Comparison of existing aneurysm models and their path forward

The two most important aneurysm types are cerebral aneurysms (CA) and abdominal aortic aneurysms (AAA), accounting together for over 80\% of all fatal aneurysm incidences. To minimise aneurysm related deaths, clinicians require various tools to accurately estimate its rupture risk. For both aneurysm types, the current state-of-the-art tools to evaluate rupture risk are identified and evaluated in terms of clinical applicability. We perform a comprehensive literature review, using the Web of Science database. Identified records (3127) are clustered by modelling approach and aneurysm location in a meta-analysis to quantify scientific relevance and to extract modelling patterns and further assessed according to PRISMA guidelines (179 full text screens). Beside general differences and similarities of CA and AAA, we identify and systematically evaluate four major modelling approaches on aneurysm rupture risk: finite element analysis and computational fluid dynamics as deterministic approaches and machine learning and assessment-tools and dimensionless parameters as stochastic approaches. The latter score highest in the evaluation for their potential as clinical applications for rupture prediction, due to readiness level and user friendliness. Deterministic approaches are less likely to be applied in a clinical environment because of their high model complexity. Because deterministic approaches consider underlying mechanism for aneurysm rupture, they have improved capability to account for unusual patient-specific characteristics, compared to stochastic approaches. We show that an increased interdisciplinary exchange between specialists can boost comprehension of this disease to design tools for a clinical environment. By combining deterministic and stochastic models, advantages of both approaches can improve accessibility for clinicians and prediction quality for rupture risk.Comment: 46 pages, 5 figure

IMPACT OF HEMODYNAMIC VORTEX SPATIAL AND TEMPORAL CHARACTERISTICS ON ANALYSIS OF INTRACRANIAL ANEURYSMS

Subarachnoid hemorrhage is a potentially devastating pathological condition in which bleeding occurs into the space surrounding the brain. One of the prominent sources of subarachnoid hemorrhage are intracranial aneurysms (IA): degenerative, irregular expansions of area(s) of the cerebral vasculature. In the event of IA rupture, the resultant subarachnoid hemorrhage ends in patient mortality occurring in ~50% of cases, with survivors enduring significant neurological damage with physical or cognitive impairment. The seriousness of IA rupture drives a degree of clinical interest in understanding these conditions that promote both the development and possible rupture of the vascular malformations. Current metrics for the assessment of this pathology rely on measuring the geometric characteristics of a patient\u27s vessel and/or IA, as well as the hemodynamic stressors existing along the vessel wall. Comparatively less focus has been granted toward understanding the characteristics of much of the bulk-flow within the vasculature and how it may play a role in IAs. Specifically, swirling hemodynamic flow (vortices) have been suggested as a condition which exacerbates vascular changes leading to IAs, yet quantified measurements of the spatial and temporal characteristics of vortices remain overlooked. This dissertation studies the role of the spatial and temporal characteristics of vortex flow and how it plays a role on IA pathology. Its chapters are a collection of five (5) works into this matter. First, established methods for the identification of vortices was investigated, and a novel method for vortex identification and quantification of their characteristics was developed to overcome the limitations of previous methods. Second, the developed method for vortex identification/quantification was then applied to a simulation study to improve predictive models aimed at predicting areas of IA development from those unlikely to suffer this pathology. Third, assessing how the simulated repair of one IA impacts changes to hemodynamic conditions within other nearby un-repaired IAs in a multiple IA system. Fourth, it was determined if vortex identification/quantification improved predictive models aimed at differentiation ruptured from unruptured IAs. Fifth, impart vortical flow of differing characteristics onto cultured vascular cells to determine if vortex stability imparts varied levels of cellular changes

Conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application

This thesis proposes a novel hybrid methodology that couples computational fluid dynamic (CFD) and data mining (DM) techniques that is applied to a multi-dimensional medical dataset in order to study potential disease development statistically. This approach allows an alternate solution for the present tedious and rigorous CFD methodology being currently adopted to study the influence of geometric parameters on hemodynamics in the human abdominal aortic aneurysm. This approach is seen as a “marriage” between medicine and computer domains

Oscillatory wall strain reduction precedes arterial intimal hyperplasia in a murine model

Cardiovascular diseases (CVD) remain the most common cause of death in the United States. Additionally, peripheral artery disease affects thousands of people each year. A major underlying cause of these diseases is the occlusion of the coronary or peripheral arteries due to arteriosclerosis. To overcome this, a number of vascular interventions have been developed including angioplasty, stenting, endarterectomies and bypass grafts. Although all of these methods are capable of restoring blood flow to the distal organ after occlusion, they are all plagued by unacceptably high restenosis rates. While the biological reactions that occur as a result of each of these methods differ, the initiating factor of both the primary atherosclerosis and subsequent failure of vascular interventions appears to be intimal hyperplasia (IH). Intimal hyperplasia is most simply defined as the expansion of multiple layers of cells internally to the internal elastic lamina of the blood vessel. This excessive cellular growth leads to arterial stenosis, plaque formation and inflammatory reactions. Despite extensive research the underlying factors that cause IH remain unclear. A quantity of research to date has implicated endothelial cell mechanosensation as the mechanism by which IH is initiated with evidence positively correlating wall shear stress with IH. Others, however, have demonstrated that changes in the stresses applied to the wall in vitro can modulate IH independent of hemodynamic shear stress. Thus, relations between wall tensile stress and IH in vivo may shed light on the underlying mechanisms of IH. Since noninvasive measurement of wall tensile stress in vivo is difficult, it is most feasible to measure oscillatory wall strain which is intimately related to wall tensile stress through the mechanical properties of the arterial wall. In this dissertation, we hypothesize that reductions in oscillatory wall strain precede the formation of intimal hyperplasia in a murine model. To test our hypothesis, we first developed a novel, high spatial and temporal resolution method to measure oscillatory wall strains in the murine common carotid artery. We validated this method both in vitro using an arterial phantom and in vivo using a murine model of abdominal aortic aneurysms. To assess relationships between strain and IH, we applied our strain measurement technique to a recently developed mouse model of IH. In this model, a suture is used to create a focal stenosis and reduce flow through the common carotid artery by 85%; resulting in proximal IH formation. Using this approach, we identified a relationship between oscillatory strain reductions and IH. Subsequent analysis demonstrated that early reductions in mechanical strain just 4 days after focal stenosis creation correlate with IH formation nearly 1 month later. Since IH is not expected to form by day 4 in this model, we went on to assess changes in gross vascular morphology at day 4. We discovered that, although strains are significantly reduced by day 4, no significant IH can be observed, suggesting that changes in wall structure are resulting in strain reductions. At day 4 post-op, we observed cellular proliferation and leukocyte recruitment to the wall without intimal hyperplasia. These studies suggest that early reductions in mechanical strain may be an important predictor of IH formation. Clinically, this relation could be important for the development of novel techniques for predicting IH formation before it becomes hemodynamically significant

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

In Vitro and Computational Analyses of Blood Flow at Aortoiliac Bifurcation for Patients with Atherosclerotic Plaque Treated with Endovascular Procedures

This research has developed an appropriate approach allowing for more accurate assessment of haemodynamic changes following implantation of endovascular stent graft to treat patients with occlusive aortoiliac disease. Two different endovascular techniques involving the use of different types of stent grafts were analysed and compared with regard to haemodynamics associated with these techniques. Results improved understanding of the flow characteristics of these endovascular techniques

Computational fluid dynamics indicators to improve cardiovascular pathologies

In recent years, the study of computational hemodynamics within anatomically complex vascular regions has generated great interest among clinicians. The progress in computational fluid dynamics, image processing and high-performance computing haveallowed us to identify the candidate vascular regions for the appearance of cardiovascular diseases and to predict how this disease may evolve. Medicine currently uses a paradigm called diagnosis. In this thesis we attempt to introduce into medicine the predictive paradigm that has been used in engineering for many years. The objective of this thesis is therefore to develop predictive models based on diagnostic indicators for cardiovascular pathologies. We try to predict the evolution of aortic abdominal aneurysm, aortic coarctation and coronary artery disease in a personalized way for each patient. To understand how the cardiovascular pathology will evolve and when it will become a health risk, it is necessary to develop new technologies by merging medical imaging and computational science. We propose diagnostic indicators that can improve the diagnosis and predict the evolution of the disease more efficiently than the methods used until now. In particular, a new methodology for computing diagnostic indicators based on computational hemodynamics and medical imaging is proposed. We have worked with data of anonymous patients to create real predictive technology that will allow us to continue advancing in personalized medicine and generate more sustainable health systems. However, our final aim is to achieve an impact at a clinical level. Several groups have tried to create predictive models for cardiovascular pathologies, but they have not yet begun to use them in clinical practice. Our objective is to go further and obtain predictive variables to be used practically in the clinical field. It is to be hoped that in the future extremely precise databases of all of our anatomy and physiology will be available to doctors. These data can be used for predictive models to improve diagnosis or to improve therapies or personalized treatments.En els últims anys, l'estudi de l'hemodinàmica computacional en regions vasculars anatòmicament complexes ha generat un gran interès entre els clínics. El progrés obtingut en la dinàmica de fluids computacional, en el processament d'imatges i en la computació d'alt rendiment ha permès identificar regions vasculars on poden aparèixer malalties cardiovasculars, així com predir-ne l'evolució. Actualment, la medicina utilitza un paradigma anomenat diagnòstic. En aquesta tesi s'intenta introduir en la medicina el paradigma predictiu utilitzat des de fa molts anys en l'enginyeria. Per tant, aquesta tesi té com a objectiu desenvolupar models predictius basats en indicadors de diagnòstic de patologies cardiovasculars. Tractem de predir l'evolució de l'aneurisma d'aorta abdominal, la coartació aòrtica i la malaltia coronària de forma personalitzada per a cada pacient. Per entendre com la patologia cardiovascular evolucionarà i quan suposarà un risc per a la salut, cal desenvolupar noves tecnologies mitjançant la combinació de les imatges mèdiques i la ciència computacional. Proposem uns indicadors que poden millorar el diagnòstic i predir l'evolució de la malaltia de manera més eficient que els mètodes utilitzats fins ara. En particular, es proposa una nova metodologia per al càlcul dels indicadors de diagnòstic basada en l'hemodinàmica computacional i les imatges mèdiques. Hem treballat amb dades de pacients anònims per crear una tecnologia predictiva real que ens permetrà seguir avançant en la medicina personalitzada i generar sistemes de salut més sostenibles. Però el nostre objectiu final és aconseguir un impacte en l¿àmbit clínic. Diversos grups han tractat de crear models predictius per a les patologies cardiovasculars, però encara no han començat a utilitzar-les en la pràctica clínica. El nostre objectiu és anar més enllà i obtenir variables predictives que es puguin utilitzar de forma pràctica en el camp clínic. Es pot preveure que en el futur tots els metges disposaran de bases de dades molt precises de tota la nostra anatomia i fisiologia. Aquestes dades es poden utilitzar en els models predictius per millorar el diagnòstic o per millorar teràpies o tractaments personalitzats.Postprint (published version