSA), oscillatory shear index (OSI), and flow rate. WSS* was significantly lower in pre-diseased SVG segments compared to corresponding control segments without disease (1.22 vs. 1.73, p = 0.012) and the area under the ROC curve was 0.71. No differences were observed in vessel-averaged anatomic or hemodynamic parameters between pre-stenosed and control whole SVGs. There are currently no clinically available tools to predict SVG failure post-CABG. CFD modeling has the potential to identify high-risk CABG patients who may benefit from more aggressive medical therapy and closer surveillance. Graphical Abstract.

Uncertainty quantification of simulated biomechanical stimuli in coronary artery bypass grafts.

Coronary artery bypass graft surgery (CABG) is performed on more than 400,000 patients annually in the U.S. However, saphenous vein grafts (SVGs) implanted during CABG exhibit poor patency compared to arterial grafts, with failure rates up to 40% within 10 years after surgery. Differences in mechanical stimuli are known to play a role in driving maladaptation and have been correlated with endothelial damage and thrombus formation. As these quantities are difficult to measure in vivo, multi-scale coronary models offer a way to quantify them, while accounting for complex coronary physiology. However, prior studies have primarily focused on deterministic evaluations, without reporting variability in the model parameters due to uncertainty. This study aims to assess confidence in multi-scale predictions of wall shear stress and wall strain while accounting for uncertainty in peripheral hemodynamics and material properties. Boundary condition distributions are computed by assimilating uncertain clinical data, while spatial variations of vessel wall stiffness are obtained through approximation by a random field. We developed a stochastic submodeling approach to mitigate the computational burden of repeated multi-scale model evaluations to focus exclusively on the bypass grafts. This produces a two-level decomposition of quantities of interest into submodel contributions and full model/submodel discrepancies. We leverage these two levels in the context of forward uncertainty propagation using a previously proposed multi-resolution approach. The time- and space-averaged wall shear stress is well estimated with a coefficient of variation of <35%, but ignorance about the spatial distribution on the wall elastic modulus and thickness lead to large variations in an objective measure of wall strain, with coefficients of variation up to 100%. Sensitivity analysis reveals how the interactions between the flow and material parameters contribute to output variability.

Hemodynamic variables in aneurysms are associated with thrombotic risk in children with Kawasaki disease.

BACKGROUND: Thrombosis is a major adverse outcome associated with coronary artery aneurysms (CAAs) resulting from Kawasaki disease (KD). Clinical guidelines recommend initiation of anticoagulation therapy with maximum CAA diameter (Dmax) ≥8 mm or Z-score ≥ 10. Here, we investigate the role of aneurysm hemodynamics as a superior method for thrombotic risk stratification in KD patients. METHODS AND RESULTS: We retrospectively studied ten KD patients with CAAs, including five patients who developed thrombosis. We constructed patient-specific anatomic models from cardiac magnetic resonance images and performed computational hemodynamic simulations using SimVascular. Our simulations incorporated pulsatile flow, deformable arterial walls and boundary conditions automatically tuned to match patient-specific arterial pressure and cardiac output. From simulation results, we derived local hemodynamic variables including time-averaged wall shear stress (TAWSS), low wall shear stress exposure, and oscillatory shear index (OSI). Local TAWSS was significantly lower in CAAs that developed thrombosis (1.2 ±â€¯0.94 vs. 7.28 ±â€¯9.77 dynes/cm2, p = 0.006) and the fraction of CAA surface area exposed to low wall shear stress was larger (0.69 ±â€¯0.17 vs. 0.25 ±â€¯0.26%, p = 0.005). Similarly, longer residence times were obtained in branches where thrombosis was confirmed (9.07 ±â€¯6.26 vs. 2.05 ±â€¯2.91 cycles, p = 0.004). No significant differences were found for OSI or anatomical measurements such us Dmax and Z-score. Assessment of thrombotic risk according to hemodynamic variables had higher sensitivity and specificity compared to standard clinical metrics (Dmax, Z-score). CONCLUSIONS: Hemodynamic variables can be obtained non-invasively via simulation and may provide improved thrombotic risk stratification compared to current diameter-based metrics, facilitating long-term clinical management of KD patients with persistent CAAs.

Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge.

PURPOSE: Image-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual groups, have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline. METHODS: 3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods, boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters. RESULTS: A total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability. CONCLUSIONS: Wide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guidelines for model extents, inflow rates, and blood properties, and by encouraging the reporting of normalized hemodynamic parameters.

Automated Tuning for Parameter Identification and Uncertainty Quantification in Multi-scale Coronary Simulations.

Atherosclerotic coronary artery disease, which can result in coronary artery stenosis, acute coronary artery occlusion, and eventually myocardial infarction, is a major cause of morbidity and mortality worldwide. Non-invasive characterization of coronary blood flow is important to improve understanding, prevention, and treatment of this disease. Computational simulations can now produce clinically relevant hemodynamic quantities using only non-invasive measurements, combining detailed three dimensional fluid mechanics with physiological models in a multiscale framework. These models, however, require specification of numerous input parameters and are typically tuned manually without accounting for uncertainty in the clinical data, hindering their application to large clinical studies. We propose an automatic, Bayesian, approach to parameter estimation based on adaptive Markov chain Monte Carlo sampling that assimilates non-invasive quantities commonly acquired in routine clinical care, quantifies the uncertainty in the estimated parameters and computes the confidence in local predicted hemodynamic indicators.