Geometric surrogates of abdominal aortic aneurysm wall mechanics

Impact of Geometric Attributes on Abdominal Aortic Aneurysm Rupture Risk: An In Vivo FSI-Based Study

Reported in this paper is a cutting-edge computational investigation into the influence of geometric characteristics on abdominal aortic aneurysm (AAA) rupture risk, beyond the traditional measure of maximum aneurysm diameter. A Comprehensive fluid-structure interaction (FSI) analysis was employed to assess risk factors in a range of patient scenarios, with the use of three-dimensional (3D) AAA models reconstructed from patient-specific aortic data and finite element method. Wall shear stress (WSS), and its derivatives such as time-averaged WSS (TAWSS), oscillatory shear index (OSI), relative residence time (RRT) and transverse WSS (transWSS) offer insights into the force dynamics acting on the AAA wall. Emphasis is placed on these WSS-based metrics and seven key geometric indices. By correlating these geometric discrepancies with biomechanical phenomena, this study highlights the novel and profound impact of geometry on risk prediction. This study demonstrates the necessity of a multidimensional assessment approach, future efforts should complement these findings with experimental validations for an applicable approach for clinical use.

A review on the biomechanical behaviour of the aorta

Large aortic aneurysm and acute and chronic aortic dissection are pathologies of the aorta requiring surgery. Recent advances in medical intervention have improved patient outcomes; however, a clear understanding of the mechanisms leading to aortic failure and, hence, a better understanding of failure risk, is still missing. Biomechanical analysis of the aorta could provide insights into the development and progression of aortic abnormalities, giving clinicians a powerful tool in risk stratification. The complexity of the aortic system presents significant challenges for a biomechanical study and requires various approaches to analyse the aorta. To address this, here we present a holistic review of the biomechanical studies of the aorta by categorising articles into four broad approaches, namely theoretical, in vivo, experimental and combined investigations. Experimental studies that focus on identifying mechanical properties of the aortic tissue are also included. By reviewing the literature and discussing drawbacks, limitations and future challenges in each area, we hope to present a more complete picture of the state-of-the-art of aortic biomechanics to stimulate research on critical topics. Combining experimental modalities and computational approaches could lead to more comprehensive results in risk prediction for the aortic system.

The Ratio of the Size of the Abdominal Aortic Aneurysm to That of the Unchanged Aorta as a Risk Factor for Its Rupture

Background: A ruptured abdominal aortic aneurysm is a severe condition associated with high mortality. Currently, the most important criterion used to estimate the risk of its rupture is the size of the aneurysm, but due to patients’ anatomical variability, many aneurysms have a high risk of rupture with a small aneurysm size. We asked ourselves whether individual differences in anatomy could be taken into account when assessing the risk of rupture. Methods: Based on the CT scan image, aneurysm and normal aorta diameters were collected from 186 individuals and compared in patients with ruptured and unruptured aneurysms. To take into account anatomical differences between patients, diameter ratios were calculated by dividing the aneurysm diameter by the diameter of the normal aorta at various heights, and then further comparisons were made. Results: It was found that the calculated ratios differ between patients with ruptured and unruptured aneurysms. This observation is also present in patients with small aneurysms, with its maximal size below the level that indicates the need for surgical treatment. For small aneurysms, the ratios help us to estimate the risk of rupture better than the maximum sac size (AUC: 0.783 vs. 0.650). Conclusions: The calculated ratios appear to be a valuable feature to indicate which of the small aneurysms have a high risk of rupture. The obtained results suggest the need for further confirmation of their usefulness in subsequent groups of patients.

Prediction of abdominal aortic aneurysm growth by artificial intelligence taking into account clinical, biologic, morphologic, and biomechanical variables

OBJECTIVES

To develop a prediction model that could risk stratify abdominal aortic aneurysms (AAAs) into high and low growth rate groups, using machine learning algorithms based on variables from different pathophysiological fields.

METHODS

A cohort of 40 patients with small AAAs (maximum diameter 32-53 mm) who had at least an initial and a follow-up CT scan (median follow-up 12 months, range 3-36 months) were included. 29 input variables from clinical, biological, morphometric, and biomechanical pathophysiological aspects extracted for predictive modeling. Collected data were used to build two supervised machine learning models. A gradient boosting (XGboost) and a support vector machines (SVM) algorithm were trained with 60% and tested with 40% of the data to predict which AAA would achieve a growth rate higher than the median of our study cohort. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used for the evaluation of the developed algorithms.

RESULTS

XGboost achieved the highest AUC in predicting high compared to low AAA growth rate with an AUC of 81.2% (95% CI from 61.1 to 100%). SVM achieved the second highest performance with an AUC of 68.8% (95% CI from 46.5 to 91%). Based on the best performing algorithm, variable importance was estimated. Diameter-diameter ratio (maximum diameter/neck diameter), Tortuosity from Renal arteries to aortic bifurcation, and maximum thickness of the intraluminal thrombus were found to be the most important factors for model predictions. Other factors were also found to play a significant but less important role.

CONCLUSIONS

A prediction model that can risk stratify AAAs into high and low growth rate groups could be developed by analyzing several factors implicated in the multifactorial pathophysiology of this disease, with the use of machine learning algorithms. Future studies including larger patient cohorts and implementing additional risk markers may aid in the establishment of such methodology during AAA rupture risk estimation.

Combined Curvature and Wall Shear Stress Analysis of Abdominal Aortic Aneurysm: An Analysis of Rupture Risk Factors

PURPOSE

To discuss the risk factors for abdominal aortic aneurysm rupture based on geometric and hemodynamic parameters.

METHODS

We retrospectively reviewed the clinical data of those who were diagnosed with an abdominal aortic aneurysm by computed tomography angiography at our hospital between October 2019 and December 2020. Thirty-five patients were included in the ruptured group (13 patients) and the unruptured group (22 patients). We analyzed the differences and correlations of anatomical factors and hemodynamic parameters between the two groups using computational fluid dynamics based on computed tomography angiography.

RESULTS

There were significant differences in the maximum diameter [(79.847 ± 10.067) mm vs. (52.320 ± 14.682) mm, P < 0.001], curvature [(0.139 ± 0.050) vs. 0.080 (0.123 - 0.068), P = 0.021], and wall shear stress at the site of maximal blood flow impact [0.549(0.839 - 0.492) Pa vs. (1.378 ± 0.255) Pa, P < 0.001] between the ruptured and unruptured groups, respectively. And in the ruptured group, wall shear stress at the rupture site was significantly different from that at the site of maximal blood flow impact [0.025 (0.049 - 0.018) Pa vs. 0.549 (0.839 - 0.492) Pa, P = 0.001]. Then, the maximum diameter and curvature were associated with rupture (maximum diameter: OR: 1.095, P = 0.003; curvature: OR: 1.142E + 10, P = 0.012). Most importantly, curvature is negatively correlated with wall shear stress (r = - 0.366, P = 0.033).

CONCLUSIONS

Both curvature and wall shear stress can evaluate the rupture risk of aneurysm. Also, curvature can be used as the geometric substitution of wall shear stress.

A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model

Rupture risk assessment of abdominal aortic aneurysms (AAAs) by means of quantifying wall stress is a common biomechanical strategy. However, the clinical translation of this approach has been greatly limited due to the complexity associated with the computational tools required for its implementation. Thus, being able to estimate wall stress using nonbiomechanical markers that can be quantified as a direct outcome of clinical image segmentation would be advantageous in improving the potential implementation of said strategy. In the present work, we investigated the use of geometric indices to predict patient-specific AAA wall stress by means of a novel neural network (NN) modeling approach. We conducted a retrospective review of existing clinical images of two patient groups: 98 asymptomatic and 50 symptomatic AAAs. The images were subject to a protocol consisting of image segmentation, processing, volume meshing, finite element modeling, and geometry quantification, from which 53 geometric indices and the spatially averaged wall stress (SAWS) were calculated. SAWS estimated from finite element analysis was considered the gold standard for the predictions. We developed feed-forward NN models composed of an input layer, two dense layers, and an output layer using Keras, a deep learning library in python. The NN models were trained, tested, and validated independently for both AAA groups using all geometric indices, as well as a reduced set of indices resulting from a variable reduction procedure. We compared the performance of the NN models with two standard machine learning algorithms (MARS: multivariate adaptive regression splines and GAM: generalized additive model) and a linear regression model (GLM: generalized linear model). With the reduced sets of indices, the NN-based approach exhibited the highest mean goodness-of-fit (for the symptomatic group 0.71 and for the asymptomatic group 0.79) and lowest mean relative error (17% for both groups). In contrast, MARS yielded a mean goodness-of-fit of 0.59 for the symptomatic group and 0.77 for the asymptomatic group, with relative errors of 17% for the symptomatic group and 22% for the asymptomatic group. GAM had a mean goodness-of-fit of 0.70 for the symptomatic group and 0.80 for the asymptomatic group, with relative errors of 16% for the symptomatic group and 20% for the asymptomatic group. GLM did not perform as well as the other algorithms, with a mean goodness-of-fit of 0.53 for the symptomatic group and 0.70 for the asymptomatic group, with relative errors of 19% for the symptomatic group and 23% for the asymptomatic group. Nevertheless, the NN models required a reduced set of 15 and 13 geometric indices to predict SAWS for the symptomatic and asymptomatic AAA groups, respectively. This was in contrast to the reduced set of nine and eight geometric indices required to predict SAWS with the MARS and GAM algorithms for each AAA group, respectively. The use of NN modeling represents a promising alternative methodology for the estimation of AAA wall stress using geometric indices as surrogates, in lieu of finite element modeling. The performance metrics of NN models are expected to improve with significantly larger group sizes, given the suitability of NN modeling for "big data" applications.

The Association Between Curvature and Rupture in a Murine Model of Abdominal Aortic Aneurysm and Dissection

BACKGROUND

Mouse models of abdominal aortic aneurysm (AAA) and dissection have proven to be invaluable in the advancement of diagnostics and therapeutics by providing a platform to decipher response variables that are elusive in human populations. One such model involves systemic Angiotensin II (Ang-II) infusion into low density-lipoprotein receptor-deficient (LDLr-/-) mice leading to intramural thrombus formation, inflammation, matrix degradation, dilation, and dissection. Despite its effectiveness, considerable experimental variability has been observed in AAAs taken from our Ang-II infused LDLr-/- mice (n=12) with obvious dissection occurring in 3 samples, outer bulge radii ranging from 0.73 to 2.12 mm, burst pressures ranging from 155 to 540 mmHg, and rupture location occurring 0.05 to 2.53 mm from the peak bulge location.

OBJECTIVE

We hypothesized that surface curvature, a fundamental measure of shape, could serve as a useful predictor of AAA failure at supra-physiological inflation pressures.

METHODS

To test this hypothesis, we fit well-known biquadratic surface patches to 360° micro-mechanical test data and used Spearman's rank correlation (rho) to identify relationships between failure metrics and curvature indices.

RESULTS

We found the strongest associations between burst pressure and the maximum value of the first principal curvature (rho=-0.591, p-val=0.061), the maximum value of Mean curvature (rho=-0.545, p-val=0.087), and local values of Mean curvature at the burst location (rho=-0.864, p-val=0.001) with only the latter significant after Bonferroni correction. Additionally, the surface profile at failure was predominantly convex and hyperbolic (saddle-shaped) as indicated by a negative sign in the Gaussian curvature. Findings reiterate the importance of shape in experimental models of AAA.

Morphologic Features of Symptomatic and Ruptured Abdominal Aortic Aneurysm in Asian Patients

BACKGROUND

To evaluate morphologic features of symptomatic and ruptured abdominal aortic aneurysms in Asian patients.

METHODS

Two hundred sixty four continuous candidates with an abdominal aortic aneurysm (AAA) were retrospectively identified from a tertiary hospital database between January 2017 and May 2019. The patients meeting inclusion criteria were divided into symptomatic or ruptured AAA (srAAA) and asymptomatic AAA (asAAA) groups. Their computed tomography angiographies were reconstructed using centerline technique and the geometric features of AAAs between the 2 groups were compared.

RESULTS

One hundred two patients fulfilled selection criteria (mean age 71 years, 80 men), comprising 35 srAAAs and 67 asAAAs. There was no essential association between gender, smoking or hypertension, and AAA-associated symptoms or rupture. The maximum diameter (5.8 ± 1.4 cm vs. 5.0 ± 0.9 cm; P = 0.001), length (8.8 ± 0.6 cm vs. 7.0 ± 0.3 cm; P = 0.002), and intraluminal thrombus (ILT) thickness (1.7 ± 0.2 cm vs. 1.3 ± 0.1 cm; P = 0.039) of AAAs were independent risk factors for AAA-associated symptoms or rupture (binary logistic regression, P < 0.05), but AAA length and ILT were strongly correlated with the AAA diameter (Pearson correlation coefficient value of 0.591 and 0.444) whereas other factors such as aneurysmal tortuosity, aneurysmal neck anatomy, or common iliac artery geometry were nonsignificant.

CONCLUSIONS

AAA diameter, length, and intraluminal thrombus thickness were identified as risk factors for AAA-associated symptoms in Asian patients. While the diameter is regarded as the most important predictor for symptoms and rupture, AAA length and ILT thickness should also be taken into consideration when contemplating intervention, particularly for borderline and smaller aneurysms.

Predictors of Abdominal Aortic Aneurysm Risks

Computational biomechanics via finite element analysis (FEA) has long promised a means of assessing patient-specific abdominal aortic aneurysm (AAA) rupture risk with greater efficacy than current clinically used size-based criteria. The pursuit stems from the notion that AAA rupture occurs when wall stress exceeds wall strength. Quantification of peak (maximum) wall stress (PWS) has been at the cornerstone of this research, with numerous studies having demonstrated that PWS better differentiates ruptured AAAs from non-ruptured AAAs. In contrast to wall stress models, which have become progressively more sophisticated, there has been relatively little progress in estimating patient-specific wall strength. This is because wall strength cannot be inferred non-invasively, and measurements from excised patient tissues show a large spectrum of wall strength values. In this review, we highlight studies that investigated the relationship between biomechanics and AAA rupture risk. We conclude that combining wall stress and wall strength approximations should provide better estimations of AAA rupture risk. However, before personalized biomechanical AAA risk assessment can become a reality, better methods for estimating patient-specific wall properties or surrogate markers of aortic wall degradation are needed. Artificial intelligence methods can be key in stratifying patients, leading to personalized AAA risk assessment.

A Comparative Study of Biomechanical and Geometrical Attributes of Abdominal Aortic Aneurysms in the Asian and Caucasian Populations

In this work, we provide a quantitative assessment of the biomechanical and geometric features that characterize abdominal aortic aneurysm (AAA) models generated from 19 Asian and 19 Caucasian diameter-matched AAA patients. 3D patient-specific finite element models were generated and used to compute peak wall stress (PWS), 99th percentile wall stress (99th WS), and spatially averaged wall stress (AWS) for each AAA. In addition, 51 global geometric indices were calculated, which quantify the wall thickness, shape, and curvature of each AAA. The indices were correlated with 99th WS (the only biomechanical metric that exhibited significant association with geometric indices) using Spearman's correlation and subsequently with multivariate linear regression using backward elimination. For the Asian AAA group, 99th WS was highly correlated (R2 = 0.77) with three geometric indices, namely tortuosity, intraluminal thrombus volume, and area-averaged Gaussian curvature. Similarly, 99th WS in the Caucasian AAA group was highly correlated (R2 = 0.87) with six geometric indices, namely maximum AAA diameter, distal neck diameter, diameter-height ratio, minimum wall thickness variance, mode of the wall thickness variance, and area-averaged Gaussian curvature. Significant differences were found between the two groups for ten geometric indices; however, no differences were found for any of their respective biomechanical attributes. Assuming maximum AAA diameter as the most predictive metric for wall stress was found to be imprecise: 24% and 28% accuracy for the Asian and Caucasian groups, respectively. This investigation reveals that geometric indices other than maximum AAA diameter can serve as predictors of wall stress, and potentially for assessment of aneurysm rupture risk, in the Asian and Caucasian AAA populations.

A canonical correlation analysis of the relationship between clinical attributes and patient-specific hemodynamic indices in adult pulmonary hypertension

Pulmonary hypertension (PH) is a progressive disease affecting approximately 10-52 cases per million, with a higher incidence in women, and with a high mortality associated with right ventricle (RV) failure. In this work, we explore the relationship between hemodynamic indices, calculated from in silico models of the pulmonary circulation, and clinical attributes of RV workload and pathological traits. Thirty-four patient-specific pulmonary arterial tree geometries were reconstructed from computed tomography angiography images and used for volume meshing for subsequent computational fluid dynamics (CFD) simulations. Data obtained from the CFD simulations were post-processed resulting in hemodynamic indices representative of the blood flow dynamics. A retrospective review of medical records was performed to collect the clinical variables measured or calculated from standard hospital examinations. Statistical analyses and canonical correlation analysis (CCA) were performed for the clinical variables and hemodynamic indices. Systolic pulmonary artery pressure (sPAP), diastolic pulmonary artery pressure (dPAP), cardiac output (CO), and stroke volume (SV) were moderately correlated with spatially averaged wall shear stress (0.60 ≤ R² ≤ 0.66; p < 0.05). Similarly, the CCA revealed a linear and strong relationship (ρ = 0.87; p << 0.001) between 5 clinical variables and 2 hemodynamic indices. To this end, in silico models of PH blood flow dynamics have a high potential for predicting the relevant clinical attributes of PH if analyzed in a group-wise manner using CCA.

A machine leaning approach for abdominal aortic aneurysm severity assessment using geometric, biomechanical, and patient-specific historical clinical features

Recent studies monitoring severity of abdominal aortic aneurysm (AAA) suggested that reliance on only the maximum transverse diameter ( D _max ) may be insufficient to predict AAA rupture risk. Moreover, geometric indices, biomechanical parameters, material properties, and patient-specific historical data affect AAA morphology, indicating the need for an integrative approach that incorporates all factors for more accurate estimation of AAA severity. We implemented a machine learning algorithm using 45 features extracted from 66 patients. The model was generated using the J48 decision tree algorithm with the aim of maximizing model accuracy. Three different feature sets were used to assess the prediction rate: i) using D _max as a single-feature set, ii) using a set of all features, and, lastly iii) using a feature set selected via the BestFirst feature selection algorithm. Our results indicate that BestFirst feature selection yielded the highest prediction accuracy. These results indicate that a combination of several specific parameters that comprehensively capture AAA behavior may enable a suitable assessment of AAA severity, suggesting the potential benefit of machine learning for this application.

(PEAK) WALL STRESS AS AN INDICATOR OF ABDOMINAL AORTIC ANEURYSM SEVERITY

Abdominal aortic aneurysms, which consist of dilatations of the infra-renal aorta by at least 1.5 times of its normal diameter, are becoming a leading cause of death worldwide. Rupture often occurs unexpectedly, before a repair procedure is conducted. The AAA maximum diameter has been used as a clinical criterion to monitor AAA severity. However, assessment of AAA rupture risk requires knowledge of wall stress and wall strength at the potential rupture location. We conducted a study on 37 patient specific CT datasets to investigate the benefits of using peak wall stress instead of D_max for AAA rupture severity. Correlation between PWS and 24 geometric indices and biomechanical factors was studied where eleven of them showed a statistically significant correlation with PWS. A Finite Element Analysis Rupture Index was used to conclude that the use of D _max as a single predictor of AAA behavior and severity may be insufficient based on our patient population with a D_max smaller than the 5.5 cm, clinically recommended repair threshold.