Successful endovascular treatment of severe chronic mesenteric ischemia facilitated by intraoperative positioning system image guidance.

We report our initial experience using the intraoperative positioning system (IOPS), a novel endovascular navigation system that does not require contrast or radiation, in the treatment of chronic mesenteric ischemia (CMI). We used IOPS to help treat three of four consecutive patients with CMI. Technical problems prevented successful use in one patient. For the patients for whom IOPS was used effectively, catheterization of the mesenteric artery was accomplished more quickly than for the patient for whom IOPS was not effective. Our experience has shown that IOPS can be safely and effectively used for CMI and can reduce the contrast load and radiation dose.

Multifactorial dysphagia: Azygos vein aneurysm (AVA) and esophagogastric junction outflow obstruction (EGJOO).

INTRODUCTION: Vascular impingement of the esophagus is a rare cause of dysphagia, and is most commonly due to aortic arch anomalies such as arterial lusoria. Dysphagia resultant from venous compression is even further less likely. PRESENTATION OF CASE: We present a highly unusual case of dysphagia secondary to a large aneurysm of the azygous vein near its confluence with the superior vena cava, which was managed with endovascular modalities. Despite initial treatment success, patient reported some intermittent solid food dysphagia, and was also found to have esophagogastric junction outflow obstruction (EGJOO) on high resolution impedance manometry (HRIM) which was successfully managed with surgical myotomy and partial fundoplication. DISCUSSION: The azygos vein has an intimate anatomic relationship with the esophagus as it traverses the posterior mediastinum. Because of this anatomic association, the azygos vein may present a point of esophageal obstruction in the setting of significant pathology. CONCLUSION: This case highlights the possibility of multifactorial causes of dysphagia, and that HRIM is a key aspect of this workup. Additionally we discuss the pertinent anatomy, diagnosis, and treatments for azygos vein aneurysm and EGJOO.

A Comparative Classification Analysis of Abdominal Aortic Aneurysms by Machine Learning Algorithms.

The objective of this work was to perform image-based classification of abdominal aortic aneurysms (AAA) based on their demographic, geometric, and biomechanical attributes. We retrospectively reviewed existing demographics and abdominal computed tomography angiography images of 100 asymptomatic and 50 symptomatic AAA patients who received an elective or emergent repair, respectively, within 1-6 months of their last follow up. An in-house script developed within the MATLAB computational platform was used to segment the clinical images, calculate 53 descriptors of AAA geometry, and generate volume meshes suitable for finite element analysis (FEA). Using a third party FEA solver, four biomechanical markers were calculated from the wall stress distributions. Eight machine learning algorithms (MLA) were used to develop classification models based on the discriminatory potential of the demographic, geometric, and biomechanical variables. The overall classification performance of the algorithms was assessed by the accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and precision of their predictions. The generalized additive model (GAM) was found to have the highest accuracy (87%), AUC (89%), and sensitivity (78%), and the third highest specificity (92%), in classifying the individual AAA as either asymptomatic or symptomatic. The k-nearest neighbor classifier yielded the highest specificity (96%). GAM used seven markers (six geometric and one biomechanical) to develop the classifier. The maximum transverse dimension, the average wall thickness at the maximum diameter, and the spatially averaged wall stress were found to be the most influential markers in the classification analysis. A second classification analysis revealed that using maximum diameter alone results in a lower accuracy (79%) than using GAM with seven geometric and biomechanical markers. We infer from these results that biomechanical and geometric measures by themselves are not sufficient to discriminate adequately between population samples of asymptomatic and symptomatic AAA, whereas MLA offer a statistical approach to stratification of rupture risk by combining demographic, geometric, and biomechanical attributes of patient-specific AAA.

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.

Wall Stress and Geometry Measures in Electively Repaired Abdominal Aortic Aneurysms.

Abdominal aortic aneurysm (AAA) is a vascular disease characterized by the enlargement of the infrarenal segment of the aorta. A ruptured AAA can cause internal bleeding and carries a high mortality rate, which is why the clinical management of the disease is focused on preventing aneurysm rupture. AAA rupture risk is estimated by the change in maximum diameter over time (i.e., growth rate) or if the diameter reaches a prescribed threshold. The latter is typically 5.5 cm in most clinical centers, at which time surgical intervention is recommended. While a size-based criterion is suitable for most patients who are diagnosed at an early stage of the disease, it is well known that some small AAA rupture or patients become symptomatic prior to a maximum diameter of 5.5 cm. Consequently, the mechanical stress in the aortic wall can also be used as an integral component of a biomechanics-based rupture risk assessment strategy. In this work, we seek to identify geometric characteristics that correlate strongly with wall stress using a sample space of 100 asymptomatic, unruptured, electively repaired AAA models. The segmentation of the clinical images, volume meshing, and quantification of up to 45 geometric measures of each AAA were done using in-house Matlab scripts. Finite element analysis was performed to compute the first principal stress distributions from which three global biomechanical parameters were calculated: peak wall stress, 99th percentile wall stress and spatially averaged wall stress. Following a feature reduction approach consisting of Pearson's correlation matrices with Bonferroni correction and linear regressions, a multivariate stepwise regression analysis was conducted to find the geometric measures most highly correlated with each of the biomechanical parameters. Our findings indicate that wall stress can be predicted by geometric indices with an accuracy of up to 94% when AAA models are generated with uniform wall thickness and up to 67% for patient specific, non-uniform wall thickness AAA. These geometric predictors of wall stress could be used in lieu of complex finite element models as part of a geometry-based protocol for rupture risk assessment.

Correction to: Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures.

This erratum is to correct the variable name on the left hand side of Eq. (2). The correct variable name is "Diameter" rather than the stated "Area."

Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures.

Abdominal aortic aneurysm (AAA) is an asymptomatic aortic disease with a survival rate of 20% after rupture. It is a vascular degenerative condition different from occlusive arterial diseases. The size of the aneurysm is the most important determining factor in its clinical management. However, other measures of the AAA geometry that are currently not used clinically may also influence its rupture risk. With this in mind, the objectives of this work are to develop an algorithm to calculate the AAA wall thickness and abdominal aortic diameter at planes orthogonal to the vessel centerline, and to quantify the effect of geometric indices derived from this algorithm on the overall classification accuracy of AAA based on whether they were electively or emergently repaired. Such quantification was performed based on a retrospective review of existing medical records of 150 AAA patients (75 electively repaired and 75 emergently repaired). Using an algorithm implemented within the MATLAB computing environment, 10 diameter- and wall thickness-related indices had a significant difference in their means when calculated relative to the AAA centerline compared to calculating them relative to the medial axis. Of these 10 indices, nine were wall thickness-related while the remaining one was the maximum diameter (Dmax). Dmax calculated with respect to the medial axis is over-estimated for both electively and emergently repaired AAA compared to its counterpart with respect to the centerline. C5.0 decision trees, a machine learning classification algorithm implemented in the R environment, were used to construct a statistical classifier. The decision trees were built by splitting the data into 70% for training and 30% for testing, and the properties of the classifier were estimated based on 1000 random combinations of the 70/30 data split. The ensuing model had average and maximum classification accuracies of 81.0 and 95.6%, respectively, and revealed that the three most significant indices in classifying AAA are, in order of importance: AAA centerline length, L2-norm of the Gaussian curvature, and AAA wall surface area. Therefore, we infer that the aforementioned three geometric indices could be used in a clinical setting to assess the risk of AAA rupture by means of a decision tree classifier. This work provides support for calculating cross-sectional diameters and wall thicknesses relative to the AAA centerline and using size and surface curvature based indices in classification studies of AAA.

Geometric surrogates of abdominal aortic aneurysm wall mechanics.

The maximum diameter criterion is the most important factor in the clinical management of abdominal aortic aneurysms (AAA). Consequently, interventional repair is recommended when an aneurysm reaches a critical diameter, typically 5.0 cm in the United States. Nevertheless, biomechanical measures of the aneurysmal abdominal aorta have long been implicated in AAA risk of rupture. The purpose of this study is to assess whether other geometric characteristics, in addition to maximum diameter, may be highly correlated with the AAA peak wall stress (PWS). Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis using an isotropic constitutive material for the AAA wall. PWS, evaluated as the spatial maximum of the first principal stress, was calculated at a systolic pressure of 120 mmHg. The models were also used to calculate 47 geometric indices characteristic of the aneurysm geometry. Statistical analyses were conducted using a feature reduction algorithm in which the 47 indices were reduced to 11 based on their statistical significance in differentiating the models in the population (p < 0.05). A subsequent discriminant analysis was performed and 7 of these indices were identified as having no error in discriminating the AAA models with a significant nonlinear regression correlation with PWS. These indices were: Dmax (maximum diameter), T (tortuosity), DDr (maximum diameter to neck diameter ratio), S (wall surface area), Kmedian (median of the Gaussian surface curvature), Cmax (maximum lumen compactness), and Mmode (mode of the Mean surface curvature). Therefore, these characteristics of an individual AAA geometry are the highest correlated with the most clinically relevant biomechanical parameter for rupture risk assessment. We conclude that the indices can serve as surrogates of PWS in lieu of a finite element modeling approach for AAA biomechanical evaluation.

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 5.5 cm. 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.

The importance of patient-specific regionally varying wall thickness in abdominal aortic aneurysm biomechanics.

Abdominal aortic aneurysm (AAA) is a vascular condition where the use of a biomechanics-based assessment for patient-specific risk assessment is a promising approach for clinical management of the disease. Among various factors that affect such assessment, AAA wall thickness is expected to be an important factor. However, regionally varying patient-specific wall thickness has not been incorporated as a modeling feature in AAA biomechanics. To the best our knowledge, the present work is the first to incorporate patient-specific variable wall thickness without an underlying empirical assumption on its distribution for AAA wall mechanics estimation. In this work, we present a novel method for incorporating regionally varying wall thickness (the "PSNUT" modeling strategy) in AAA finite element modeling and the application of this method to a diameter-matched cohort of 28 AAA geometries to assess differences in wall mechanics originating from the conventional assumption of a uniform wall thickness. For the latter, we used both a literature-derived population average wall thickness (1.5 mm; the "UT" strategy) as well as the spatial average of our patient-specific variable wall thickness (the "PSUT" strategy). For the three different wall thickness modeling strategies, wall mechanics were assessed by four biomechanical parameters: the spatial maxima of the first principal stress, strain, strain-energy density, and displacement. A statistical analysis was performed to address the hypothesis that the use of any uniform wall thickness model resulted in significantly different biomechanical parameters compared to a patient-specific regionally varying wall thickness model. Statistically significant differences were obtained with the UT modeling strategy compared to the PSNUT strategy for the spatial maxima of the first principal stress (p = 0.002), strain (p = 0.0005), and strain-energy density (p = 7.83 e-5) but not for displacement (p = 0.773). Likewise, significant differences were obtained comparing the PSUT modeling strategy with the PSNUT strategy for the spatial maxima of the first principal stress (p = 9.68 e-7), strain (p = 1.03 e-8), strain-energy density (p = 9.94 e-8), and displacement (p = 0.0059). No significant differences were obtained comparing the UT and PSUT strategies for the spatial maxima of the first principal stress (p = 0.285), strain (p = 0.152), strain-energy density (p = 0.222), and displacement (p = 0.0981). This work strongly recommends the use of patient-specific regionally varying wall thickness derived from the segmentation of abdominal computed tomography (CT) scans if the AAA finite element analysis is focused on estimating peak biomechanical parameters, such as stress, strain, and strain-energy density.

Fluid-structure interaction modeling of abdominal aortic aneurysms: the impact of patient-specific inflow conditions and fluid/solid coupling.

Rupture risk assessment of abdominal aortic aneurysms (AAA) by means of biomechanical analysis is a viable alternative to the traditional clinical practice of using a critical diameter for recommending elective repair. However, an accurate prediction of biomechanical parameters, such as mechanical stress, strain, and shear stress, is possible if the AAA models and boundary conditions are truly patient specific. In this work, we present a complete fluid-structure interaction (FSI) framework for patient-specific AAA passive mechanics assessment that utilizes individualized inflow and outflow boundary conditions. The purpose of the study is two-fold: (1) to develop a novel semiautomated methodology that derives velocity components from phase-contrast magnetic resonance images (PC-MRI) in the infrarenal aorta and successfully apply it as an inflow boundary condition for a patient-specific fully coupled FSI analysis and (2) to apply a one-way-coupled FSI analysis and test its efficiency compared to transient computational solid stress and fully coupled FSI analyses for the estimation of AAA biomechanical parameters. For a fully coupled FSI simulation, our results indicate that an inlet velocity profile modeled with three patient-specific velocity components and a velocity profile modeled with only the axial velocity component yield nearly identical maximum principal stress (σ1), maximum principal strain (ε1), and wall shear stress (WSS) distributions. An inlet Womersley velocity profile leads to a 5% difference in peak σ1, 3% in peak ε1, and 14% in peak WSS compared to the three-component inlet velocity profile in the fully coupled FSI analysis. The peak wall stress and strain were found to be in phase with the systolic inlet flow rate, therefore indicating the necessity to capture the patient-specific hemodynamics by means of FSI modeling. The proposed one-way-coupled FSI approach showed potential for reasonably accurate biomechanical assessment with less computational effort, leading to differences in peak σ1, ε1, and WSS of 14%, 4%, and 18%, respectively, compared to the axial component inlet velocity profile in the fully coupled FSI analysis. The transient computational solid stress approach yielded significantly higher differences in these parameters and is not recommended for accurate assessment of AAA wall passive mechanics. This work demonstrates the influence of the flow dynamics resulting from patient-specific inflow boundary conditions on AAA biomechanical assessment and describes methods to evaluate it through fully coupled and one-way-coupled fluid-structure interaction analysis.

Assessing the impact of distal protection filter design characteristics on 30-day outcomes of carotid artery stenting procedures.

OBJECTIVE: This study aims to review retrospectively the records of patients treated with carotid artery stenting (CAS) to investigate the potential correlations between clinical variables, distal protection filter (DPF) type and characteristics, and 30-day peri-/postprocedural outcomes. METHODS: This is a multicenter, single-arm, nonrandomized retrospective study of patients who underwent filter-protected CAS in the Pittsburgh, Pennsylvania, region between July 2000 and May 2011. Analysis of peri-/postprocedural complications included myocardial infarction, transient ischemic attacks (TIA), stroke, death, and a composition of all adverse events (AEs). Filter characteristics for Accunet (Abbott Vascular, Santa Clara, Calif; n = 429 [58.8%]), Angioguard (Cordis Endovascular, Miami Lakes, Fla; n = 114 [15.6%]), FilterWire (Boston Scientific, Natick, Mass; n = 113 [15.5%]), Spider (ev3 Endovascular, Plymouth, Minn; n = 45 [6.2%]), and Emboshield (Abbott Vascular; n = 24 [3.3%]) were previously determined in vitro and were used to find correlations with CAS procedural outcomes. Both univariate and multivariate analyses were performed, as well as goodness-of-fit tests to find multivariate correlations with procedural outcomes. RESULTS: In total, 731 CAS procedures using six different DPFs were analyzed. Peri-/postprocedural AEs included 19 TIAs (2.6%), 38 strokes (5.2%), one myocardial infarction (0.1%), 19 deaths (3.6%), and a total of 61 patients with complications (8.3%). Univariate analysis for filter design characteristics showed that the composite of AE was negatively associated with both vascular resistance (P = .01) and eccentricity (P = .02) and was positively associated with porosity (P = .0007), number of pores (P = .005), and pore density (P = .001). Multivariate analysis and the goodness-of-fit test revealed that patients with a history of congestive heart failure, stroke, and TIA (each with odds ratio >1) led to a good-fit model P value of .72 for peri-/postprocedural AEs. Multivariate analysis was inconclusive for all filter design characteristics. CONCLUSIONS: The following filter design characteristics are independently significant for minimizing peri-/postprocedural AEs: higher vascular resistance, concentric in shape, greater capture efficiency, lower porosity, lower number of pores, and lower pore density. Lower porosity and smaller wall apposition were also found to be independently significant for minimization of peri-/postprocedural TIAs. This information can be used when considering the desirable design characteristics of future DPFs.).

Surface curvature as a classifier of abdominal aortic aneurysms: a comparative analysis.

An abdominal aortic aneurysm (AAA) carries one of the highest mortality rates among vascular diseases when it ruptures. To predict the role of surface curvature in rupture risk assessment, a discriminatory analysis of aneurysm geometry characterization was conducted. Data was obtained from 205 patient-specific computed tomography image sets corresponding to three AAA population subgroups: patients under surveillance, those that underwent elective repair of the aneurysm, and those with an emergent repair. Each AAA was reconstructed and their surface curvatures estimated using the biquintic Hermite finite element method. Local surface curvatures were processed into ten global curvature indices. Statistical analysis of the data revealed that the L2-norm of the Gaussian and Mean surface curvatures can be utilized as classifiers of the three AAA population subgroups. The application of statistical machine learning on the curvature features yielded 85.5% accuracy in classifying electively and emergent repaired AAAs, compared to a 68.9% accuracy obtained by using maximum aneurysm diameter alone. Such combination of non-invasive geometric quantification and statistical machine learning methods can be used in a clinical setting to assess the risk of rupture of aneurysms during regular patient follow-ups.

The association of wall mechanics and morphology: a case study of abdominal aortic aneurysm growth.

The purpose of this study is to evaluate the potential correlation between peak wall stress (PWS) and abdominal aortic aneurysm (AAA) morphology and how it relates to aneurysm rupture potential. Using in-house segmentation and meshing software, six 3-dimensional (3D) AAA models from a single patient followed for 28 months were generated for finite element analysis. For the AAA wall, both isotropic and anisotropic materials were used, while an isotropic material was used for the intraluminal thrombus (ILT). These models were also used to calculate 36 geometric indices characteristic of the aneurysm morphology. Using least squares regression, seven significant geometric features (p < 0.05) were found to characterize the AAA morphology during the surveillance period. By means of nonlinear regression, PWS estimated with the anisotropic material was found to be highly correlated with three of these features: maximum diameter (r = 0.992, p = 0.002), sac volume (r = 0.989, p = 0.003) and diameter to diameter ratio (r = 0.947, p = 0.033). The correlation of wall mechanics with geometry is nonlinear and reveals that PWS does not increase concomitantly with aneurysm diameter. This suggests that a quantitative characterization of AAA morphology may be advantageous in assessing rupture risk.

Challenging neck anatomy is associated with need for intraoperative endovascular adjuncts during endovascular aortic aneurysm repair (EVAR).

BACKGROUND: The purpose of this study was to determine which proximal seal zone characteristics were predictive of early and late type Ia endoleak development after endovascular aortic aneurysm repair (EVAR) for infrarenal abdominal aortic aneurysmal disease. METHODS: We evaluated 146 patients who underwent EVAR between January 2006 and March 2007. In the cohort, high-resolution computed tomography images of 100 (68.5%) patients were available, which showed detailed measurement of proximal neck parameters, including diameter, length, calcification, thrombus, suprarenal and infrarenal angles, and reverse taper morphology. Postprocessing of digital data sets was performed to obtain centerline-of-flow measurements. Relevant medical records and follow-up computed tomography scans were reviewed. RESULTS: Mean age of the patients was 72.7 years, with 78% being male. Of these patients, 66% did not satisfy the instructions for use for the Zenith EVAR device, and 50% did not satisfy the instructions for use for the AneuRx device. Nine patients had intraoperative type Ia endoleaks. A 100% assisted primary technical success rate was achieved with the adjunctive use of angioplasty (n = 4), uncovered stent (n = 3), and extension cuff (n = 2) placement. There was a significant association between type Ia endoleak development and magnitude of the infrarenal angle (p < 0.01); however, other parameters were not significant. At follow-up (mean, 587 days), no patient had a type Ia endoleak, and there were no aneurysm-related deaths. CONCLUSIONS: Our data indicate that infrarenal angle is related to intraoperative type Ia endoleak occurrence, but other factors often thought to be indicative of adverse neck anatomy are not significant predictors. Moreover, all type Ia endoleaks in this cohort were successfully eliminated intraoperatively, and durability was confirmed on postoperative surveillance. These data demonstrate that challenging neck anatomy is associated with the need for intraoperative endovascular adjuncts, and that effective and durable aneurysm exclusion should still be expected.

The effect of immunosuppression on lower extremity arterial bypass outcomes.

BACKGROUND: Myointimal hyperplasia is a pathologic result of the body's natural inflammatory response to injury of the blood vessels and a leading cause of peripheral arterial bypass failure. Because immunosuppressive agents are known to abate inflammation, we hypothesized the superior outcome of lower extremity bypass in renal transplant recipients compared with the hemodialysis population. METHODS: The vascular surgery registry at a single tertiary care center was retrospectively reviewed to identify patients who underwent lower extremity bypass procedures. All patients with a history of renal transplantation were selected for analysis. A consecutive group of bypass patients with dialysis-dependent renal failure was selected as a control cohort. The primary endpoint was amputation-free survival. RESULTS: Vascular reconstruction for chronic peripheral vascular disease yielded an amputation-free survival rate of 82% at 1 year for the those in the control group as compared with only 22% in the those with a history of renal transplantation (p = 0.02), which corresponded exactly with primary patency at 1 year. Patients were operated on for severe claudication (n = 1), rest pain (n = 1), and tissue loss (n = 17). There was no difference between the groups with regard to indication for operation or comorbid conditions. CONCLUSIONS: These data suggest a deleterious effect of immunosuppression on outcome of lower extremity bypass procedures at the doses required to prevent allograft rejection. This finding, which has been scarcely reported, underscores the importance of peripheral vascular disease screening in the transplant population and early intervention when clinically indicated.

Quantitative assessment of abdominal aortic aneurysm geometry.

Recent studies have shown that the maximum transverse diameter of an abdominal aortic aneurysm (AAA) and expansion rate are not entirely reliable indicators of rupture potential. We hypothesize that aneurysm morphology and wall thickness are more predictive of rupture risk and can be the deciding factors in the clinical management of the disease. A non-invasive, image-based evaluation of AAA shape was implemented on a retrospective study of 10 ruptured and 66 unruptured aneurysms. Three-dimensional models were generated from segmented, contrast-enhanced computed tomography images. Geometric indices and regional variations in wall thickness were estimated based on novel segmentation algorithms. A model was created using a J48 decision tree algorithm and its performance was assessed using ten-fold cross validation. Feature selection was performed using the χ2-test. The model correctly classified 65 datasets and had an average prediction accuracy of 86.6% (κ=0.37). The highest ranked features were sac length, sac height, volume, surface area, maximum diameter, bulge height, and intra-luminal thrombus volume. Given that individual AAAs have complex shapes with local changes in surface curvature and wall thickness, the assessment of AAA rupture risk should be based on the accurate quantification of aneurysmal sac shape and size.

Endoluminal treatment of type IA endoleak with Onyx.

Type IA endoleaks associated with endovascular aortic aneurysm repair are typically treated with endovascular adjuncts. Technical failure results when such maneuvers are unsuccessful, and endograft removal may, unfortunately, become necessary. The novel management of a recalcitrant type IA endoleak using the artificial embolization device, Onyx (Micro Therapeutics Inc, Irvine, Calif) is presented for the case of a nonagenarian with prohibitive surgical risk after conventional techniques had failed.

Three-dimensional geometrical characterization of abdominal aortic aneurysms: image-based wall thickness distribution.

The clinical assessment of abdominal aortic aneurysm (AAA) rupture risk is based on the quantification of AAA size by measuring its maximum diameter from computed tomography (CT) images and estimating the expansion rate of the aneurysm sac over time. Recent findings have shown that geometrical shape and size, as well as local wall thickness may be related to this risk; thus, reliable noninvasive image-based methods to evaluate AAA geometry have a potential to become valuable clinical tools. Utilizing existing CT data, the three-dimensional geometry of nine unruptured human AAAs was reconstructed and characterized quantitatively. We propose and evaluate a series of 1D size, 2D shape, 3D size, 3D shape, and second-order curvature-based indices to quantify AAA geometry, as well as the geometry of a size-matched idealized fusiform aneurysm and a patient-specific normal abdominal aorta used as controls. The wall thickness estimation algorithm, validated in our previous work, is tested against discrete point measurements taken from a cadaver tissue model, yielding an average relative difference in AAA wall thickness of 7.8%. It is unlikely that any one of the proposed geometrical indices alone would be a reliable index of rupture risk or a threshold for elective repair. Rather, the complete geometry and a positive correlation of a set of indices should be considered to assess the potential for rupture. With this quantitative parameter assessment, future research can be directed toward statistical analyses correlating the numerical values of these parameters with the risk of aneurysm rupture or intervention (surgical or endovascular). While this work does not provide direct insight into the possible clinical use of the geometric parameters, we believe it provides the foundation necessary for future efforts in that direction.

Wall stress and flow dynamics in abdominal aortic aneurysms: finite element analysis vs. fluid-structure interaction.

Abdominal aortic aneurysm (AAA) rupture is the clinical manifestation of an induced force exceeding the resistance provided by the strength of the arterial wall. This force is most frequently assumed to be the product of a uniform luminal pressure acting along the diseased wall. However fluid dynamics is a known contributor to the pathogenesis of AAAs, and the dynamic interaction of blood flow and the arterial wall represents the in vivo environment at the macro-scale. The primary objective of this investigation is to assess the significance of assuming an arbitrary estimated peak fluid pressure inside the aneurysm sac for the evaluation of AAA wall mechanics, as compared with the non-uniform pressure resulting from a coupled fluid-structure interaction (FSI) analysis. In addition, a finite element approach is utilised to estimate the effects of asymmetry and wall thickness on the wall stress and fluid dynamics of ten idealised AAA models and one non-aneurysmal control. Five degrees of asymmetry with uniform and variable wall thickness are used. Each was modelled under a static pressure-deformation analysis, as well as a transient FSI. The results show that the inclusion of fluid flow yields a maximum AAA wall stress up to 20% higher compared to that obtained with a static wall stress analysis with an assumed peak luminal pressure of 117 mmHg. The variable wall models have a maximum wall stress nearly four times that of a uniform wall thickness, and also increasing with asymmetry in both instances. The inclusion of an axial stretch and external pressure to the computational domain decreases the wall stress by 17%.