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Indiana University-Purdue University Indianapolis (IUPUI) / Atherosclerotic lesions of human beings are common diagnosed in regions of arte- rial branching and curvature. The prevalence of atherosclerosis is usually associated with hardening and ballooning of aortic wall surfaces because of narrowing of flow path by the deposition of fatty materials, platelets and influx of plasma through in- timal wall of Aorta. High Wall Shear Stress (WSS) is proved to be the main cause behind all these aortic diseases by physicians and researchers. Due to the fact that the atherosclerotic regions are associated with complex blood flow patterns, it has believed that hemodynamics and fluid-structure interaction play important roles in regulating atherogenesis. As one of the most complex flow situations found in cardio- vascular system due to the strong curvature effects, irregular geometry, tapering and branching, and twisting, theoretical prediction and in vivo quantitative experimental data regarding to the complex blood flow dynamics are substantial paucity. In recent years, computational fluid dynamics (CFD) has emerged as a popular research tool to study the characteristics of aortic flow and aim to enhance the understanding of the underlying physics behind arteriosclerosis. In this research, we study the hemo- dynamics and flow-vessel interaction in patient specific normal (healthy) and dilated (diseased) aortas using Ansys-Fluent and Ansys-Workbench. The computation con- sists of three parts: segmentation of arterial geometry for the CFD simulation from computed tomography (CT) scanning data using MIMICS; finite volume simulation of hemodynamics of steady and pulsatile flow using Ansys-Fluent; an attempt to perform the Fluid Structure Simulation of the normal aorta using Ansys-Workbench. Instead of neglecting the branching or smoothing out the wall for simplification as a lot of similar computation in literature, we use the exact aortic geometry. Segmen- tation from real time CT images from two patients, one young and another old to represent healthy and diseased aorta respectively, is on MIMICS. The MIMICS seg- mentation operation includes: first cropping the required part of aorta from CT dicom data of the whole chest, masking of the aorta from coronal, axial and saggital views of the same to extract the exact 3D geometry of the aorta. Next step was to perform surface improvement using MIMICS 3-matic module to repair for holes, noise shells and overlapping triangles to create a good quality surface of the geometry. A hexahe- dral volume mesh was created in T-Grid. Since T-grid cannot recognize the geometry format created by MIMICS 3-matic; the required step geometry file was created in Pro-Engineer. After the meshing operation is performed, the mesh is exported to Ansys Fluent to perform the required fluid simulation imposing adequate boundary conditions accordingly. Two types of study are performed for hemodynamics. First is a steady flow driven by specified parabolic velocity at inlet. We captured the flow feature such as skewness of velocity around the aortic arch regions and vortices pairs, which are in good agreement with open data in literature. Second is a pulsatile flow. Two pulsatile velocity profiles are imposed at the inlet of healthy and diseased aorta respectively. The pulsatile analysis was accomplished for peak systolic, mid systolic and diastolic phase of the entire cardiac cycle. During peak systole and mid-systole, high WSS was found at the aortic branch roots and arch regions and diastole resulted in flow reversals and low WSS values due to small aortic inflow. In brief, areas of sudden geometry change, i.e. the branch roots and irregular surfaces of the geom- etry experience more WSS. Also it was found that dilated aorta has more sporadic nature of WSS in different regions than normal aorta which displays a more uniform WSS distribution all over the aorta surface. Fluid-Structure Interaction simulation is performed on Ansys-WorkBench through the coupling of fluid dynamics and solid mechanics. Focus is on the maximum displacement and equivalent stress to find out the future failure regions for the peak velocity of the cardiac cycle.

Emergency departments are fundamental for providing high-quality care, and their operations directly impact the logistics of the hospitals in their entirety. Poor emergency department performance leads to delays, prolonged hospitalization, and improper allocation of resources, reducing the quality of the provided care and increasing costs. Describing the variability embedded in real clinical data in a useful way is essential for improving the organization of hospitals in the near future. However, it is a challenging task due to clinical complexity and the lack of an established bridge between logistic systems and the clinical insights of the hospital. Therefore, this work aims to design and implement a simplified process model describing patient flow within an emergency department, which could allow the evaluation of the clinical impact of complex patient characteristics on the system's logistics. To achieve this, a novel nonlinear mixed-effects approach with hospital medical records was applied to design patient flow within the emergency department in the form of a multi-state Markov process. Four independent training data samples were extracted from the main dataset. For each of them, the set of covariates that could lead to the most significant improvement in the values of the employed likelihood indicators was selected. Through statistical tests, analysis of the outputs, and a validation process carried out on a fifth and independent dataset, it was possible to obtain a final model containing the most relevant and significant covariates for describing each of the modeled state transitions and confirming their clinical meaningfulness and relevance. The results achieved in this thesis can lead to future improvement of the healthcare logistics systems by extending the use of nonlinear mixed-effects approaches to the estimation of the covariate impact on emergency department flows. / Akutmottagningar är centrala för att tillhandahålla högkvalitativ vård. Deras verksamhet har en direkt inverkan på sjukhusens logistik i helhet. Undermålig prestation i en akutmottagning leder till förseningar, förlängd sjukhusvistelse för patienter och olämpliga resursfördelningar, som i sin tur försämrar kvaliteten på den erbjudna vården, samt ökar kostnader. Därför är det viktigt att beskriva den variabilitet som är inbäddad i kliniskt data för att kunna förbättra strukturen av sjukhus i den närmaste framtiden. Emellertid är det ett utmanande uppdrag på grund av den kliniska komplexiteten och bristen på en etablerad bro mellan logistiska system och insikter om den kliniska situationen på sjukhuset. Detta examensarbete ämnar därför designa och implementera en förenklad processmodel som beskriver patientflödet inom en akutmottagning, vilket skulle kunna tillåta evaluering av vad för klinisk inverkan patienters komplexa egenskaper har på systemets logistik. För att uppnå detta tillämpades ett nytt icke-linjärt tillvägagångssätt för blandade effekter med patientjournaler, med syfte att designa patientflöde inom akutmottagningen i form av en Markovprocess i flera tillstånd. Fyra oberoende urvalsgrupper med övningsdata extraherades från huvuddatasetet och för var och en av dem valdes den uppsättning kovariat som hade möjlighet att leda till den största förbättringen i de applicerade sannolikhetsindikatorerna. Genom statistiska test, analys av uteffekten och en valideringsprocess utförd på en femte oberoende urvalsgrupp, möjliggjordes framtagandet av en slutgiltig modell innehållande de mest relevanta och signifikanta kovariat för att beskriva var och en av de modellerade tillståndsövergångarna, och bekräfta dess kliniska betydelse och relevans. De resultat som uppnåddes i det här examensarbetet har potential att i framtiden leda till förbättring av sjukvårdens logistiksystem, genom att utvidga användningen av icke-linjära blandade effektmodeller för att uppskatta kovariatinverkan på akutmottagningsflöden.