The predictive capability of aortic stiffness index for aortic dissection among dilated ascending aortas.

OBJECTIVE: We created a finite element model to predict the probability of dissection based on imaging-derived aortic stiffness and investigated the link between stiffness and wall tensile stress using our model. METHODS: Transthoracic echocardiogram measurements were used to calculate aortic diameter change over the cardiac cycle. Aortic stiffness index was subsequently calculated based on diameter change and blood pressure. A series of logistic models were developed to predict the binary outcome of aortic dissection using 1 or more series of predictor parameters such as aortic stiffness index or patient characteristics. Finite element analysis was performed on a subset of diameter-matched patients exhibiting patient-specific material properties. RESULTS: Transthoracic echocardiogram scans of patients with type A aortic dissection (n = 22) exhibited elevated baseline aortic stiffness index when compared with aneurysmal patients' scans with tricuspid aortic valve (n = 83, P < .001) and bicuspid aortic valve (n = 80, P < .001). Aortic stiffness index proved an excellent discriminator for a future dissection event (area under the curve, 0.9337, odds ratio, 2.896). From the parametric finite element study, we found a correlation between peak longitudinal wall tensile stress and stiffness index (ρ = .6268, P < .001, n = 28 pooled). CONCLUSIONS: Noninvasive transthoracic echocardiogram-derived aortic stiffness measurements may serve as an impactful metric toward predicting aortic dissection or quantifying dissection risk. A correlation between longitudinal stress and stiffness establishes an evidence-based link between a noninvasive stiffness parameter and stress state of the aorta with clinically apparent dissection events.

Aortic Dissection is Determined by Specific Shape and Hemodynamic Interactions.

The aim of this study was to determine whether specific three-dimensional aortic shape features, extracted via statistical shape analysis (SSA), correlate with the development of thoracic ascending aortic dissection (TAAD) risk and associated aortic hemodynamics. Thirty-one patients followed prospectively with ascending thoracic aortic aneurysm (ATAA), who either did (12 patients) or did not (19 patients) develop TAAD, were included in the study, with aortic arch geometries extracted from computed tomographic angiography (CTA) imaging. Arch geometries were analyzed with SSA, and unsupervised and supervised (linked to dissection outcome) shape features were extracted with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), respectively. We determined PLS-DA to be effective at separating dissection and no-dissection patients ([Formula: see text]), with decreased tortuosity and more equal ascending and descending aortic diameters associated with higher dissection risk. In contrast, neither PCA nor traditional morphometric parameters (maximum diameter, tortuosity, or arch volume) were effective at separating dissection and no-dissection patients. The arch shapes associated with higher dissection probability were supported with hemodynamic insight. Computational fluid dynamics (CFD) simulations revealed a correlation between the PLS-DA shape features and wall shear stress (WSS), with higher maximum WSS in the ascending aorta associated with increased risk of dissection occurrence. Our work highlights the potential importance of incorporating higher dimensional geometric assessment of aortic arch anatomy in TAAD risk assessment, and in considering the interdependent influences of arch shape and hemodynamics as mechanistic contributors to TAAD occurrence.

Computational modeling of the strength of the ascending thoracic aortic media tissue under physiologic biaxial loading conditions.

Type A Aortic Dissection (TAAD) is a life-threatening condition involving delamination of ascending aortic media layers. While current clinical guidelines recommend surgical intervention for aneurysm diameter > 5.5 cm, high incidence of TAAD in patients below this diameter threshold indicates the pressing need for improved evidence-based risk prediction metrics. Construction of such metrics will require the knowledge of the biomechanical failure properties of the aortic wall tissue under biaxial loading conditions. We utilized a fiber-level finite element based structural model of the aortic tissue to quantify the relationship between aortic tissue strength and physiologically relevant biaxial stress state for nonaneurysmal and aneurysmal patient cohorts with tricuspid aortic valve phenotype. We found that the model predicted strength of the aortic tissue under physiologic biaxial loading conditions depends on the stress biaxiality ratio, defined by the ratio of the longitudinal and circumferential components of the tissue stress. We determined that predicted biaxial tissue strength is statistically similar to its uniaxial circumferential strength below biaxiality ratios of 0.68 and 0.69 for nonaneurysmal and aneurysmal cohorts, respectively. Beyond this biaxiality ratio, predicted biaxial strength for both cohorts reduced drastically to a magnitude statistically similar to its longitudinal strength. We identified fiber-level failure mechanisms operative under biaxial stress state governing aforementioned tissue failure behavior. These findings are an important first step towards the development of mechanism-based TAAD risk assessment metrics for early identification of high-risk patients.

Effect of localized tendon remodeling on supraspinatus tear propagation.

Rotator cuff tear propagation is multifactorial and may be due to localized changes in mechanical properties from tendon remodeling based on the inhomogeneous stresses experienced by a tendon with a tear. The objective of this study was to investigate the effect of localized tendon remodeling on tear propagation for simulated supraspinatus tendon tears. A validated computational model of a supraspinatus tendon using subject-specific geometry and material properties with a 1 cm wide anterior tear was used. The medial edge of the supraspinatus tendon was displaced 5 mm to induce tear propagation and cohesive elements were used to model tear propagation. Four remodeling scenarios were investigated: (1) Baseline (no remodeling), (2) Positive remodeling (increased fiber stiffness) and (3) Negative remodeling (decreased fiber stiffness) at tear tips, and (4) Negative remodeling along the medial-lateral tear edge. Output parameters included the amount of tear propagation, critical load to propagate the tear, and maximum principal stress at the tear tips. Positive remodeling at the tear tips resulted in the largest amount of tear propagation (18.4 mm), highest peak maximum principal stress (25.2 MPa), and lowest critical load to propagate the tear (249N). Conversely, negative remodeling at the tear tips resulted in the least amount of tear propagation (16 mm), lowest peak maximum principal stress (17.6 MPa) and highest critical load to propagate the tear (278N). Overall, remodeling at the tear tips has the greatest effect on tear propagation. Therefore, a better method for clinicians to measure tendon stiffness at the tear tips would be helpful to improve outcome of patients.

Computational modeling reveals the relationship between intrinsic failure properties and uniaxial biomechanical behavior of arterial tissue.

Biomechanical failure of the artery wall can lead to rupture, a catastrophic event with a high rate of mortality. Thus, there is a pressing need to understand failure behavior of the arterial wall. Uniaxial testing remains the most common experimental technique to assess tissue failure properties. However, the relationship between intrinsic failure parameters of the tissue and measured uniaxial failure properties is not fully established. Furthermore, the effect of the experimental variables, such as specimen shape and boundary conditions, on the measured failure properties is not well understood. We developed a finite element model capable of recapitulating pre-failure and post-failure uniaxial biomechanical response of the arterial tissue specimen. Intrinsic stiffness, strength and fracture toughness of the vessel wall tissue were used as the input material parameters to the model. Two uniaxial testing protocols were considered: a conventional setup with a rectangular specimen held at the grips by cardboard inserts, and the other used a dogbone specimen with soft foam inserts at the grips. Our computational study indicated negligible differences in the peak stress and post-peak mechanical behavior between these two testing protocols. It was also found that the tissue experienced only modest localized failure until higher levels of applied stretch beyond the peak stress. A robust cohesive model was capable of modeling the post-peak biomechanical response, which was primarily governed by tissue fracture toughness. Our results suggest that the post-peak region, in conjunction with the peak stress, must be considered to evaluate the complete biomechanical failure behavior of the soft tissue.

A structural finite element model for lamellar unit of aortic media indicates heterogeneous stress field after collagen recruitment.

Incorporation of collagen structural information into the study of biomechanical behavior of ascending thoracic aortic (ATA) wall tissue should provide better insight into the pathophysiology of ATA. Structurally motivated constitutive models that include fiber dispersion and recruitment can successfully capture overall mechanical response of the arterial wall tissue. However, these models cannot examine local microarchitectural features of the collagen network, such as the effect of fiber disruptions and interaction between fibrous and non-fibrous components, which may influence emergent biomechanical properties of the tissue. Motivated by this need, we developed a finite element based three-dimensional structural model of the lamellar units of the ATA media that directly incorporates the collagen fiber microarchitecture. The fiber architecture was computer generated utilizing network features, namely fiber orientation distribution, intersection density and areal concentration, obtained from image analysis of multiphoton microscopy images taken from human aneurysmal ascending thoracic aortic media specimens with bicuspid aortic valve (BAV) phenotype. Our model reproduces the typical J-shaped constitutive response of the aortic wall tissue. We found that the stress state in the non-fibrous matrix was homogeneous until the collagen fibers were recruited, but became highly heterogeneous after that event. The degree of heterogeneity was dependent upon local network architecture with high stresses observed near disrupted fibers. The magnitude of non-fibrous matrix stress at higher stretch levels was negatively correlated with local fiber density. The localized stress concentrations, elucidated by this model, may be a factor in the degenerative changes in aneurysmal ATA tissue.