A Cohort Longitudinal Study Identifies Morphology and Hemodynamics Predictors of Abdominal Aortic Aneurysm Growth

Preliminary establishment and validation of the inversion method for growth and remodeling parameters of patient-specific abdominal aortic aneurysm

Traditional medical imaging and biomechanical studies have challenges in analyzing the long-term evolution process of abdominal aortic aneurysm (AAA). The homogenized constrained mixture theory (HCMT) allows for quantitative analysis of the changes in the multidimensional morphology and composition of AAA. However, the accuracy of HCMT still requires further clinical verification. This study aims to establish a patient-specific AAA growth model based on HCMT, simulate the long-term growth and remodeling (G&R) process of AAA, and validate the feasibility and accuracy of the method using two additional AAA cases with five follow-up datasets. The media and adventitia layers of AAA were modeled as mixtures composed of elastin, collagen fibers, and smooth muscle cells (SMCs). The strain energy function was used to describe the continuous deposition and degradation effect of the mixture during the AAA evolution. Multiple sets of growth parameters were applied to finite element simulations, and the simulation results were compared with the follow-up data for gradually selecting the optimal growth parameters. Two additional AAA patients with different growth rates were used for validating this method, the optimal growth parameters were obtained using the first two follow-up imaging data, and the growth model was applied to simulate the subsequent four time points. The differences between the simulated diameters and the follow-up diameters of AAA were compared to validate the accuracy of the mechanistic model. The growth parameters, especially the stress-mediated substance deposition gain factor, are highly related to the AAA G&R process. When setting the optimal growth parameters to simulate AAA growth, the proportion of simulation results within the distance of less than 0.5 mm from the baseline models is above 80%. For the validating cases, the mean difference rates between the simulated diameter and the real-world diameter are within 2.5%, which basically meets the clinical demand for quantitatively predicting the AAA growth in maximum diameters. This study simulated the growth process of AAA, and validated the accuracy of this mechanistic model. This method was proved to be used to predict the G&R process of AAA caused by dynamic changes in the mixtures of the AAA vessel wall during long-term, assisting accurately and quantitatively predicting the multidimensional morphological development and mixtures evolution process of AAA in the clinic.

CTA-Based Radiomics and Area Change Rate Predict Infrarenal Abdominal Aortic Aneurysms Patients Events: A Multicenter Study

RATIONALE AND OBJECTIVES

Clinical assessment of abdominal aortic aneurysm (AAA) intervention and rupture risk relies primarily on maximum diameter, but studies have shown that sole dependence on diameter has limitations. CTA-based radiomics, aneurysm and lumen area change rates (AACR, LACR) are measured to predict potential AAA events.

MATERIALS AND METHODS

Between January 2017 and November 2022, 260 AAA patients from four centers who underwent two preoperative CTA examinations were included in this retrospective study. The endpoint event is defined as AAA rupture or repair. Patients were categorized into event and no-event groups based on the occurrence of endpoint event during follow-up. AACR and LACR were assessed using baseline and follow-up CTA, with radiomics features extracted from the baseline images. C-statistics and the Kaplan-Meier analysis were used to evaluate the predictive performance.

RESULTS

A total of 193 eligible infrarenal AAA patients were included, 176 (91.2%) were man and 17 (8.8%) were woman. The median follow-up was 33.4 (14.2, 57.4) months. Seven models were constructed, comprising the aneurysm-based Radscore model, lumen-based Radscore model, intraluminal thrombus (ILT)-based Radscore model, AACR model, LACR model, clinical model (including high-density lipoprotein, D-dimer, and baseline aneurysm diameter), and a merged model. On the external validation set, the C-index of seven models were 0.713 (0.574-0.853), 0.642 (0.499-0.786), 0.727 (0.600-0.854), 0.619 (0.484-0.753), 0.680 (0.530-0.830), 0.690 (0.557-0.824) and 0.760 (0.651-0.869), in that order. In the Kaplan-Meier analysis, the merged model was best-divided patients into high/low-risk groups with Log-rank p < 0.0001. The AARC and LARC between non-event and event groups have significant differences (AACR: 1.4 cm2/y vs. 2.3 cm2/y, p < 0.0001; LACR: 0.3 cm2/y vs. 1.1 cm2/y, p < 0.0001).

CONCLUSION

CTA-based radiomics, AACR and LACR have good predictive value for outcome event in infrarenal AAA patients.

Narrative Review of Risk Assessment of Abdominal Aortic Aneurysm Rupture Based on Biomechanics-Related Morphology

CLINICAL IMPACT

Studies have shown that the biomechanical indicators based on multi-scale models are more effective in accurately assessing the rupture risk of AAA. To meet the need for clinical monitoring and rapid decision making, the typical morphological parameters associated with AAA rupture and their relationships with the mechanical environment have been summarized, which provide a reference for clinical preoperative risk assessment of AAA.

Uncertainty quantification of the impact of peripheral arterial disease on abdominal aortic aneurysms in blood flow simulations

Peripheral arterial disease (PAD) and abdominal aortic aneurysms (AAAs) often coexist and pose significant risks of mortality, yet their mutual interactions remain largely unexplored. Here, we introduce a fluid mechanics model designed to simulate the haemodynamic impact of PAD on AAA-associated risk factors. Our focus lies on quantifying the uncertainty inherent in controlling the flow rates within PAD-affected vessels and predicting AAA risk factors derived from wall shear stress. We perform a sensitivity analysis on nine critical model parameters through simulations of three-dimensional blood flow within a comprehensive arterial geometry. Our results show effective control of the flow rates using two-element Windkessel models, although specific outlets need attention. Quantities of interest like endothelial cell activation potential (ECAP) and relative residence time are instructive for identifying high-risk regions, with ECAP showing greater reliability and adaptability. Our analysis reveals that the uncertainty in the quantities of interest is 187% of that of the input parameters. Notably, parameters governing the amplitude and frequency of the inlet velocity exert the strongest influence on the risk factors' variability and warrant precise determination. This study forms the foundation for patient-specific simulations involving PAD and AAAs which should ultimately improve patient outcomes and reduce associated mortality rates.

A novel parameter for the prediction of rupture risk of cerebral aneurysms based on morphology

Neurosurgeons often encounter dilemmas in the clinical management of cerebral aneurysms owing to an uncertainty of their rupture status and rupture risk. This study evaluates the influence of natural frequency of an aneurysm, as a novel morphological parameter to understand and analyze rupture status and risk prediction. In this work, we employ the natural frequency of 20 idealized and 50 patient specific aneurysms. The natural frequency of patient specific aneurysms is then compared against their rupture status. A strong correlation was observed between various morphological indicators and natural frequency for ideal and patient specific geometries. A statistical analysis with both Mann Whitney U test and T-test for rupture status against natural frequency has given a p-value less than 0.01 indicating a strong correlation between them. The correlation of morphological parameters with natural frequency from Pearson correlation coefficient and T-test suggests a holistic reflection of their effects on the natural frequency of an aneurysm. Thus, natural frequency could be a good indicator to discern the rupture potential of an aneurysm. The correlation between rupture status and natural frequency makes it a novel parameter that can differentiate between ruptured and unruptured patient specific aneurysms.

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

OBJECTIVE

ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict the ascending aortic aneurysm growth.

MATERIAL AND METHODS

70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified.

RESULTS

the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth.

CONCLUSION

global shape features might provide an important contribution for predicting the aneurysm growth.

AI-powered assessment of biomarkers for growth prediction of abdominal aortic aneurysms

Objective

The purpose of this study was to employ biomechanics-based biomarkers to locally characterize abdominal aortic aneurysm (AAA) tissue and investigate their relation to local aortic growth by means of an artificial intelligence model.

Methods

The study focused on a population of 36 patients with AAAs undergoing serial monitoring with electrocardiogram-gated multiphase computed tomography angiography acquisitions. The geometries of the aortic lumen and wall were reconstructed from the baseline scans and used for the baseline assessment of regional aortic weakness with three functional biomarkers, time-averaged wall-shear stress, in vivo principal strain, and intra-luminal thrombus thickness. The biomarkers were encoded as regional averages on axial and circumferential sections perpendicularly to the aortic centerline. Local diametric growth was obtained as difference in diameter between baseline and follow-up at the level of each axial section. An artificial intelligence model was developed to predict accelerated aneurysmal growth with the Extra Trees algorithm used as a binary classifier where the positive class represented regions that grew more than 2.5 mm/year. Additional clinical biomarkers, such as maximum aortic diameter at baseline, were also investigated as predictors of growth.

Results

The area under the curve for the constructed receiver operating characteristic curve for the Extra Trees classifier showed a very good performance in predicting relevant aortic growth (area under the curve = 0.92), with the three biomechanics-based functional biomarkers being objectively selected as the main predictors of growth.

Conclusions

The use of features based on the functional and local characterization of the aortic tissue resulted in a superior performance in terms of growth prediction when compared with models based on geometrical assessments. With rapid growth linked to increasing risk for patients with AAAs, the ability to access functional information related to tissue weakening and disease progression at baseline has the potential to support early clinical decisions and improve disease management.

Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction

Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the early treatment and surgical intervention of AAA. Capturing key features of vascular growth, such as blood flow and intraluminal thrombus (ILT) accumulation play a crucial role in uncovering the intricated mechanism of vascular adaptation, which can ultimately enhance AAA growth prediction capabilities. However, local correlations between hemodynamic metrics, biological and morphological characteristics, and AAA growth rates present high inter-patient variability that results in that the temporal-spatial biochemical and mechanical processes are still not fully understood. Hence, this study aims to integrate the physics-based knowledge with deep learning with a patch-based convolutional neural network (CNN) approach by incorporating important multiphysical features relating to its pathogenesis for validating its impact on AAA growth prediction. For this task, we observe that the unstructured multiphysical features cannot be directly employed in the kernel-based CNN. To tackle this issue, we propose a parameterization of features to leverage the spatio-temporal relations between multiphysical features. The proposed architecture was tested on different combinations of four features including radius, intraluminal thrombus thickness, time-average wall shear stress, and growth rate from 54 patients with 5-fold cross-validation with two metrics, a root mean squared error (RMSE) and relative error (RE). We conduct extensive experiments on AAA patients, the results show the effect of leveraging multiphysical features and demonstrate the superiority of the presented architecture to previous state-of-the-art methods in AAA growth prediction.

Association between blood flow pattern and rupture risk of abdominal aortic aneurysm based on computational fluid dynamics

OBJECTIVES

This study aimed to derive a novel classification of blood flow pattern in AAA based on computational fluid dynamics, and determine the predicting value of flow pattern in abdominal aortic aneurysm (AAA) rupture.

DESIGN

Age, gender matched case-control study MATERIALS: Case patients were identified as those who underwent emergent endovascular or open repair due to ruptured or impending rupture AAA. Control patients were those age and gender matched AAA patients who were asymptomatic and confirmed unruptured from CTA images from the same period.

METHODS

Classification of blood flow pattern (Type I: non-helical main flow channel with multiple vortices; Type II: non-helical main flow channel with single vortices; Type III, helical main flow channel with helical vortices) and hemodynamic parameters [areas of low wall shear stress (A _{low WSS}), aneurysm pressure drop (Δ pressure), etc.] were derived from computational fluid dynamic (CFD) analyses. Multivariate regression was used to determine independent risk factors of AAA rupture. The incremental discriminant and reclassification abilities for AAA rupture were compared among different models.

RESULTS

This study included 53 ruptured and 53 intact AAA patients. Ruptured AAA showed higher prevalence of type III flow pattern (60.38% vs. 15.09%, P<.001) compared to intact AAA. Type III flow pattern was associated with a significantly increased risk of aneurysm rupture (OR 10.22, 95%CI 3.43-30.49). Among all predicting models, combination of AAA diameter, hemodynamic parameters (A _{low WSS} or Δ pressure) and flow pattern showed highest discriminant abilities in both overall population (concordance statistic [c-index] .862) and subgroup patients with AAAs <55mm (c-index .972). Compared to AAA diameter, adding flow pattern could significantly improve the reclassification abilities in both overall population (net reclassification index [NRI] .321; p<.001) and subgroup of AAAs < 55mm (NRI .732, P<.001).

CONCLUSION

Type III flow pattern was associated with a significantly increased risk of AAA rupture. Integration of blood flow pattern may improve the identification of high-risk aneurysms in both overall population and AAAs smaller than 55mm.

A Comparative Study on the Hemodynamic Performance Within Cross and Non-cross Stent-Grafts for Abdominal Aortic Aneurysms With an Angulated Neck

Objectives: Cross-limb stent grafts for endovascular aneurysm repair (EVAR) are often employed for abdominal aortic aneurysms (AAAs) with significant aortic neck angulation. Neck angulation may be coronal or sagittal; however, previous hemodynamic studies of cross-limb EVAR stent grafts (SGs) primarily utilized simplified planar neck geometries. This study examined the differences in flow patterns and hemodynamic parameters between crossed and non-crossed limb SGs at different spatial neck angulations.

Methods: Ideal models consisting of 13 cross and 13 non-cross limbs were established, with coronal and sagittal angles ranging from 0 to 90°. Computational fluid dynamics (CFD) was used to capture the hemodynamic information, and the differences were compared.

Results: With regards to the pressure drop index, the maximum difference caused by the configuration and angular direction was 4.6 and 8.0%, respectively, but the difference resulting from the change in aneurysm neck angle can reach 27.1%. With regards to the SAR-TAWSS index, the maximum difference caused by the configuration and angular direction was 7.8 and 9.8%, respectively, but the difference resulting from the change in aneurysm neck angle can reach 26.7%. In addition, when the aneurysm neck angle is lower than 45°, the configuration and angular direction significantly influence the OSI and helical flow intensity index. However, when the aneurysm neck angle is greater than 45°, the hemodynamic differences of each model at the same aneurysm neck angle are reduced.

Conclusion: The main factor affecting the hemodynamic index was the angle of the aneurysm neck, while the configuration and angular direction had little effect on the hemodynamics. Furthermore, when the aneurysm neck was greatly angulated, the cross-limb technique did not increase the risk of thrombosis.

Mechanisms of abdominal aortic aneurysm progression: A review

Abdominal aortic aneurysm (AAA) is a common disease associated with significant cardiovascular morbidity and mortality. Up to now, there is still controversy on the choice of treatment method of AAA. Even so, the mechanisms of AAA progression are poorly defined, making targeting new therapies problematic. Current evidence favors an interaction of the hemodynamic microenvironment with local and systemic immune responses. In this review, we aim to provide an update of mechanisms in AAA progression, involving hemodynamics, perivascular adipose tissue, adventitial fibroblasts, vasa vasorum remodeling, intraluminal thrombus, and distribution of macrophage subtypes.

Statistical techniques for predicting rupture risk in abdominal aortic aneurysms: A contribution based on bootstrap

The morphometry of abdominal aortic aneurysms (AAA) has been recognized as one of the main factors that may predispose them to rupture. The need to quantify the morphometry of AAA on a patient-specific basis constitutes a valuable tool for assisting in rupture risk prediction. Previous results of this research group have determined the correlations between hemodynamic stresses and aneurysm morphometry by means of the Pearson coefficient. The present work aims to find how the AAA morphology correlates with the hemodynamic stresses acting on the arterial wall. To do so, the potential of the bootstrap technique has been explored. Bootstrap works appropriately in applications where few data are available (13 patient-specific AAA models were simulated). The methodology developed can be considered a contribution to predicting the hemodynamic stresses from the size and shape indices. The present work explores the use of a specific statistical technique (the bootstrap technique) to predict, based on morphological correlations, the patient-specific aneurysm rupture risk, provide greater understanding of this complex phenomenon that can bring about improvements in the clinical management of aneurysmatic patients. The results obtained using the bootstrap technique have greater reliability and robustness than those obtained by regression analysis using the Pearson coefficient, thus allowing to obtain more reliable results from the characteristics of the samples used, such as their small size and high variability. Additionally, it could be an indicator that other indices, such as AAA length, deformation rate, saccular index, and asymmetry, are important.

Analysis of hemodynamic changes from aneurysm inception to large sizes

While previous studies have identified many risk factors for the progression and rupture of cerebral aneurysms, the changes in aneurysm flow characteristics during its evolution are not fully understood. This work analyzes the changes in the aneurysm hemodynamic environment from its initial development to later stages when the aneurysm has substantially enlarged. A total of 88 aneurysms at four locations were studied with image based computational fluid dynamics (CFD). Two synthetic sequences representing the aneurysm geometry at three earlier stages were generated by shrinking the aneurysm sac while keeping the neck fixed or shrinking the neck simultaneously. The flow conditions were then quantitatively compared between these two modes of evolution. As aneurysms enlarged, the inflow rate increased in growing neck sequences, but decreased in fixed neck sequences. The inflow jet became more concentrated in both sequences. The mean aneurysm flow velocity and wall shear stress decreased in both sequences, but they decreased faster in enlarging aneurysms if the neck was fixed. Additionally, the intra-aneurysmal flows became more complex and more unstable, wall shear stress distribution became more oscillatory, and the area under low wall shear stress increased for both sequences. The evolution of flow characteristics of aneurysms with fixed and growing necks are different. The observed trends suggest that fixed neck aneurysms may evolve towards a flow environment characteristic of stable aneurysms faster than aneurysms with growing necks, which could also evolve towards a more disfavorable environment.

Ultrasound Based Computational Fluid Dynamics Assessment of Brachial Artery Wall Shear Stress in Preeclamptic Pregnancy

PURPOSE

Preeclampsia (PE) is a pregnancy complication of abnormally elevated blood pressure and organ damage where endothelial function is impaired. Wall shear stress (WSS) strongly effects endothelial cell morphology and function but in PE the WSS values are unknown. WSS calculations from ultrasound inaccurately assume cylindrical arteries and patient specific computational fluid dynamics (CFD) typically require time-consuming 3D imaging such as CT or MRI.

METHODS

Two-dimensional (2D) B-mode ultrasound images were lofted together to create simplified three-dimensional (3D) geometries of the brachial artery (BA) that incorporate artery curvature and non-circular cross sections. This process was efficient and on average took 120 ± 10 s. Patient specific CFD was then performed to quantify BA WSS for a small cohort of PE (n = 5) and normotensive pregnant patients (n = 5) and compared against WSS calculations assuming a cylindrical artery.

RESULTS

For several WSS metrics (time averaged WSS (TAWSS), peak systolic WSS, oscillatory shear index (OSI), OSI/TAWSS and relative residence time) CFD on the simplified arterial geometries calculated large spatial differences in WSS that assuming a cylindrical artery cannot calculate. Bland-Altman and intra-class correlation (ICC) analyses found assuming a cylindrical artery both underestimated (p < 0.05) and had poor agreement (ICC < 0.5) with the maximum WSS values from CFD. WSS values that were abnormal compared to the normotensive patients (OSI = 0.014 ± 0.026) appear related to the pregnancy complications fetal growth restriction (n = 2, OSI = 0.14, 0.25) and gestational diabetes (n = 1, OSI = 0.23).

CONCLUSION

Creating 3D artery geometries from 2D ultrasound images can be used for CFD simulations to calculate WSS from ultrasound without assuming cylindrical arteries. This approach requires minimal time for both medical imaging and CFD analysis.