Non-Newtonian coupled field analysis of blood flow in normal and stenosed carotid artery with varying haemodynamic parameters

Atherosclerosis is a chronic disease affecting millions worldwide by leading to heart attack and stroke. It usually develops in regions with disturbed flow like the carotid artery, aorta, and coronary arteries. The major cause of atherosclerosis development is the deposition of lipids under the endothelial layer of the artery leading to plaque build-up. Also, evidence that the plaque formation occurs mainly near the bifurcations or curvatures had led to the hypothesis that irregular flow conditions plays a major role in development and progression of atherosclerosis. In vivo and in vitro studies at the cellular level and macroscopic levels shows the importance of understanding the local haemodynamics in atherosclerosis prone regions. Although diagnostic techniques such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) provides detailed anatomic information non-invasively, local haemodynamics can be studied at patient specific models using computational techniques like Computational Fluid Dynamics (CFD) and Fluid Structure Interaction (FSI). Therefore, it is very important to reconstruct anatomical models using CT or MRI images to gain accurate results in CFD or FSI analysis. The flow behaviour in large arteries is complex and it is influenced by the elasticity of the artery. Apart from this, the blood pressure changes during day to day activities. This interesting phenomenon of variation of blood pressure is studied by numerical simulation of blood flow in the normal and stenosed carotid artery. In this work, a three-dimensional (3D) Fluid Structure Interaction (FSI) study was carried out for a normal and stenosed patient specific carotid artery models. By considering physiological conditions, first the normal and then with hypertension disease, haemodynamic parameters were evaluated to better understand the genesis and progression of atherosclerotic plaques in the carotid artery bifurcation. Two-way FSI was performed by applying a fully implicit second-order backward Euler differencing scheme using commercial software ANSYS and ANSYS CFX (version 19.0). Arbitrary Lagrangian–Eulerian (ALE) formulation was employed to calculate the arterial response by using the temporal blood response. Due to arterial bifurcation, obvious velocity reduction and backflow formation were observed which decreased shear stress and made it oscillatory at the starting point of the internal carotid artery near the carotid sinus, which resulted in low shear stress. Oscillatory shear index (OSI) signifies oscillatory behaviour of artery wall shear stress. Comparison of the results of this study with those in the published literature showed that the regions with low wall shear stress and with OSI value greater than 0.3 pose potential risk to the development of plaques. It was observed that haemodynamics of the carotid artery was very much affected by the geometry and flow conditions. Furthermore, regions of relatively low wall shear stress were observed post stenosis, which is a known cause of plaque development and progression. The results were compared between Newtonian and Carreau – Yasuda blood viscosity models. Critical haemodynamic parameters such as wall shear stress (WSS) and Oscillatory Shear Index (OSI) were examined. Simulated hemodynamic parameters were able to capture the disturbed flow conditions in a normal and a stenosed carotid artery bifurcation, which play an important role in the development of local atherosclerotic plaques. Computational simulations based on diagnostic tools such as Ultrasound might help improving diagnostic and treatment management of carotid atherosclerosis

Contribuciones de las técnicas machine learning a la cardiología. Predicción de reestenosis tras implante de stent coronario

[ES]Antecedentes: Existen pocos temas de actualidad equiparables a la posibilidad de la tecnología actual para desarrollar las mismas capacidades que el ser humano, incluso en medicina. Esta capacidad de simular los procesos de inteligencia humana por parte de máquinas o sistemas informáticos es lo que conocemos hoy en día como inteligencia artificial. Uno de los campos de la inteligencia artificial con mayor aplicación a día de hoy en medicina es el de la predicción, recomendación o diagnóstico, donde se aplican las técnicas machine learning. Asimismo, existe un creciente interés en las técnicas de medicina de precisión, donde las técnicas machine learning pueden ofrecer atención médica individualizada a cada paciente. El intervencionismo coronario percutáneo (ICP) con stent se ha convertido en una práctica habitual en la revascularización de los vasos coronarios con enfermedad aterosclerótica obstructiva significativa. El ICP es asimismo patrón oro de tratamiento en pacientes con infarto agudo de miocardio; reduciendo las tasas de muerte e isquemia recurrente en comparación con el tratamiento médico. El éxito a largo plazo del procedimiento está limitado por la reestenosis del stent, un proceso patológico que provoca un estrechamiento arterial recurrente en el sitio de la ICP. Identificar qué pacientes harán reestenosis es un desafío clínico importante; ya que puede manifestarse como un nuevo infarto agudo de miocardio o forzar una nueva resvascularización del vaso afectado, y que en casos de reestenosis recurrente representa un reto terapéutico. Objetivos: Después de realizar una revisión de las técnicas de inteligencia artificial aplicadas a la medicina y con mayor profundidad, de las técnicas machine learning aplicadas a la cardiología, el objetivo principal de esta tesis doctoral ha sido desarrollar un modelo machine learning para predecir la aparición de reestenosis en pacientes con infarto agudo de miocardio sometidos a ICP con implante de un stent. Asimismo, han sido objetivos secundarios comparar el modelo desarrollado con machine learning con los scores clásicos de riesgo de reestenosis utilizados hasta la fecha; y desarrollar un software que permita trasladar esta contribución a la práctica clínica diaria de forma sencilla. Para desarrollar un modelo fácilmente aplicable, realizamos nuestras predicciones sin variables adicionales a las obtenidas en la práctica rutinaria. Material: El conjunto de datos, obtenido del ensayo GRACIA-3, consistió en 263 pacientes con características demográficas, clínicas y angiográficas; 23 de ellos presentaron reestenosis a los 12 meses después de la implantación del stent. Todos los desarrollos llevados a cabo se han hecho en Python y se ha utilizado computación en la nube, en concreto AWS (Amazon Web Services). Metodología: Se ha utilizado una metodología para trabajar con conjuntos de datos pequeños y no balanceados, siendo importante el esquema de validación cruzada anidada utilizado, así como la utilización de las curvas PR (precision-recall, exhaustividad-sensibilidad), además de las curvas ROC, para la interpretación de los modelos. Se han entrenado los algoritmos más habituales en la literatura para elegir el que mejor comportamiento ha presentado. Resultados: El modelo con mejores resultados ha sido el desarrollado con un clasificador extremely randomized trees; que superó significativamente (0,77; área bajo la curva ROC a los tres scores clínicos clásicos; PRESTO-1 (0,58), PRESTO-2 (0,58) y TLR (0,62). Las curvas exhaustividad sensibilidad ofrecieron una imagen más precisa del rendimiento del modelo extremely randomized trees que muestra un algoritmo eficiente (0,96) para no reestenosis, con alta exhaustividad y alta sensibilidad. Para un umbral considerado óptimo, de 1,000 pacientes sometidos a implante de stent, nuestro modelo machine learning predeciría correctamente 181 (18%) más casos en comparación con el mejor score de riesgo clásico (TLR). Las variables más importantes clasificadas según su contribución a las predicciones fueron diabetes, enfermedad coronaria en 2 ó más vasos, flujo TIMI post-ICP, plaquetas anormales, trombo post-ICP y colesterol anormal. Finalmente, se ha desarrollado una calculadora para trasladar el modelo a la práctica clínica. La calculadora permite estimar el riesgo individual de cada paciente y situarlo en una zona de riesgo, facilitando la toma de decisión al médico en cuanto al seguimiento adecuado para el mismo. Conclusiones: Aplicado inmediatamente después de la implantación del stent, un modelo machine learning diferencia mejor a aquellos pacientes que presentarán o no reestenosis respecto a los discriminadores clásicos actuales

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

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal