According to clinical studies, around one third of patients with atrial fibrillation (AF) will suffer a stroke during their lifetime. Between 70 and 90% of these strokes are caused by thrombus formed in the left atrial appendage. In patients with contraindications to oral anticoagulants, a left atrial appendage occluder (LAAO) is often implanted to prevent blood flow entering in the LAA. A limited range of LAAO devices is available, with different designs and sizes. Together with the heterogeneity of LAA morphology, these factors make LAAO success dependent on clinician's experience. A sub-optimal LAAO implantation can generate thrombi outside the device, eventually leading to stroke if not treated. The aim of this study was to develop clinician-friendly tools based on biophysical models to optimize LAAO device therapies. A web-based 3D interactive virtual implantation platform, so-called VIDAA, was created to select the most appropriate LAAO configurations (type of device, size, landing zone) for a given patient-specific LAA morphology. An initial LAAO configuration is proposed in VIDAA, automatically computed from LAA shape features (centreline, diameters). The most promising LAAO settings and LAA geometries were exported from VIDAA to build volumetric meshes and run Computational Fluid Dynamics (CFD) simulations to assess blood flow patterns after implantation. Risk of thrombus formation was estimated from the simulated hemodynamics with an index combining information from blood flow velocity and complexity. The combination of the VIDAA platform with in silico indices allowed to identify the LAAO configurations associated to a lower risk of thrombus formation; device positioning was key to the creation of regions with turbulent flows after implantation. Our results demonstrate the potential for optimizing LAAO therapy settings during pre-implant planning based on modeling tools and contribute to reduce the risk of thrombus formation after treatment.
Atrial fibrillation (AF) is the most common clinically significant arrhythmia, often severely disrupting cardiac hemodynamics and drastically increasing the risk of thromboembolic events. Around 90% of such intracardiac thrombus formation in AF patients takes place in the left atrial appendage (LAA). Such thrombus have been related to blood stasis, which at the moment, can be only assessed through noisy imaging data from transesophageal echocardiography (TEE) at one single point in space and time, vastly oversimplifying the characterization of the complex 4D nature of blood flow patterns. Alternatively, attempts have been made to relate LAA morphology to the risk of thrombi formation, some studies suggesting reduced risk of thrombosis on chicken-wing morphologies. Nonetheless, such classification of the LAA morphology has been found to be highly inconsistent and subjective, excluding as well, several fundamental morphological parameters such as the ostium size or the pulmonary vein (PV) orientation among others. More recently, computational fluid dynamics (CFD) have been employed on the left atrium (LA), seeking to assess the risk of thrombogenesis more quantitatively. CFD has proven to be an invaluable tool in establishing a mechanistic relation between patient-specific organ morphology and its characteristic hemodynamics. In fact, it has long been implemented in other human tissues, such as the coronary arteries, cerebral aneurysms and the aorta with unparalleled success, enabling early diagnosis and risk assessment of various cardiovascular diseases. Nevertheless, traditional CFD methods are renowned for their large memory requirements and long computing times, which severely hinders its suitability for time-sensitive clinical applications. Hence, this thesis seeks to harness the immense potential of deep learning (DL) by developing a deep neural network (DNN), with the objective of generating a fast and accurate surrogate of CFD, capable of instantaneously evaluating the risk of thrombus formation in the LAA. Already having revolutionized fields such as data processing, it has only recently begun to employ DNNs in high-dimensional, complex dynamical systems such as fluid dynamics. In fact to our knowledge, this study represents the first successful implementation of a DL surrogate of CFD analysis in a structure as complex as the LAA, which had only been previously attempted in the aorta. For that purpose, two DL architectures have been successfully designed and trained, which receive the specific LAA geometry as an input, and accurately predict its corresponding endothelial cell activation potential (ECAP) map, parameter linked to the risk of thrombosis. The first approach, is based on a simple fully-connected feedforward network, while the latter, also embeds unsupervised learning. An statistical shape model (SSM) of the LAA was created to generate the training dataset, encompassing 210 virtual shapes, on which CFD simulations were performed to attain the ground truth ECAP mappings. Once trained, the final D...
Atrial fibrillation (AF) is nowadays the most common human arrhythmia and it is considered a marker of an increased risk of embolic stroke. It is known that 99% of AF-related thrombi are generated in the left atrial appendage (LAA), an anatomical structure located within the left atrium (LA). Left atrial appendage occlusion (LAAO) has become a good alternative for nonvalvular AF patients with contraindications to anticoagulants. However, there is a non-negligible number of device-related thrombus (DRT) events, created next to the device surface. In silico fluid simulations can be a powerful tool to better understand the relation between LA anatomy, haemodynamics, and the process of thrombus formation. Despite the increasing literature in LA fluid modelling, a consensus has not been reached yet in the community on the optimal modelling choices and boundary conditions for generating realistic simulations. In this line, we have performed a sensitivity analysis of several boundary conditions scenarios, varying inlet/outlet and LA wall movement configurations, using patient-specific imaging data of six LAAO patients (three of them with DRT at follow-up). Mesh and cardiac cycle convergence were also analysed. The boundary conditions scenario that better predicted DRT cases had echocardiography-based velocities at the mitral valve outlet, a generic pressure wave from an AF patient at the pulmonary vein inlets, and a dynamic mesh approach for LA wall deformation, emphasizing the need for patient-specific data for realistic simulations. The obtained promising results need to be further validated with larger cohorts, ideally with ground truth data, but they already offer unique insights on thrombogenic risk in the left atria.
Atrial fibrillation (AF) is considered the most common human arrhythmia. Around 99% of thrombi in non-valvular AF are formed in the left atrial appendage (LAA). Studies suggest that abnormal LAA haemodynamics and the subsequently stagnated flow are the factors triggering clot formation. However, the relation between LAA morphology, the blood pattern and the triggering is not fully understood. Moreover, the impact of structures such as the pulmonary veins (PVs) on LA haemodynamics has not been thoroughly studied due to the difficulties of acquiring appropriate data. On the other hand, in-silico studies and flow simulations allow a thorough analysis of haemodynamics, analysing the 4D nature of blood flow patterns under different boundary conditions. However, the reduced number of cases reported on the literature of these studies has been a limitation. The main goal of this work was to study the influence of PVs on left atrium (LA) and LAA haemodynamics. Computational fluid dynamics simulations were run on 52 patients, the largest cohort so far in the literature, where different parameters were individually studied: pulmonary veins orientation and configuration; LAA and LA volumes and its ratio; and flow velocities. Our computational analysis showed how the right pulmonary vein height and angulation have a great influence on LA haemodynamics. Additionally, we found that LAA with great bending with its tip pointing towards the mitral valve could contribute to favour flow stagnation.
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