Patient-specific computer models have been developed representing a variety of aspects of the cardiovascular system spanning the disciplines of electrophysiology, electromechanics, solid mechanics, and fluid dynamics. These physiological mechanistic models predict macroscopic phenomena such as electrical impulse propagation and contraction throughout the entire heart as well as flow and pressure dynamics occurring in the ventricular chambers, aorta, and coronary arteries during each heartbeat. Such models have been used to study a variety of clinical scenarios including aortic aneurysms, coronary stenosis, cardiac valvular disease, left ventricular assist devices, cardiac resynchronization therapy, ablation therapy, and risk stratification. After decades of research, these models are beginning to be incorporated into clinical practice directly via marketed devices and indirectly by improving our understanding of the underlying mechanisms of health and disease within a clinical context.

/pdf/patient-specific-cardiovascular-computational-modeling-1je0s3m4wu.pdf

Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges.

Computational fluid dynamics (CFD) provides a noninvasive method to functionally assess aortic hemodynamics. The thoracic aorta has an anatomically complex inlet comprising of the aortic valve and root, which is highly prone to different morphologies and pathologies. We investigated the effect of using patient-specific (PS) inflow velocity profiles compared to idealized profiles based on the patient's flow waveform. A healthy 31 yo with a normally functioning tricuspid aortic valve (subject A), and a 52 yo with a bicuspid aortic valve (BAV), aortic valvular stenosis, and dilated ascending aorta (subject B) were studied. Subjects underwent MR angiography to image and reconstruct three-dimensional (3D) geometric models of the thoracic aorta. Flow-magnetic resonance imaging (MRI) was acquired above the aortic valve and used to extract the patient-specific velocity profiles. Subject B's eccentric asymmetrical inflow profile led to highly complex velocity patterns, which were not replicated by the idealized velocity profiles. Despite having identical flow rates, the idealized inflow profiles displayed significantly different peak and radial velocities. Subject A's results showed some similarity between PS and parabolic inflow profiles; however, other parameters such as Flowasymmetry were significantly different. Idealized inflow velocity profiles significantly alter velocity patterns and produce inaccurate hemodynamic assessments in the thoracic aorta. The complex structure of the aortic valve and its predisposition to pathological change means the inflow into the thoracic aorta can be highly variable. CFD analysis of the thoracic aorta needs to utilize fully PS inflow boundary conditions in order to produce truly meaningful results.

Impact of Patient-Specific Inflow Velocity Profile on Hemodynamics of the Thoracic Aorta

Abdominal aortic aneurysm (AAA) is often accompanied by in traluminal thrombus (ILT), which complicates AAA progression and risk of rupture. Patient-specific computational fluid dynamics modeling of 10 small human AAA was performed to investigate relations between hemodynamics and ILT progression. The patients were imaged using magnetic resonance twice in a 2- to 3-yr interval. Wall content data were obtained by a planar T1-weighted fast spin echo black-blood scan, which enabled quantification of thrombus thickness at midaneurysm location during baseline and followup. Computational simulations with patient-specific geometry and boundary conditions were performed to quantify the hemodynamic parameters of time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and mean exposure time at baseline. Spatially resolved quantifications of the change in ILT thickness were compared with the different hemodynamic parameters. Regions of low OSI had the strongest correlation with ILT growth and demonstrated a statistically significant correlation coefficient. Prominent regions of high OSI (>0.4) and low TAWSS (<1 dyn/cm2) did not appear to coincide with locations of thrombus deposition.

https://www.physiology.org/doi/pdf/10.1152/ajpheart.00461.2014

A longitudinal comparison of hemodynamics and intraluminal thrombus deposition in abdominal aortic aneurysms

Aneurysms of the common iliac artery (CIAA) are typically found in association with an abdominal aortic aneurysm (AAA). Isolated CIAAs, in the absence of an AAA, are uncommon. Similar to AAAs, CIAA may develop intraluminal thrombus (ILT). As isolated CIAAs have a contralateral common iliac artery for comparison, they provide an opportunity to study the hemodynamic mechanisms behind ILT formation.

In this study, we compared a large isolated CIAA and the contralateral iliac artery using computational fluid dynamics to determine if hemodynamic metrics correlate with the location of ILT. We performed a comprehensive computational fluid dynamics study and investigated the residence time of platelets and monocytes, velocity fields, time-averaged wall shear stress, oscillatory shear index, and endothelial cell activation potential. We then correlated these data to ILT burden determined with computed tomography.

We found that high cell residence times, low time-averaged wall shear stress, high oscillatory shear index, and high endothelial cell activation potential all correlate with regions of ILT development. Our results show agreement with previous hypotheses of thrombus formation in AAA and provide insights into the computational hemodynamics of iliac artery aneurysms.

/pdf/a-comparison-of-hemodynamic-metrics-and-intraluminal-8pnz03on6k.pdf

A comparison of hemodynamic metrics and intraluminal thrombus burden in a common iliac artery aneurysm

Background

Aneurysm expansion rate is an important indicator of the potential risk of abdominal aortic aneurysm (AAA) rupture. Stress within the AAA wall is also thought to be a trigger for its rupture. However, the association between aneurysm wall stresses and expansion of AAA is unclear. 

Methods and Results

Forty-four patients with AAAs were included in this longitudinal follow-up study. They were assessed by serial abdominal ultrasonography and computed tomography scans if a critical size was reached or a rapid expansion occurred. Patient-specific 3-dimensional AAA geometries were reconstructed from the follow-up computed tomography images. Structural analysis was performed to calculate the wall stresses of the AAA models at both baseline and final visit. A nonlinear large-strain finite element method was used to compute the wall-stress distribution. The relationship between wall stresses and expansion rate was investigated. Slowly and rapidly expanding aneurysms had comparable baseline maximum diameters (median, 4.35 cm [interquartile range, 4.12 to 5.0 cm] versus 4.6 cm [interquartile range, 4.2 to 5.0 cm]; P=0.32). Rapidly expanding AAAs had significantly higher shoulder stresses than slowly expanding AAAs (median, 300 kPa [interquartile range, 280 to 320 kPa] versus 225 kPa [interquartile range, 211 to 249 kPa]; P=0.0001). A good correlation between shoulder stress at baseline and expansion rate was found (r=0.71; P=0.0001). 

Conclusion

A higher shoulder stress was found to have an association with a rapidly expanding AAA. Therefore, it may be useful for estimating the expansion of AAAs and improve risk stratification of patients with AAAs.

Association between aneurysm shoulder stress and abdominal aortic aneurysm expansion: A longitudinal follow-up study

In abdominal aortic aneurysm disease, the aortic wall is exposed to intense biological activity involving inflammation and matrix metalloproteinase-mediated degradation of the extracellular matrix. These processes are orchestrated by monocytes and rather than affecting the aorta uniformly, damage and weaken focal areas of the wall leaving it vulnerable to rupture. This study attempts to model numerically the deposition of monocytes using large eddy simulation, discrete phase modelling and near-wall particle residence time. The model was first applied to idealised aneurysms and then to three patient-specific lumen geometries using three-component inlet velocities derived from phase-contrast magnetic resonance imaging. The use of a novel, variable wall shear stress-limiter based on previous experimental data significantly improved the results. Simulations identified a critical diameter (1.8 times the inlet diameter) beyond which significant monocyte deposition is expected to occur. Monocyte adhesion occurred proximally in smaller abdominal aortic aneurysms and distally as the sac expands. The near-wall particle residence time observed in each of the patient-specific models was markedly different. Discrete hotspots of monocyte residence time were detected, suggesting that the monocyte infiltration responsible for the breakdown of the abdominal aortic aneurysm wall occurs heterogeneously. Peak monocyte residence time was found to increase with aneurysm sac size. Further work addressing certain limitations is needed in a larger cohort to determine clinical significance.

On the prediction of monocyte deposition in abdominal aortic aneurysms using computational fluid dynamics

The tumor microenvironment is abnormal and associated with tumor tissue hypoxia, immunosuppression, and poor response to treatment. One important abnormality present in tumors is vessel compression. Vessel decompression has been shown to increase survival rates in animal models via enhanced and more homogeneous oxygenation. However, our knowledge of the biophysical mechanisms linking tumor decompression to improved tumor oxygenation is limited. In this study, we propose a computational model to investigate the impact of vessel compression on red blood cell (RBC) dynamics in tumor vascular networks. Our results demonstrate that vessel compression can alter RBC partitioning at bifurcations in a hematocrit-dependent and flow rate–independent manner. We identify RBC focusing due to cross-streamline migration as the mechanism responsible and characterize the spatiotemporal recovery dynamics controlling downstream partitioning. Based on this knowledge, we formulate a reduced-order model that will help future research to elucidate how these effects propagate at a whole vascular network level. These findings contribute to the mechanistic understanding of hemodilution in tumor vascular networks and oxygen homogenization following pharmacological solid tumor decompression.

Compressed vessels bias red blood cell partitioning at bifurcations in a hematocrit-dependent manner: Implications in tumor blood flow.

3D imaging and computational modelling for estimation of wall stress in diseased arteries.

Muscle-on-chip devices aim to recapitulate the physiological characteristics of in vivo muscle tissue and so maintaining levels of oxygen transported to cells is essential for cell survival and for providing the normoxic conditions experienced in vivo. We use finite-element method numerical modelling to describe oxygen transport and reaction in a proposed three-dimensional muscle-on-chip bioreactor with embedded channels for muscle cells and growth medium. We determine the feasibility of ensuring adequate oxygen for muscle cell survival in a device sealed from external oxygen sources and perfused via medium channels. We investigate the effects of varying elements of the bioreactor design on oxygen transport to optimize muscle tissue yield and maintain normoxic conditions. Successful co-culturing of muscle cells with motor neurons can boost muscle tissue function and so we estimate the maximum density of seeded neurons supported by oxygen concentrations within the bioreactor. We show that an enclosed bioreactor can provide sufficient oxygen for muscle cell survival and growth. We define a more efficient arrangement of muscle and perfusion chambers that can sustain a predicted 50% increase in maximum muscle volume per perfusion vessel. A study of simulated bioreactors provides functions for predicting bioreactor designs with normoxic conditions for any size of perfusion vessel, muscle chamber and distance between chambers.

/pdf/mathematical-modelling-of-oxygen-transport-in-a-muscle-on-f21d1j5g.pdf

Mathematical modelling of oxygen transport in a muscle-on-chip device

[This corrects the article DOI: 10.1098/rsfs.2022.0020.][This corrects the article DOI: 10.1098/rsfs.2022.0020.].

Correction to ‘Mathematical modelling of oxygen transport in a muscle-on-chip device’ (2022) by Hardman et al.

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