Ex vivo biaxial load testing analysis of aortic biomechanics demonstrates variation in elastic energy distribution across the aortic zone zero

Aortic diameter is a poor predictor of aortic tissue failure metrics in patients with ascending aneurysms

OBJECTIVES

There is growing consensus that aortic diameter is a flawed predictor of aortic dissection risk. We hypothesized that aortic tissue metrics would be better predicted by clinical metrics other than aortic diameter. Our objectives were to (1) characterize circumferential aortic failure stress and stretch as a result of aortic size and patient demographics, and (2) identify the influence of bicuspid aortic valve on failure metrics.

METHODS

From February 2018 to January 2021, 136 aortic tissue samples were obtained from 86 adults undergoing elective ascending aorta repair. Uniaxial biomechanical testing to failure, defined as a full-thickness central tear, was performed to obtain tissue failure stress and failure stretch and compared with clinical data and preoperative computed tomography imaging. The relationships among aortic diameter, patient demographics, and failure metrics were assessed using random forest regression models.

RESULTS

Median failure stress was 1.46 (1.02-1.94) megapascals, and failure stretch was 1.36 (1.27-1.54). Regression models correlated moderately with failure stress (R2 = 0.557) and highly with failure stretch (R2 = 0.806). Failure stress decreased with increasing age, lower body mass index, thicker tissue, and tricuspid aortic valves, whereas failure stretch was most highly correlated with age. Aortic area-to-height index outperformed aortic diameter in all models.

CONCLUSIONS

Aneurysmal ascending aortic tissue failure metrics correlated with available clinical metrics. Greater tissue thickness, older age, and tricuspid aortic valve morphology outperformed aortic diameter, warranting further investigation into the role of a patient-specific multifactorial dissection risk assessment over aortic diameter as a sole marker of aortic tissue integrity.

Age is superior to aortopathy phenotype as a predictor of aortic mechanics in patients with bicuspid valve

OBJECTIVES

Bicuspid aortic valve (BAV) aortopathy is defined by 3 phenotypes-root, ascending, and diffuse-based on region of maximal aortic dilation. We sought to determine the association between aortic mechanical behavior and aortopathy phenotype versus other clinical variables.

METHODS

From August 1, 2016, to March 1, 2023, 375 aortic specimens were collected from 105 patients undergoing elective ascending aortic aneurysm repair for BAV aortopathy. Planar biaxial data (191 specimens) informed constitutive descriptors of the arterial wall that were combined with in vivo geometry and hemodynamics to predict stiffness, stress, and energy density under physiologic loads. Uniaxial testing (184 specimens) evaluated failure stretch and failure Cauchy stress. Boosting regression was implemented to model the association between clinical variables and mechanical metrics.

RESULTS

There were no significant differences in mechanical metrics between the root phenotype (N = 33, 31%) and ascending/diffuse phenotypes (N = 72, 69%). Biaxial testing demonstrated older age was associated with increased circumferential stiffness, decreased stress, and decreased energy density. On uniaxial testing, longitudinally versus circumferentially oriented specimens failed at significantly lower Cauchy stress (50th [15th, 85th percentiles]: 1.0 [0.7, 1.6] MPa vs 1.9 [1.3, 3.1] MPa; P < .001). Age was associated with decreased failure stretch and stress. Elongated ascending aortas were also associated with decreased failure stress.

CONCLUSIONS

Aortic mechanical function under physiologic and failure conditions in BAV aortopathy is robustly associated with age and poorly associated with aortopathy phenotype. Data suggesting that the root phenotype of BAV aortopathy portends worse outcomes are unlikely to be related to aberrant, phenotype-specific tissue mechanics.

Thoracic Aortic Aneurysm Risk Assessment: A Machine Learning Approach

Background

Traditional methods of risk assessment for thoracic aortic aneurysm (TAA) based on aneurysm size alone have been called into question as being unreliable in predicting complications. Biomechanical function of aortic tissue may be a better predictor of risk, but it is difficult to determine in vivo.

Objectives

This study investigates using a machine learning (ML) model as a correlative measure of energy loss, a measure of TAA biomechanical function.

Methods

Biaxial tensile testing was performed on resected TAA tissue collected from patients undergoing surgery. The energy loss of the tissue was calculated and used as the representative output. Input parameters were collected from clinical assessments including observations from medical scans and genetic paneling. Four ML algorithms including Gaussian process regression were trained in Matlab.

Results

A total of 158 patients were considered (mean age 62 years, range 22-89 years, 78% male), including 11 healthy controls. The mean ascending aortic diameter was 47 ± 10 mm, with 46% having a bicuspid aortic valve. The best-performing model was found to give a greater correlative measure to energy loss (R2 = 0.63) than the surprisingly poor performance of aortic diameter (R2 = 0.26) and indexed aortic size (R2 = 0.32). An echocardiogram-derived stiffness metric was investigated on a smaller subcohort of 67 patients as an additional input, improving the correlative performance from R2 = 0.46 to R2 = 0.62.

Conclusions

A preliminary set of models demonstrated the ability of a ML algorithm to improve prediction of the mechanical function of TAA tissue. This model can use clinical data to provide additional information for risk stratification.

Longitudinal versus circumferential biomechanical behavior of the aneurysmal ascending aorta

OBJECTIVES

We evaluate the independent effects of patient and aortic tissue characteristics on biaxial physiologic mechanical metrics in aneurysmal and nonaneurysmal tissues, and uniaxial failure metrics in aneurysmal tissue, comparing longitudinal and circumferential behavior.

METHODS

From February 2017 to October 2022, 382 aortic specimens were collected from 134 patients; 268 specimens underwent biaxial testing, and 114 specimens underwent uniaxial testing. Biaxial testing evaluated Green-Lagrange transition strain and low and high tangent moduli. Uniaxial testing evaluated failure stretch, Cauchy stress, and low and high tangent moduli. Longitudinal gradient boosting models were implemented to estimate mechanical metrics and covariates of importance.

RESULTS

On biaxial testing, nonaneurysmal tissue was less deformable and exhibited a lower transition strain than aneurysmal tissue in the longitudinal (0.18 vs 0.30, P < .001) and circumferential (0.25 vs 0.30, P = .01) directions. Older age and increasing ascending aortic length contributed most to predicting transition strain. On uniaxial testing, longitudinal specimens failed at lower stretch (1.4 vs 1.5, P = .003) and Cauchy stress (1.0 vs 1.9 kPa, P < .001) than circumferential specimens. Failure stretch and Cauchy stress were most strongly associated with tissue orientation and decreased sharply with older age. Age, ascending aortic length, and tissue thickness were the most frequent covariates predicting mechanical metrics across 10 prediction models.

CONCLUSIONS

Age was the strongest predictor of mechanical behavior. After adjusting for age, nonaneurysmal tissue was less deformable than aneurysmal tissue. Differences in longitudinal and circumferential mechanics contribute to tissue dysfunction and failure in ascending aneurysms. This highlights the need to better understand the effects of age, ascending aortic length, and thickness on clinical aortic behavior.

Lengthwise regional mechanics of the human aneurysmal ascending thoracic aorta

The prognosis of patients undergoing emergency endovascular repair of ascending thoracic aortic aneurysm (ATAA) depends on defect location, with root disease bearing worse outcomes than proximal or distal aortopathy. We speculate that a spatial gradient in aneurysmal tissue mechanics through the length of the ascending thoracic aorta may fuel noted survival discrepancies. To this end, we performed planar biaxial testing on 153 root, proximal, and distal segments of ATAA samples collected from 80 patients receiving elective open surgical repair. Following data averaging via surface fitting-based interpolation of strain-controlled protocols, we combined in-vitro and in-vivo measurements of loads and geometry to resolve inflation-extension kinematics and evaluate mechanical metrics of stress, stiffness, and energy at consistent deformation levels. Representative (averaged) experimental data and simulated in-vivo conditions revealed significantly larger biaxial stiffness at the root compared to either proximal or distal tissues, which persisted as the entire aorta stiffened during aging. Advancing age further reduced biaxial stretch and energy storage, a measure of aortic function, across all ATAA segments. Importantly, age emerged as a stronger predictor of tissue mechanics in ATAA disease than either bicuspid aortic valve or connective tissue disorders. Besides strengthening the general understanding of aneurysmal disease, our findings provide specifications to customize the design of stent-grafts for the treatment of ATAA disease. Optimization of deployment and interaction of novel endovascular devices with the local native environment is expected to carry significant potential for improving clinical outcomes. STATEMENT OF SIGNIFICANCE: Elucidating the lengthwise regional mechanics of ascending thoracic aortic aneurysms (ATAAs) is critical for the design of endovascular devices tailored to the ascending aorta. Stent-grafts provide a less invasive alternative to support the long-term survival of ATAA patients ineligible for open surgical repair. In this study, we developed a numerical framework that combines semi-inverse constitutive and forward modeling with in-vitro and in-vivo data to extract mechanical descriptors of ATAA tissue behavior at physiologically meaningful deformation. Moving distally from the aortic root to the first ascending aortic branch, we observed a progressive decline in biaxial stiffness. Furthermore, we showed that aging leads to reduced aortic function and is a stronger predictor of mechanics than either valve morphology or underlying syndromic disorder.

Clinical implications of the biomechanics of bicuspid aortic valve and bicuspid aortopathy

Bicuspid aortic valve (BAV), which affects up to 2% of the general population, results from the abnormal fusion of the cusps of the aortic valve. Patients with BAV are at a higher risk for developing aortic dilatation, a condition known as bicuspid aortopathy, which is associated with potentially life-threatening sequelae such as aortic dissection and aortic rupture. Although BAV biomechanics have been shown to contribute to aortopathy, their precise impact is yet to be delineated. Herein, we present the latest literature related to BAV biomechanics. We present the most recent definitions and classifications for BAV. We also summarize the current evidence pertaining to the mechanisms that drive bicuspid aortopathy. We highlight how aberrant flow patterns can contribute to the development of aortic dilatation. Finally, we discuss the role cardiac magnetic resonance imaging can have in assessing and managing patient with BAV and bicuspid aortopathy.