High precision invasive FFR, low-cost invasive iFR, or non-invasive CFR?: optimum assessment of coronary artery stenosis based on the patient-specific computational models

Effect of upper body venoarterial ECMO on systemic hemodynamics and oxygenation: A computational study

BACKGROUND

This study seeks to quantify the effects of upper body veno-arterial extracorporeal membrane oxygenation (VA ECMO) on the anatomical distribution of oxygen delivery in the setting of hypoxic respiratory failure and provide new insights that will guide clinical use of this support strategy to bridge patients to lung transplant.

METHODS

Employing a patient-specific vascular geometry and a quantitative model of oxygen transport, computational simulations were performed to determine hemodynamics and oxygen delivery in the ascending and descending aorta, left and right coronary arteries, and great vessels during upper body VA ECMO support. Oxygen content in ECMO circuit blood flow was varied while considering different degrees of lung failure severity. Using lumped parameter models to dynamically apply perfusion boundary conditions, hemodynamic parameters and oxygen content were analyzed to assess the effect of ECMO supply titration.

RESULTS

The results emphasize the importance of anatomical distribution for tissue oxygen delivery in severe lung failure, with ECMO-derived flow primarily augmenting oxygen content in specific vascular beds. They also demonstrate that although cannulating the subclavian artery can enhance cerebral oxygen delivery, its ability to ensure sufficient oxygen delivery to the coronary circulation seems to be comparatively restricted.

CONCLUSIONS

The oxygen delivery to a specific vascular area is primarily determined by the oxygen content in the source of perfusion. Caution is advised with upper body VA ECMO for patients with hypoxic respiratory failure and right ventricle dysfunction, due to potential coronary ischemia. Management of these patients is challenging due to disease progression and organ availability uncertainties.

Type B aortic dissection in Marfan patients after the David procedure: Insights from patient-specific simulation

Objective

An elevated risk of acute type B aortic dissection exists in patients with Marfan syndrome after the David procedure. This study explores hemodynamic changes in the descending aorta postsurgery.

Methods

A single-center retrospective review identified 5 patients with Marfan syndrome who experienced acute type B aortic dissection within 6 years after the David procedure, alongside 5 matched patients with Marfan syndrome without dissection more than 6 years postsurgery. Baseline and postoperative computed tomography and magnetic resonance scans were analyzed for aortic geometry reconstruction. Computational fluid dynamic simulations evaluated preoperative and postoperative hemodynamics.

Results

Patients with acute type B aortic dissection showed lower blood flow velocities, increased vortices, and altered velocity profiles in the proximal descending aorta compared with controls. Preoperatively, median time-averaged wall shear stress in the descending aorta was lower in patients with acute type B aortic dissection (control: 1.76 [1.50-2.83] Pa, dissection: 1.16 [1.06-1.30] Pa, P = .047). Postsurgery, neither group had significant time-averaged wall shear stress changes (dissection: P = .69, control: P = .53). Localized analysis revealed surgery-induced time-averaged wall shear stress increases near the subclavian artery in the dissection group (range, +0.30 to +1.05 Pa, each comparison, P < .05). No such changes were observed in controls. Oscillatory shear index and relative residence time were higher in patients with acute type B aortic dissection before and after surgery versus controls.

Conclusions

Hemodynamics likely play a role in post-David procedure acute type B aortic dissection. Further investigation into aortic geometry, hemodynamics, and postoperative acute type B aortic dissection is vital for enhancing outcomes and refining surgical strategies in patients with Marfan syndrome.

Spectrum of Non-Obstructive Coronary Artery Disease and Its Relationship with Atrial Fibrillation

This article aims to analyze the relationship between non-obstructive coronary artery disease (NOCAD) and atrial fibrillation (AF), exploring the underlying pathophysiological mechanisms and implications for clinical management. NOCAD and AF are prevalent cardiovascular conditions that often coexist, yet their interrelation is not well understood. NOCAD can lead to ischemic necrosis of cardiomyocytes and their replacement with fibrous tissue, sustaining focal ectopic activity in atrial myocardium. Atrial fibrillation, on the other hand, the most common sustained cardiac arrhythmia, is able to accelerate atherosclerosis and increase oxygen consumption in the myocardium, creating a mismatch between supply and demand, and thus promoting the development or worsening of coronary ischemia. Therefore, NOCAD and AF seem to be a complex interplay with one begets another.

Non-invasive fractional flow reserve estimation in coronary arteries using angiographic images

Coronary artery disease is the leading global cause of mortality and Fractional Flow Reserve (FFR) is widely regarded as the gold standard for assessing coronary artery stenosis severity. However, due to the limitations of invasive FFR measurements, there is a pressing need for a highly accurate virtual FFR calculation framework. Additionally, it's essential to consider local haemodynamic factors such as time-averaged wall shear stress (TAWSS), which play a critical role in advancement of atherosclerosis. This study introduces an innovative FFR computation method that involves creating five patient-specific geometries from two-dimensional coronary angiography images and conducting numerical simulations using computational fluid dynamics with a three-element Windkessel model boundary condition at the outlet to predict haemodynamic distribution. Furthermore, four distinct boundary condition methodologies are applied to each geometry for comprehensive analysis. Several haemodynamic features, including velocity, pressure, TAWSS, and oscillatory shear index are investigated and compared for each case. Results show that models with average boundary conditions can predict FFR values accurately and observed errors between invasive FFR and virtual FFR are found to be less than 5%.

Impact of minimal lumen segmentation uncertainty on patient-specific coronary simulations: A look at FFRCT

We examined the effect of minimal lumen segmentation uncertainty on Fractional Flow Reserve obtained from Coronary Computed Tomography AngiographyFFR CT . A total of 14 patient-specific coronary models with different stenosis locations and degrees of severity were enrolled in this study. The optimal segmented coronary lumens were disturbed using intra± 6 % and inter-operator± 15 % variations on the segmentation threshold.FFR CT was evaluated in each case by 3D-OD CFD simulations. The findings suggest that the sensitivity ofFFR CT to this type of uncertainty increases distally and with the stenosis severity. Cases with moderate or severe distal coronary lesions should undergo either exact and thorough segmentation operations or invasive FFR measurements, particularly if theFFR CT is close to the cutoff (0.80). Therefore, we conclude that it is crucial to consider the lesion's location and degree of severity when evaluatingFFR CT results.

Impact of TAVR on coronary artery hemodynamics using clinical measurements and image-based patient-specific in silico modeling

In recent years, transcatheter aortic valve replacement (TAVR) has become the leading method for treating aortic stenosis. While the procedure has improved dramatically in the past decade, there are still uncertainties about the impact of TAVR on coronary blood flow. Recent research has indicated that negative coronary events after TAVR may be partially driven by impaired coronary blood flow dynamics. Furthermore, the current technologies to rapidly obtain non-invasive coronary blood flow data are relatively limited. Herein, we present a lumped parameter computational model to simulate coronary blood flow in the main arteries as well as a series of cardiovascular hemodynamic metrics. The model was designed to only use a few inputs parameters from echocardiography, computed tomography and a sphygmomanometer. The novel computational model was then validated and applied to 19 patients undergoing TAVR to examine the impact of the procedure on coronary blood flow in the left anterior descending (LAD) artery, left circumflex (LCX) artery and right coronary artery (RCA) and various global hemodynamics metrics. Based on our findings, the changes in coronary blood flow after TAVR varied and were subject specific (37% had increased flow in all three coronary arteries, 32% had decreased flow in all coronary arteries, and 31% had both increased and decreased flow in different coronary arteries). Additionally, valvular pressure gradient, left ventricle (LV) workload and maximum LV pressure decreased by 61.5%, 4.5% and 13.0% respectively, while mean arterial pressure and cardiac output increased by 6.9% and 9.9% after TAVR. By applying this proof-of-concept computational model, a series of hemodynamic metrics were generated non-invasively which can help to better understand the individual relationships between TAVR and mean and peak coronary flow rates. In the future, tools such as these may play a vital role by providing clinicians with rapid insight into various cardiac and coronary metrics, rendering the planning for TAVR and other cardiovascular procedures more personalized.

Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning

Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.

Long-term prognostic impact of paravalvular leakage on coronary artery disease requires patient-specific quantification of hemodynamics

Transcatheter aortic valve replacement (TAVR) is a frequently used minimally invasive intervention for patient with aortic stenosis across a broad risk spectrum. While coronary artery disease (CAD) is present in approximately half of TAVR candidates, correlation of post-TAVR complications such as paravalvular leakage (PVL) or misalignment with CAD are not fully understood. For this purpose, we developed a multiscale computational framework based on a patient-specific lumped-parameter algorithm and a 3-D strongly-coupled fluid-structure interaction model to quantify metrics of global circulatory function, metrics of global cardiac function and local cardiac fluid dynamics in 6 patients. Based on our findings, PVL limits the benefits of TAVR and restricts coronary perfusion due to the lack of sufficient coronary blood flow during diastole phase (e.g., maximum coronary flow rate reduced by 21.73%, 21.43% and 21.43% in the left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) respectively (N = 6)). Moreover, PVL may increase the LV load (e.g., LV load increased by 17.57% (N = 6)) and decrease the coronary wall shear stress (e.g., maximum wall shear stress reduced by 20.62%, 21.92%, 22.28% and 25.66% in the left main coronary artery (LMCA), left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) respectively (N = 6)), which could promote atherosclerosis development through loss of the physiological flow-oriented alignment of endothelial cells. This study demonstrated that a rigorously developed personalized image-based computational framework can provide vital insights into underlying mechanics of TAVR and CAD interactions and assist in treatment planning and patient risk stratification in patients.

In Vivo Intravascular Optical Coherence Tomography (IVOCT) Structural and Blood Flow Imaging Based Mechanical Simulation Analysis of a Blood Vessel

INTRODUCTION

Computer modelling of blood vessels based on biomedical imaging provides important hemodynamic and biomechanical information for vascular disease studies and diagnosis. However due to lacking well-defined physiological blood flow profiles, the accuracy of the simulation results is often not guaranteed. Flow velocity profiles of a specific cross section of a blood vessel were obtained by in vivo Doppler intravascular optical coherence tomography (IVOCT) lately. However due to the influence of the catheter, the velocity profile imaged by IVOCT can't be applied to simulation directly.

METHODS

A simulation-experiment combined method to determine the inlet flow boundary based on in vivo porcine carotid Doppler IVOCT imaging is proposed. A single conduit carotid model was created from the 3D IVOCT structural images using an image processing-computer aided design combined method.

RESULTS

With both high- resolution arterial model and near physiological blood flow profile, stress analysis by fluid-structure interaction and computational fluid dynamics were performed. The influence of the catheter to the wall shear stress, the hemodynamics and the stresses of the carotid wall under the measured inlet flow and various outlet pressure boundary conditions, are analyzed.

CONCLUSION

This study provides a solution to the difficulty of getting inlet flow boundary for numerical simulation of arteries. It paves the way for developing IVOCT based vascular stress analysis and imaging methods for the studies and diagnosis of vascular diseases.

Mathematical modeling of plaque progression and associated microenvironment: How far from predicting the fate of atherosclerosis?

Mathematical modeling contributes to pathophysiological research of atherosclerosis by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. In turn, the complexity of atherosclerosis is well suited to quantitative approaches as it provides challenges and opportunities for new developments of modeling. In this review, we summarize the current 'state of the art' on the mathematical modeling of the effects of biomechanical factors and microenvironmental factors on the plaque progression, and its potential help in prediction of plaque development. We begin with models that describe the biomechanical environment inside and outside the plaque and its influence on its growth and rupture. We then discuss mathematical models that describe the dynamic evolution of plaque microenvironmental factors, such as lipid deposition, inflammation, smooth muscle cells migration and intraplaque hemorrhage, followed by studies on plaque growth and progression using these modelling approaches. Moreover, we present several key questions for future research. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving plaque vulnerability and propose future potential direction in therapy for cardiovascular disease.

Numerical Study of the Unsteady Flow in Simplified and Realistic Iliac Bifurcation Models

Cardiovascular diseases are a major cause of death and disability worldwide and they are commonly associated with the occurrence of atherosclerotic plaque deposition in the vessel walls, a process denoted as atherosclerosis. This is a chronic and progressive inflammatory disease of large-/medium-sized blood vessels that affects blood flow profiles, with the abdominal aorta and its branches being one of the locations prone to the development of this pathology, due to their curvatures and bifurcations. In this regard, the effect of flow patterns was studied and compared for both a simplified three-dimensional model of aorta bifurcation on the iliac arteries and a realistic model of iliac bifurcation, which was constructed from a computational tomography medical image. The flow patterns were analyzed in terms of velocity and wall shear stress distribution, but a special focus was given to the size and location of the recirculation zone. The simulations were performed using the Computational Fluid Dynamics software, FLUENT, taking into account the cardiac cycle profile at the infrarenal aorta. The shear stress and the velocity distribution observed for both models indicated that higher shear stress occurred along the flow divider wall (inner wall) and low shear stress occurred along the outer walls. In addition, the results demonstrated that the wall shear stress profiles were deeply affected by the transient profile of the cardiac cycle, with the deceleration phase being the most critical phase to the occurrence of backflow.