Geometric, biomechanical and molecular analyses of abdominal aortic aneurysm

Background Abdominal aortic aneurysm (AAA) is defined as a dilatation of the abdominal aorta of 30 mm in diameter or more. Main risk factors are smoking, age and male sex. Pathophysiological features include inflammation, smooth muscle cell loss and destruction of the extracellular matrix. The AAA is typically asymptomatic but can expand and eventually rupture, with a mortality of 70-80% as a result. Risk factors for rupture include a large diameter, female sex, active smoking, high blood pressure and low body mass index (BMI). There is no medical treatment to inhibit growth or rupture of AAA. The only measure to prevent rupture in a large AAA is aortic surgery. This intervention carries its own significant risk of morbidity and mortality, necessitating a risk stratification method. The diameter is currently used to decide when to operate on an AAA and it is repeatedly monitored until the threshold for surgery is reached. However, this measurement leaves room for improvement, as the individual aneurysm growth rate is difficult to predict and some large AAAs do not rupture while in other patients, small AAAs rupture during surveillance. Finite element analysis (FEA) is a method by which biomechanical rupture risk can be estimated based on patient characteristics and a computed tomography (CT)-derived 3D model of an AAA. Microarray analysis allows high-throughput analyses of tissue gene expression. Aims The overall aim of this thesis was to explore and develop new strategies to improve, refine and individualize management of patients with AAA, by applying geometric, biomechanical and molecular analyses. Methods and Results In study I, the CTs of 146 patients with AAAs of diameters between 40 and 60 mm were analyzed with three-dimensional (3D) segmentation and FEA. Simple and multiple regression analyses were performed. Female sex, patient height, lumen volume, body surface area (BSA) and low BMI were shown to be associated with the biomechanical rupture risk of AAA. Study II included 191 patients with AAAs of diameters between 40-50 mm. The AAAs were analyzed with 3D segmentation and FEA after which prediction algorithms were developed by use of machine learning strategies. More precise diameter measurements improved prediction of growth and four-year prognosis of small AAAs. Biomechanical indices and lumen diameter were predictive of future rupture or symptomatic AAA. Growth and rupture required different prediction models. In study III, 37 patients, 42 controls and a validation cohort of 51 patients were analyzed with respect to their circulating levels of neutrophil elastase-derived fibrin degradation products (E-XDP). The results showed that E-XDP was a sensitive marker for AAA, independently of examined comorbidities, and its concentration in peripheral blood correlated with the AAA diameter and the volume and mechanical stress of the intraluminal thrombus (ILT). It was further increased by the presence of coexisting aneurysms. Study IV included 246 tissue samples, divided into tunica media and adventitia, from 76 patients with AAA and 13 organ donor controls, analyzed by microarrays. There were large differences between the transcriptomes of AAA and control media and adventitia. Processes related to inflammation were transmural, whereas the upregulation of proteolysis, angiogenesis and apoptosis along with downregulation of smooth muscle- and differentiation-related gene sets were specific for the aneurysm media. Active smoking increased oxidative stress in all tissues and increased inflammation and lipid-related processes in AAA. The growth rate of the AAA diameter correlated with adaptive immunity in media and lipid processes in adventitia. Conclusions In this thesis, we show that known clinical risk factors and certain geometric properties are associated with biomechanical deterioration of AAAs. Furthermore, geometric and biomechanical analyses can enhance prediction of outcome. Importantly, there are differences between prediction of AAA growth and rupture. Finally, a biomarker was discovered and the transcriptome of AAA including effects of the ILT, smoking and rapid diameter growth rate, was mapped and we envision that the data may be used for future biomarker and drug target discovery

Morphologic evaluation of ruptured abdominal aortic aneurysm by 3D modeling

This thesis was created in Word and converted to PDF using Mac OS X 10.7.5 Quartz PDFContext.Abdominal aortic aneurysm (AAA) is defined as a dilatation of the abdominal aorta exceeding the normal diameter by more than 50%. The standard and widely used approach to assess AAA size is by measuring the maximal diameter (Dmax). Currently, the main predictors of rupture risk are the Dmax, sex, and the expansion rate of the aneurysm. Yet, Dmax has some limitations. AAAs of vastly different shapes may have the same maximal diameter. Dmax lacks sensitivity for rupture risk, especially among smaller AAAs. Thus, there is a need to evaluate the susceptibility of a given AAA to rupture on a patient-specific basis. We present the design concept and workflow of the AAA segmentation software developed at our institution. We describe the previous validation steps in which we evaluated the reproducibility of manual Dmax, compared software Dmax against manual Dmax, validated reproducibility of software Dmax and volume in cross-sectional and longitudinal studies for detection of AAA growth, and evaluated the reproducibility of software measurements in unenhanced computed tomographic angiography (CTA) and in the presence of stent-graft. In order to define new geometric features associated with rupture, we performed a case-control study in which we compared 63 cases with ruptured or symptomatic AAA and 94 controls with asymptomatic AAA. Univariate logistic regression analysis revealed 14 geometric indices associated with AAA rupture. In the multivariate logistic regression analysis, adjusting for Dmax and sex, the AAA with a higher bulge location and higher mean averaged surface area were associated with AAA rupture. Our preliminary results suggest that incorporating geometrical indices obtained by segmentation of CT shows a trend toward improvement of the classification accuracy of AAA with high rupture risk at CT over a traditional model based on Dmax and sex alone. Larger longitudinal studies are needed to verify the validity of the proposed model. Addition of flow and biomechanical simulations should be investigated to improve rupture risk prediction based on AAA modeling.Un anévrysme de l'aorte abdominale (AAA) est défini par une dilatation de plus de 50% par rapport au diamètre normal. La méthode standard et largement répandue pour mesurer la dimension d'un AAA consiste à mesurer le diamètre maximal (Dmax). Présentement, les principaux prédicteurs de risque de rupture sont le Dmax, le sexe et le taux d'expansion d'un anévrysme. Toutefois, le Dmax a certaines limitations. Des AAAs de formes très différentes peuvent avoir le même diamètre maximal. Le Dmax manque de sensibilité pour détecter le risque de rupture, en particulier pour les petits anévrysmes. Par conséquent, il y a un besoin d'évaluer de manière spécifique et individuelle la susceptibilité de rupture d'un AAA. Nous présentons le concept et le flux de travail d'un logiciel de segmentation des AAAs développé à notre institution. Nous décrivons les étapes antérieures de validation: évaluation de la reproductibilité du Dmax manuel, comparaison de Dmax par logiciel avec Dmax manuel, validation de la reproductibilité du Dmax et volume par logiciel dans des études transversale et longitudinale pour la détection de croissance et évaluation de la reproductibilité de mesures sur angiographie par tomodensitométrie et en présence d'endoprothèse. En vue d’identifier de nouveaux paramètres géométrique associés avec le risque de rupture, nous avons réalisé une étude cas-témoin comparant 63 cas avec AAA rompu ou symptomatique et 94 contrôles avec AAA asymptomatique. Une analyse de régression logistique univariée a identifié 14 indices géométriques associés avec une rupture de AAA. Dans l'analyse de régression logistique multivariée, en ajustant pour le Dmax et le sexe, les AAA avec un bombement plus haut situé et une surface moyenne plus élevée étaient associés à une rupture. Nos résultats préliminaires suggèrent que l'inclusion d'indices géométriques obtenus par segmentation de tomodensitométrie tend à améliorer la classification de AAA avec un risque de rupture par rapport à un modèle traditionnel seulement basé sur le Dmax et le sexe. De plus larges études longitudinales sont requises pour vérifier la validité du modèle proposé. Des simulations de flux et biomécaniques devraient être envisagées pour améliorer la prédiction du risque de rupture basée sur la modélisation d'anévrysmes

Computational analysis of flow and stress patterns in thoracic aortic aneurysms : a patient specific study

Imperial Users onl

A Systematic Review and Discussion of the Clinical Potential

Funding Information: Funding by Portuguese Foundation for Science and Technology (FCT-MCTES) under the following projects: PTDC/EMD-EMD/1230/2021—Fluid-structure interaction for functional assessment of ascending aortic aneurysms: a biomechanical-based approach toward clinical practice ; UNIDEMI UIDB/00667/2020; A. Mourato PhD grant UI/BD/151212/2021; R. Valente PhD grant 2022.12223.BD. Publisher Copyright: © 2022 by the authors.Aortic aneurysm is a cardiovascular disease related to the alteration of the aortic tissue. It is an important cause of death in developed countries, especially for older patients. The diagnosis and treatment of such pathology is performed according to guidelines, which suggest surgical or interventional (stenting) procedures for aneurysms with a maximum diameter above a critical threshold. Although conservative, this clinical approach is also not able to predict the risk of acute complications for every patient. In the last decade, there has been growing interest towards the development of advanced in silico aortic models, which may assist in clinical diagnosis, surgical procedure planning or the design and validation of medical devices. This paper details a comprehensive review of computational modelling and simulations of blood vessel interaction in aortic aneurysms and dissection, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). In particular, the following questions are addressed: “What mathematical models were applied to simulate the biomechanical behaviour of healthy and diseased aortas?” and “Why are these models not clinically implemented?”. Contemporary evidence proves that computational models are able to provide clinicians with additional, otherwise unavailable in vivo data and potentially identify patients who may benefit from earlier treatment. Notwithstanding the above, these tools are still not widely implemented, primarily due to low accuracy, an extensive reporting time and lack of numerical validation.publishersversionpublishe

Automatic classification and 3D visualisation of abdominal aortic aneurysms to predict aneurysm expansion

Abdominal aortic aneurysms (AAA) are a major cause of death in men above the age of 65 in the western world. Currently decisions for AAA management are based on the size of maximum AAA diameter (>5.5cm), measured using ultrasound imaging. However, as a proportion of AAAs rupture whilst still below this diameter threshold, while larger AAAs may never rupture, better methods for AAA expansion and rupture prediction are required. Previous research suggested that the presence of “hotspots” (focal areas) of inflammation as detected with USPIO-enhanced MRI may have potential in identifying faster-growing AAAs. However, the identification of these USPIO “hotspots” had been up to this point restricted to manual processing of the MRI data in a time-consuming and laborious slice-by-slice method, which only used 2D information. Inter- and intra- observer variability were an issue, as well as the use of empirically-defined signal thresholds which were dependent on each acquisition protocol. The work presented in this thesis aimed to evaluate current methodologies for AAA assessment and growth prediction and to contribute to improved prediction models by introducing novel techniques. Ultrasound was found to under-measure AAA size and the use of maximum AAA diameter was found to be problematic, especially for growth calculations. Automatically calculated alternatives which account for the total size and shape of the AAA, as measured with MRI, were introduced for more reproducible measurements. Furthermore, automation and standardisation of the previously-employed manual methods for hotspot detection and AAA classification were achieved, with the development of an efficient algorithm with excellent agreement levels. Taken a step further, two improved algorithms were introduced, adaptive to the data and USPIO distribution of individual AAAs and eliminating the universal threshold previously used. These algorithms incorporated information on 3D USPIO distribution along the length of the AAAs to detect and visualise 3D hotspots of inflammation for the first time. Novel 2D and 3D metrics were introduced, while the algorithms were also incorporated into a GUI for ease of clinical use. Additional aneurysm metrics automatically derived by the algorithms were incorporated into multiple linear regression models to investigate prediction of AAA growth rate. This investigation introduced three significant predictors which have not been used in previous predictive models of AAA expansion: the “mean thrombus major axis” metric, which reflected baseline size of AAA throughout multiple axial slices of the AAA; the “eccentricity WT” metric which reflected the relationship between wall shape and thrombus; and the presence of “3D hotspots” which may potentially reflect transported USPIO within a network of vascular channels along the length of the aneurysm. In line with previous literature, family history of AAA and high diastolic BP were also found to be significant predictors, but larger cohorts are needed for more reliable assessment of the predictive models suggested in this thesis

Predicting the risk of progression in patients with thoracic aortic aneurysm

This body of work has examined a range of easily quantifiable circulating biomarkers in a cohort of 50 patients with aneurysms of the arch and descending thoracic aorta. I have described within this thesis various obstacles (logistical and biological) to identifying biomarkers of aneurysm progression. Despite these challenges, I have created a unique dataset quantifying plasma proteins and PBMC-RNAs in a cohort of 50 AD patients with measurements of growth or clinical progression over 2 years. Analysis of my dataset indicates that: a) aortic size alone is not a good predictor of growth b) prediction of future growth can be improved by combining plasma protein measurements with aortic size: dAoD/dT = 0.43(baseline indexed AoD) -1.5(logTGFB1) -0.86(logIL2) dAoV/dT = 1.57(baseline indexed AoD) +0.01(logTGFB1) -22.5(logIL2) c) wall stress estimations appear to provide even more robust predictions of future growth d) PBMC-RNA profiling could be useful as a diagnostic tool to detect patients with AD aneurysms

Assessment of abdominal aortic aneurysm biology using magnetic resonance imaging and positron emission tomography-computed tomography.

Background Although abdominal aortic aneurysm (AAA) growth is non-linear, serial measurements of aneurysm diameter are the mainstay of aneurysm surveillance and contribute to decisions on timing of intervention. Aneurysm biology plays a key part in disease evolution but is not currently routinely assessed in clinical practice. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography-Computed Tomography (PET-CT) provide insight into disease processes on a cellular or molecular level, and represent exciting new imaging biomarkers of disease activity. Macrophage-mediated inflammation may be assessed using ultrasmall superparamagnetic particles of iron oxide (USPIO) MRI and the PET radiotracer 18FSodium Fluoride (18F-NaF) identifies microcalcification which is a response to underlying necrotic inflammation. The central aim of this thesis was to investigate these imaging modalities in patients with AAA. Methods and Results USPIO MRI: MULTI-CENTRE STUDY In a prospective multi-centre observational cohort study, 342 patients (85.4% male, mean age 73.1±7.2 years, mean AAA diameter 49.6±7.7mm) with asymptomatic AAA ≥4 cm anteroposterior diameter underwent MRI before and 24-36 hours after intravenous administration of USPIO. Colour maps (depicting the change in T2* caused by USPIO) were used to classify aneurysms on the basis of the presence of USPIO uptake in the aneurysm wall, representing mural inflammation. Intra- and inter-observer agreement were found to be very good, with proportional agreement of 0.91 (kappa 0.82) and 0.83 (kappa 0.66), respectively. At 1 year, there was 29.3% discordant classification of aneurysms on repeated USPIO MRI and at 2 years, discordance was 65%, suggesting that inflammation evolves over time. In the observational study, after a mean of 1005±280 days of follow up, there were 126 (36.8%) aneurysm repairs and 17 (5.0%) ruptures. Participants with USPIO enhancement (42.7%) had increased aneurysm expansion rates (3·1±2·5 versus 2·5±2·4 mm/year; difference 0·6 [95% confidence intervals (CI), 0·02 to 1·2] mm/year, p=0·0424) and had higher rates of aneurysm rupture or repair (69/146=47·3% versus 68/191=35·6%; difference 11·7%, 95% CI 1·1 to 22·2%, p=0·0308). USPIO MRI was therefore shown to predict AAA expansion and the composite of rupture or repair, however this was not independent of aneurysm diameter (c-statistic, 0·7924 to 0·7926; unconditional net reclassification -13·5%, 95% confidence intervals -36·4% to 9·3%). 18F-NaF PET-CT: SINGLE-CENTRE STUDY A sub-group of 76 patients also underwent 18F-NaF PET-CT, which was evaluated using the maximum tissue-to-background ratio (TBRmax) in the most diseased segment (MDS), a technique that showed very good intra- (ICC 0.70-0.89) and inter-observer (ICC 0.637-0.856) agreement. Aneurysm tracer uptake was compared firstly in a case-control study, with 20 patients matched to 20 control patients for age, sex and smoking status. 18F-NaF uptake was higher in aneurysm when compared to control aorta (log2TBRmax 1.712±0.560 vs. 1.314±0.489; difference 0.398 (95% CI 0.057, 0.739), p=0.023), or to non-aneurysmal aorta in patients with AAA (log2TBRmax 1.647±0.537 vs. 1.332±0.497; difference 0.314 (95% CI 0.0685, 0.560), p=0.004). An ex vivo study was performed on aneurysm and control tissue, which demonstrated that 18F-NaF uptake on microPET-CT was higher in the aneurysm hotspots and higher in aneurysm tissue compared to control tissue. Histological analysis suggested that 18F-NaF was highest in areas of focal calcification and necrosis. In an observational cohort study, aneurysms were stratified by tertiles of TBRmax in the MDS and followed up for 510±196 days, with 6 monthly serial ultrasound measurements of diameter. Those in the highest tertile of tracer uptake expanded more than 2.5 times more rapidly than those in the lowest tertile (3.10 [3.58] mm/year vs. 1.24 [2.41] mm/year, p=0.008) and were also more likely to experience repair or rupture (15.3% vs. 5.6%, log-rank p=0.043). In multivariable analyses, 18F-NaF uptake on PET-CT emerged as an independent predictor of AAA expansion (p=0.042) and rupture or repair (HR 2.49, 95% CI1.07, 5.78; p=0.034), even when adjusted for age, sex, body mass index, systolic blood pressure, current smoking and, crucially, aneurysm diameter. Conclusion These are the largest USPIO MRI and PET-CT studies in AAA disease to date and the first to investigate 18F-NaF. Both USPIO MRI and 18F-NaF PET-CT are able to predict AAA expansion and the composite of rupture and repair, with 18F-NaF PETCT emerging as the first imaging biomarker that independently predicts expansion and AAA events, even after adjustment for aneurysm diameter. This represents an exciting new predictor of disease progression that adds incremental value to standard clinical assessments. Feasibility and randomised clinical trials are now required to assess the potential of this technique to change the management and outcome of patients with AAA