Abdominal Aortic Aneurysm Wall Stress Research Articles

On the Relative Effects of Wall and Intraluminal Thrombus Constitutive Material Properties in Abdominal Aortic Aneurysm Wall Stress.

An abdominal aortic aneurysm (AAA) is a dilation localized in the infrarenal segment of the abdominal aorta that can expand continuously and rupture if left untreated. Computational methods such as finite element analysis (FEA) are widely used with in silico models to calculate biomechanical predictors of rupture risk while choosing constitutive material properties for the AAA wall and intraluminal thrombus (ILT). In the present work, we investigated the effect of different constitutive material properties for the wall and ILT on 21 idealized and 10 unruptured patient-specific AAA geometries. Three material properties were used to characterize the wall and two for the ILT, leading to six material model combinations for each AAA geometry subject to appropriate boundary conditions. The results of the FEA simulations indicate significant differences in the average peak wall stress (PWS), 99th percentile wall stress (99th WS), and spatially averaged wall stress (SAWS) for all AAA geometries subject to the choice of a material model combination. Specifically, using a material model combination with a compliant ILT yielded statistically higher wall stresses compared to using a stiff ILT, irrespective of the constitutive equation used to model the AAA wall. This work provides quantitative insight into the variability of the wall stress distributions ensuing from AAA FEA modeling due to its strong dependency on population-averaged soft tissue material characterizations. This dependency leads to uncertainty about the true biomechanical state of stress of an individual AAA and the subsequent assessment of its rupture risk.

Biomechanics and early sac regression after endovascular aneurysm repair of abdominal aortic aneurysm

BackgroundSac regression after endovascular aneurysm repair (EVAR) of abdominal aortic aneurysms (AAA) is regarded as a marker of successful response to treatment. Several factors influence sac behavior after EVAR, yet little is known about the value of preoperative biomechanics. The aim of this study was to investigate the difference in aortic biomechanics between patients with and without sac regression. MethodsPatients treated with standard EVAR for infrarenal AAA at the Karolinska University Hospital between 2009 and 2012 with one preoperative and a minimum of two postoperative computed tomography angiography (CTA) scans were considered for inclusion in this single-center retrospective cohort study. Biomechanical indices such as AAA wall stress and wall stress-strength ratio as well as intraluminal thrombus (ILT) thickness and stress were measured preoperatively in A4ClinicRE (VASCOPS GmbH). AAA diameter and volume were analyzed on preoperative, 30-day, and 1-year CTAs. Patients were dichotomized based on sac regression, defined as a ≥5 mm decrease in maximal AAA diameter between the first two postoperative CTA scans. Multivariable logistic regression was used for analysis of factors associated with early sac regression. ResultsOf the 101 patients treated during the inclusion period, 64 were included. Thirty-nine (61%) demonstrated sac regression and 25 (39%) had a stable sac or sac increase. The mean patients age (73 years vs 76 years), male sex (85% vs 96%), and median AAA diameter (58 mm vs 58.5 mm) did not differ between patients with and without sac regression. Although no difference in preoperative biomechanics was seen between the groups, multivariable logistic regression revealed that a larger AAA diameter (odds ratio [OR], 1.27; 95% confidence interval [CI], 1.06-1.51; P = .009) and smoking (OR, 22.1; 95% CI, 2.78-174; P = .003) were positively associated with sac regression. In contrast, the lumen diameter (OR, 0.87; 95% CI, 0.77-0.98; P = .023), ILT thickness (OR, 0.85; 95% CI, 0.75-0.97; P = .013), aspirin or direct-acting oral anticoagulant use (OR, 0.11; 95% CI, 0.02-0.61; P = .012), and mean ILT stress (OR, 0.35; 95% CI, 0.14-0.87; P = .024) showed a negative association. Patients with sac regression had fewer reinterventions (log-rank P = .010) and lower mortality (log-rank P = .012) at the 5-year follow-up. ConclusionsThis study, characterizing preoperative biomechanics in patients with and without sac regression, demonstrated a negative association between mean ILT stress and ILT thickness with a change in sac diameter after EVAR. Given that the ILT is a highly dynamic entity, further studies focusing on the role of the thrombus are needed. Furthermore, patients presenting with early sac regression had improved outcomes after EVAR.

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.

Toward modeling the effects of regional material properties on the wall stress distribution of abdominal aortic aneurysms.

The overall geometry and different biomechanical parameters of an abdominal aortic aneurysm (AAA), contribute to its severity and risk of rupture, therefore they could be used to track its progression. Previous and ongoing research efforts have resorted to using uniform material properties to model the behavior of AAA. However, it has been recently illustrated that different regions of the AAA wall exhibit different behavior due to the effect of the biological activities in the metalloproteinase matrix that makes up the wall at the aneurysm site. In this work, we introduce a non-invasive patient-specific regional material property model to help us better understand and investigate the AAA wall stress distribution, peak wall stress (PWS) severity, and potential rupture risk. Our results indicate that the PWS and the overall wall stress distribution predicted using the proposed regional material property model, are higher than those predicted using the traditional homogeneous, hyper-elastic model (p <1.43E-07). Our results also show that to investigate AAA, the overall geometry, presence of intra-luminal thrombus (ILT), and loading condition in a patient specific manner may be critical for capturing the biomechanical complexity of AAAs.

Impact of isotropic constitutive descriptions on the predicted peak wall stress in abdominal aortic aneurysms

Biomechanics-based assessment of Abdominal Aortic Aneurysm (AAA) rupture risk has gained considerable scientific and clinical momentum. However, computation of peak wall stress (PWS) using state-of-the-art finite element models is time demanding. This study investigates which features of the constitutive description of AAA wall are decisive for achieving acceptable stress predictions in it. Influence of five different isotropic constitutive descriptions of AAA wall is tested; models reflect realistic non-linear, artificially stiff non-linear, or artificially stiff pseudo-linear constitutive descriptions of AAA wall. Influence of the AAA wall model is tested on idealized (n=4) and patient-specific (n=16) AAA geometries. Wall stress computations consider a (hypothetical) load-free configuration and include residual stresses homogenizing the stresses across the wall. Wall stress differences amongst the different descriptions were statistically analyzed. When the qualitatively similar non-linear response of the AAA wall with low initial stiffness and subsequent strain stiffening was taken into consideration, wall stress (and PWS) predictions did not change significantly. Keeping this non-linear feature when using an artificially stiff wall can save up to 30% of the computational time, without significant change in PWS. In contrast, a stiff pseudo-linear elastic model may underestimate the PWS and is not reliable for AAA wall stress computations.

Haemodynamics and stresses in abdominal aortic aneurysms: A fluid-structure interaction study into the effect of proximal neck and iliac bifurcation angle

Our knowledge of how geometry influences abdominal aortic aneurysm (AAA) biomechanics is still developing. Both iliac bifurcation angle and proximal neck angle could impact the haemodynamics and stresses within AAA. Recent comparisons of the morphology of ruptured and intact AAA show that cases with large iliac bifurcation angles are less likely to rupture than those with smaller angles. We aimed to perform fluid-structure interaction (FSI) simulations on a range of idealised AAA geometries to conclusively determine the influence of proximal neck and iliac bifurcation angle on AAA wall stress and haemodynamics.Peak wall shear stress (WSS) and time-averaged WSS (TAWSS) in the AAA sac region only increased when the proximal neck angle exceeded 30°. Both peak WSS (p<0.0001) and peak von Mises wall stress (p=0.027) increased with iliac bifurcation angle, whereas endothelial cell activation potential (ECAP) decreased with iliac bifurcation angle (p<0.001) and increased with increasing neck angle.These observations may be important as AAAs have been shown to expand, develop thrombus and rupture in areas of low WSS. Here we show that AAAs with larger iliac bifurcation angles have higher WSS, potentially reducing the likelihood of rupture. Furthermore, ECAP was lower in AAA geometries with larger iliac bifurcation angles, implying less likelihood of thrombus development and wall degeneration. Therefore our findings could help explain the clinical observation of lower rupture rates associated with AAAs with large iliac bifurcation angles.

The Relationship Between Surface Curvature and Abdominal Aortic Aneurysm Wall Stress.

The maximum diameter (MD) criterion is the most important factor when predicting risk of rupture of abdominal aortic aneurysms (AAAs). An elevated wall stress has also been linked to a high risk of aneurysm rupture, yet is an uncommon clinical practice to compute AAA wall stress. The purpose of this study is to assess whether other characteristics of the AAA geometry are statistically correlated with wall stress. Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis (FEA). These models were subsequently used to estimate wall stress and maximum diameter and to evaluate the spatial distributions of wall thickness, cross-sectional diameter, mean curvature, and Gaussian curvature. Data analysis consisted of statistical correlations of the aforementioned geometry metrics with wall stress for the 30 AAA inner and outer wall surfaces. In addition, a linear regression analysis was performed with all the AAA wall surfaces to quantify the relationship of the geometric indices with wall stress. These analyses indicated that while all the geometry metrics have statistically significant correlations with wall stress, the local mean curvature (LMC) exhibits the highest average Pearson's correlation coefficient for both inner and outer wall surfaces. The linear regression analysis revealed coefficients of determination for the outer and inner wall surfaces of 0.712 and 0.516, respectively, with LMC having the largest effect on the linear regression equation with wall stress. This work underscores the importance of evaluating AAA mean wall curvature as a potential surrogate for wall stress.

The Association Between Geometry and Wall Stress in Emergently Repaired Abdominal Aortic Aneurysms.

Abdominal aortic aneurysm (AAA) is a prevalent cardiovascular disease characterized by the focal dilation of the aorta, which supplies blood to all the organs and tissues in the systemic circulation. With the AAA increasing in diameter over time, the risk of aneurysm rupture is generally associated with the size of the aneurysm. If diagnosed on time, intervention is recommended to prevent AAA rupture. The criterion to decide on surgical intervention is determined by measuring the maximum diameter of the aneurysm relative to the critical value of 5.5cm. However, a more reliable approach could be based on understanding the biomechanical behavior of the aneurysmal wall. In addition, geometric features that are proven to be significant predictors of the AAA wall mechanics could be used as surrogates of the AAA biomechanical behavior and, subsequently, of the aneurysm's risk of rupture. The aim of this work is to identify those geometric indices that have a high correlation with AAA wall stress in the population of patients who received an emergent repair of their aneurysm. In-house segmentation and meshing algorithms were used to model 75 AAAs followed by estimation of the spatially distributed wall stress by performing finite element analysis. Fifty-two shape and size geometric indices were calculated for the same models using MATLAB scripting. Hypotheses testing were carried out to identify the indices significantly correlated with wall stress by constructing a Pearson's correlation coefficient matrix. The analyses revealed that 12 indices displayed high correlation with the wall stress, amongst which wall thickness and curvature-based indices exhibited the highest correlations. Stepwise regression analysis of these correlated indices indicated that wall stress can be predicted by the following four indices with an accuracy of 76%: maximum aneurysm diameter, aneurysm sac length, average wall thickness at the maximum diameter cross-section, and the median of the wall thickness variance. The primary outcome of this work emphasizes the use of global measures of size and wall thickness as geometric surrogates of wall stress for emergently repaired AAAs.

Increased expression of Lamin A/C correlates with regions of high wall-stress in abdominal aortic aneurysms.

Increased expression of Lamin A/C correlates with regions of high wall-stress in abdominal aortic aneurysms.

Biomechanical rupture risk assessment of abdominal aortic aneurysms based on a novel probabilistic rupture risk index.

A rupture risk assessment is critical to the clinical treatment of abdominal aortic aneurysm (AAA) patients. The biomechanical AAA rupture risk assessment quantitatively integrates many known AAA rupture risk factors but the variability of risk predictions due to model input uncertainties remains a challenging limitation. This study derives a probabilistic rupture risk index (PRRI). Specifically, the uncertainties in AAA wall thickness and wall strength were considered, and wall stress was predicted with a state-of-the-art deterministic biomechanical model. The discriminative power of PRRI was tested in a diameter-matched cohort of ruptured (n = 7) and intact (n = 7) AAAs and compared to alternative risk assessment methods. Computed PRRI at 1.5 mean arterial pressure was significantly (p = 0.041) higher in ruptured AAAs (20.21(s.d. 14.15%)) than in intact AAAs (3.71(s.d. 5.77)%). PRRI showed a high sensitivity and specificity (discriminative power of 0.837) to discriminate between ruptured and intact AAA cases. The underlying statistical representation of stochastic data of wall thickness, wall strength and peak wall stress had only negligible effects on PRRI computations. Uncertainties in AAA wall stress predictions, the wide range of reported wall strength and the stochastic nature of failure motivate a probabilistic rupture risk assessment. Advanced AAA biomechanical modelling paired with a probabilistic rupture index definition as known from engineering risk assessment seems to be superior to a purely deterministic approach.

A simple, effective and clinically applicable method to compute abdominal aortic aneurysm wall stress

Abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is a symptomless condition that if left untreated can expand to the point of rupture. Mechanically-speaking, rupture of an artery occurs when the local wall stress exceeds the local wall strength. It is therefore desirable to be able to non-invasively estimate the AAA wall stress for a given patient, quickly and reliably.In this paper we present an entirely new approach to computing the wall tension (i.e. the stress resultant equal to the integral of the stresses tangent to the wall over the wall thickness) within an AAA that relies on trivial linear elastic finite element computations, which can be performed instantaneously in the clinical environment on the simplest computing hardware. As an input to our calculations we only use information readily available in the clinic: the shape of the aneurysm in-vivo, as seen on a computed tomography (CT) scan, and blood pressure. We demonstrate that tension fields computed with the proposed approach agree well with those obtained using very sophisticated, state-of-the-art non-linear inverse procedures. Using magnetic resonance (MR) images of the same patient, we can approximately measure the local wall thickness and calculate the local wall stress. What is truly exciting about this simple approach is that one does not need any information on material parameters; this supports the development and use of patient-specific modelling (PSM), where uncertainty in material data is recognised as a key limitation.The methods demonstrated in this paper are applicable to other areas of biomechanics where the loads and loaded geometry of the system are known.

Computational Growth and Remodeling of Abdominal Aortic Aneurysms Constrained by the Spine.

Abdominal aortic aneurysms (AAAs) evolve over time, and the vertebral column, which acts as an external barrier, affects their biomechanical properties. Mechanical interaction between AAAs and the spine is believed to alter the geometry, wall stress distribution, and blood flow, although the degree of this interaction may depend on AAAs specific configurations. In this study, we use a growth and remodeling (G&R) model, which is able to trace alterations of the geometry, thus allowing us to computationally investigate the effect of the spine for progression of the AAA. Medical image-based geometry of an aorta is constructed along with the spine surface, which is incorporated into the computational model as a cloud of points. The G&R simulation is initiated by local elastin degradation with different spatial distributions. The AAA-spine interaction is accounted for using a penalty method when the AAA surface meets the spine surface. The simulation results show that, while the radial growth of the AAA wall is prevented on the posterior side due to the spine acting as a constraint, the AAA expands faster on the anterior side, leading to higher curvature and asymmetry in the AAA configuration compared to the simulation excluding the spine. Accordingly, the AAA wall stress increases on the lateral, posterolateral, and the shoulder regions of the anterior side due to the AAA-spine contact. In addition, more collagen is deposited on the regions with a maximum diameter. We show that an image-based computational G&R model not only enhances the prediction of the geometry, wall stress, and strength distributions of AAAs but also provides a framework to account for the interactions between an enlarging AAA and the spine for a better rupture potential assessment and management of AAA patients.

Local Quantification of Wall Thickness and Intraluminal Thrombus Offer Insight into the Mechanical Properties of the Aneurysmal Aorta.

Wall stress is a powerful tool to assist clinical decisions in rupture risk assessment of abdominal aortic aneurysms. Key modeling assumptions that influence wall stress magnitude and distribution are the inclusion or exclusion of the intraluminal thrombus in the model and the assumption of a uniform wall thickness. We employed a combined numerical-experimental approach to test the hypothesis that abdominal aortic aneurysm (AAA) wall tissues with different thickness as well as wall tissues covered by different thrombus thickness, exhibit differences in the mechanical behavior. Ultimate tissue strength was measured from in vitro tensile testing of AAA specimens and material properties of the wall were estimated by fitting the results of the tensile tests to a histo-mechanical constitutive model. Results showed a decrease in tissue strength and collagen stiffness with increasing wall thickness, supporting the hypothesis of wall thickening being mediated by accumulation of non load-bearing components. Additionally, an increase in thrombus deposition resulted in a reduction of elastin content, collagen stiffness and tissue strength. Local wall thickness and thrombus coverage may be used as surrogate measures of local mechanical properties of the tissue, and therefore, are possible candidates to improve the specificity of AAA wall stress and rupture risk evaluations.

Correction: Improving the Efficiency of Abdominal Aortic Aneurysm Wall Stress Computations

Correction: Improving the Efficiency of Abdominal Aortic Aneurysm Wall Stress Computations

Improving the Efficiency of Abdominal Aortic Aneurysm Wall Stress Computations

An abdominal aortic aneurysm is a pathological dilation of the abdominal aorta, which carries a high mortality rate if ruptured. The most commonly used surrogate marker of rupture risk is the maximal transverse diameter of the aneurysm. More recent studies suggest that wall stress from models of patient-specific aneurysm geometries extracted, for instance, from computed tomography images may be a more accurate predictor of rupture risk and an important factor in AAA size progression. However, quantification of wall stress is typically computationally intensive and time-consuming, mainly due to the nonlinear mechanical behavior of the abdominal aortic aneurysm walls. These difficulties have limited the potential of computational models in clinical practice. To facilitate computation of wall stresses, we propose to use a linear approach that ensures equilibrium of wall stresses in the aneurysms. This proposed linear model approach is easy to implement and eliminates the burden of nonlinear computations. To assess the accuracy of our proposed approach to compute wall stresses, results from idealized and patient-specific model simulations were compared to those obtained using conventional approaches and to those of a hypothetical, reference abdominal aortic aneurysm model. For the reference model, wall mechanical properties and the initial unloaded and unstressed configuration were assumed to be known, and the resulting wall stresses were used as reference for comparison. Our proposed linear approach accurately approximates wall stresses for varying model geometries and wall material properties. Our findings suggest that the proposed linear approach could be used as an effective, efficient, easy-to-use clinical tool to estimate patient-specific wall stresses.

Importance of material model in wall stress prediction in abdominal aortic aneurysms

BackgroundResults of biomechanical simulation of the abdominal aortic aneurysm (AAA) depend on the constitutive description of the wall. Based on in vitro and in vivo experimental data several constitutive models for the AAA wall have been proposed in the literature. Those models differ strongly from each other and their impact on the computed stress in biomechanical simulation is not clearly understood. MethodsFinite element (FE) models of AAAs from 7 patients who underwent elective surgical repair were used to compute wall stresses. AAA geometry was reconstructed from CT angiography (CT-A) data and patient-specific (PS) constitutive descriptions of the wall were derived from planar biaxial testing of anterior wall tissue samples. In total 28 FE models were used, where the wall was described by either patient-specific or previously reported study-average properties. This data was derived from either uniaxial or biaxial in vitro testing. Computed wall stress fields were compared on node-by-node basis. ResultsDifferent constitutive models for the AAA wall cause significantly different predictions of wall stress. While study-average data from biaxial testing gives globally the same stress field as the patient-specific wall properties, the material model based on uniaxial test data overestimates the wall stress on average by 30kPa or about 67% of the mean stress. A quasi-linear description based on the in vivo measured distensibility of the AAA wall leads to a completely altered stress field and overestimates the wall stress by about 75kPa or about 167% of the mean stress. ConclusionThe present study demonstrated that the constitutive description of the wall is crucial for AAA wall stress prediction. Consequently, results obtained using different models should not be mutually compared unless different stress gradients across the wall are not taken into account. Highly nonlinear material models should be preferred when the response of AAA to increased blood pressure is investigated, while the quasi-linear model with high initial stiffness produces negligible stress gradients across the wall and thus, it is more appropriate when response to mean blood pressure is calculated.

Wall Stress Reduction in Abdominal Aortic Aneurysms as a Result of Polymeric Endoaortic Paving

Polymeric endoaortic paving (PEAP) may improve endovascular repair of abdominal aortic aneurysms (AAA) since it has the potential to treat patients with complex AAA geometries while reducing the incidence of migration and endoleak. Polycaprolactone (PCL)/polyurethane (PU) blends are proposed as PEAP materials due to their range of mechanical properties, thermoformability, and resistance to biodegradation. In this study, the reduction in AAA wall stress that can be achieved using PEAP was estimated and compared to that resulting from stent-grafts. This was accomplished by mechanically modeling the anisotropic response of PCL/PU blends and implementing these results into finite element model (FEM) simulations. We found that at the maximum diameter of the AAA, the 50/50 and 10/90 PCL/PU blends reduced wall stress by 99 and 98%, respectively, while a stent-graft reduced wall stress by 99%. Our results also show that wall stress reduction increases with increasing PEAP thickness and PCL content in the blend ratio. These results indicate that PEAP can reduce AAA wall stress as effectively as a stent-graft. As such, we propose that PEAP may provide an improved treatment alternative for AAA, since many of the limitations of stent-grafts have the potential to be solved using this approach.

A Randomized Placebo Controlled Trial of the Effect of Preoperative Statin Use on Matrix Metalloproteinases and Tissue Inhibitors of Matrix Metalloproteinases in Areas of Low and Peak Wall Stress in Patients Undergoing Elective Open Repair of Abdominal Aortic Aneurysm

A double-blind, randomized controlled trial was carried out to study the effects of statins on matrix metalloproteinases (MMPs) and tissue inhibitors of matrix metalloproteinases (TIMPs) in areas of peak and low abdominal aortic aneurysm (AAA) wall stress. A total of 40 patients undergoing elective open AAA repair were randomized to receive either atorvastatin 80 mg (n = 20) or placebo (n = 20) for 4 weeks preoperatively. Finite element analysis was used to determine AAA wall stress distribution. Full thickness aortic samples were obtained at surgery from areas of low and peak wall stress, snap-frozen, and stored at -80°C for subsequent MMP-2, -8, and -9 and TIMP-1 and -2 analyses. Statistical analysis was performed using SPSS 16.0 (SPSS Inc, Chicago, IL). Both groups were well matched (p > 0.05) regarding age, gender, comorbidities, and duration of hospital stay. There were no statistically significant differences in levels of MMPs and TIMPs between the statin and placebo group and between areas of low and peak AAA wall stress. The short-term use of statins is not associated in reducing levels of MMP 2, 8, and 9 and TIMP-1 and -2 in areas of low and peak wall stress in patients with AAA.

Comments regarding ‘The Influence of Wall Stress on AAA Growth and Biomarkers’

This is a complex study of 37 patients with abdominal aortic aneurysms that range from 40 to 55 mm in diameter, sizes that are normally followed closely and considered for intervention at the upper end of the diameter range. The authors examined the relationship between maximum aneurysm diameter (Dm), maximum aneurysm wall stress (Sm), aneurysm diameter growth rate and several circulating biomarkers of inflammation and/or degeneration. While Dm is the accepted AAA variable for size classification and management recommendations, the advent of computational methods for determining local wall stresses from CT scans has emerged in the past decade as a potentially powerful tool for predicting aneurysm rupture. However, Sm is only half of the AAA rupture equation. The other half is the degree of damage to the aneurysm wall due to several factors which lower wall rupture strength including atherosclerosis and deformation associated with growth. Mechanical failure or rupture occurs when arterial pressure induced wall stress exceeds the ability of the damaged wall to remain intact. While determination of aneurysm wall rupture strength in vivo is an elusive and complex problem, serum biomarker concentrations may indirectly provide an estimate of the degree of AAA wall damage. This study gives a glimpse of the relationships between these variables. The potential value of Sm over Dm in predicting aneurysm rupture is illustrated in Figure 2. While the positive correlation of maximum stress with maximum diameter is expected and not a new finding, Figure 2 illustrates both the wide variability of Sm in aneurysms of similar Dm and the potential value of maximum stress relative to maximum diameter, that is, the ratio of Sm to Dm, or Sm/Dm. AAAs develop from near cylindrical aortas with low and relatively similar Sm/Dm values. With growth aneurysm geometries vary considerably between patients and Sm is rarely located in the plane of Dm, resulting in the wide variability of Sm/Dm. Figure 3 shows that the diameter growth rates of the one third of aneurysms with the lowest Sm/Dm values have significantly lower growth rates than that of the two thirds with higher Sm/Dm values. This means that maximum stress relative to maximum diameter (Sm/Dm) may be a valuable index for predicting AAA growth. Unfortunately circulating biomarkers were measured in only 18 of the 37 patients. However, in this subset, aneurysm growth rates correlate significantly with levels of matrix metalloproteinase-9 and both MMP-9 and CPR tended to increase with increasing values of Sm/Dm. These findings support the author's hypothesis that AAAs in patients with high Sm/Dm values may undergo more rapid growth and wall damage than those with low Sm/Dm values. The shortcomings of this study are the small number of patients with serum biomarker analysis, the restriction of data to AAAs with diameters of 40 to 55 mm and the utilization of repeated measurement of aneurysm growth in a number of patients. Never the less, this study serves as a model for future investigations into the role of AAA wall stress, growth rate and serum biomarkers in estimating the probability of AAA rupture. The Influence of Wall Stress on AAA Growth and BiomarkersEuropean Journal of Vascular and Endovascular SurgeryVol. 39Issue 4PreviewThis study investigated the relation between abdominal aortic aneurysm (AAA) wall stress, AAA growth rate and biomarker concentrations. With increasing wall stress, more damage may be caused to the AAA wall, possibly leading to progression of the aneurysm and reflection in up- or downregulation of specific circulating biomarkers. Levels of matrix metalloproteinase-9, tissue inhibitor of matrix metalloproteinase-1, C-reactive protein and alpha 1-antitrypsin were therefore evaluated. Full-Text PDF Open Archive

The Influence of Wall Stress on AAA Growth and Biomarkers

Objectives This study investigated the relation between abdominal aortic aneurysm (AAA) wall stress, AAA growth rate and biomarker concentrations. With increasing wall stress, more damage may be caused to the AAA wall, possibly leading to progression of the aneurysm and reflection in up- or downregulation of specific circulating biomarkers. Levels of matrix metalloproteinase-9, tissue inhibitor of matrix metalloproteinase-1, C-reactive protein and alpha 1-antitrypsin were therefore evaluated. Methods Thirty-seven patients (maximum AAA diameter 41–55 mm) with two, three or four consecutive computed tomography angiography (CTA) scans were prospectively included. Diameter growth rate in mm/year was determined between each pair of two sequential CTA scans. AAA wall stress was computed by finite element analysis, based on the first of the two sequential CTA scans only ( n = 69 pairs). Biomarker information was determined in 46 measurements in 18 patients. The relation between AAA diameter and wall stress was determined and the AAA's were divided into three equally sized groups (relative low, medium and high stress). Growth rate and biomarker concentrations were compared between these groups. Additionally, correlation coefficients were computed between absolute wall stress, AAA growth and biomarker concentrations. Results A relative low AAA wall stress was associated with a lower aneurysm growth rate. Growth rate was also positively related to MMP-9 plasma concentration ( r = 0.32). The average MMP-9 and CRP concentrations increased with increasing degrees of relative wall stress, although the absolute and relative wall stress did not correlate with any of the biomarkers. Conclusion Although lower relative wall stress was associated to a lower AAA growth rate, no relation was found between biomarker concentrations and wall stress. Future research may focus on more and extensive biomarker measurements in relation to AAA wall stress.