Impact of left atrial wall motion assumptions in fluid simulations on proposed predictors of thrombus formation.

Atrial fibrillation (AF) poses a significant risk of stroke due to thrombus formation, which primarily occurs in the left atrial appendage (LAA). Medical image-based computational fluid dynamics (CFD) simulations can provide valuable insight into patient-specific hemodynamics and could potentially enhance personalized assessment of thrombus risk. However, the importance of accurately representing the left atrial (LA) wall dynamics has not been fully resolved. In this study, we compared four modeling scenarios; rigid walls, a generic wall motion based on a reference motion, a semi-generic wall motion based on patient-specific motion, and patient-specific wall motion based on medical images. We considered a LA geometry acquired from 4D computed tomography during AF, systematically performed convergence tests to assess the numerical accuracy of our solution strategy, and quantified the differences between the four approaches. The results revealed that wall motion had no discernible impact on LA cavity hemodynamics, nor on the markers that indicate thrombus formation. However, the flow patterns within the LAA deviated significantly in the rigid model, indicating that the assumption of rigid walls may lead to errors in the estimated risk factors. In contrast, the generic, semi-generic, and patient-specific cases were qualitatively similar. The results highlight the crucial role of wall motion on hemodynamics and predictors of thrombus formation, and also demonstrate the potential of using a generic motion model as a surrogate for the more complex patient-specific motion. While the present study considered a single case, the employed CFD framework is entirely open-source and designed for adaptability, allowing for integration of additional models and generic motions.

The role of the pulmonary veins on left atrial flow patterns and thrombus formation.

Atrial fibrillation (AF) is the most common human arrhythmia, forming thrombi mostly in the left atrial appendage (LAA). However, the relation between LAA morphology, blood patterns and clot formation is not yet fully understood. Furthermore, the impact of anatomical structures like the pulmonary veins (PVs) have not been thoroughly studied due to data acquisition difficulties. In-silico studies with flow simulations provide a detailed analysis of blood flow patterns under different boundary conditions, but a limited number of cases have been reported in the literature. To address these gaps, we investigated the influence of PVs on LA blood flow patterns and thrombus formation risk through computational fluid dynamics simulations conducted on a sizeable cohort of 130 patients, establishing the largest cohort of patient-specific LA fluid simulations reported to date. The investigation encompassed an in-depth analysis of several parameters, including pulmonary vein orientation (e.g., angles) and configuration (e.g., number), LAA and LA volumes as well as their ratio, flow, and mass-less particles. Our findings highlight the total number of particles within the LAA as a key parameter for distinguishing between the thrombus and non-thrombus groups. Moreover, the angles between the different PVs play an important role to determine the flow going inside the LAA and consequently the risk of thrombus formation. The alignment between the LAA and the main direction of the left superior pulmonary vein, or the position of the right pulmonary vein when it exhibits greater inclination, had an impact to distinguish the control group vs. the thrombus group. These insights shed light on the intricate relationship between PV configuration, LAA morphology, and thrombus formation, underscoring the importance of comprehensive blood flow pattern analyses.

On the importance of fundamental computational fluid dynamics toward a robust and reliable model of left atrial flows.

Computational fluid dynamics (CFD) studies of left atrial flows have reached a sophisticated level, for example, revealing plausible relationships between hemodynamics and stresses with atrial fibrillation. However, little focus has been on fundamental fluid modeling of LA flows. The purpose of this study was to investigate the spatiotemporal convergence, along with the differences between high- (HR) versus normal-resolution/accuracy (NR) solution strategies, respectively. Rigid wall CFD simulations were conducted on 12 patient-specific left atrial geometries obtained from computed tomography scans, utilizing a second-order accurate and space/time-centered solver. The convergence studies showed an average variability of around 30% and 55% for time averaged wall shear stress (WSS), oscillatory shear index (OSI), relative residence time (RRT), and endothelial cell activation potential (ECAP), even between intermediate spatial and temporal resolutions, in the left atrium (LA) and left atrial appendage (LAA), respectively. The comparison between HR and NR simulations showed good correlation in the LA for WSS, RRT, and ECAP ( R 2 > .9 ), but not for OSI ( R 2 = .63 ). However, there were poor correlations in the LAA especially for OSI, RRT, and ECAP ( R 2 = .55, .63, and .61, respectively), except for WSS ( R 2 = .81 ). The errors are comparable to differences previously reported with disease correlations. To robustly predict atrial hemodynamics and stresses, numerical resolutions of 10 M elements (i.e., Δ x = ∼ .5 mm) and 10 k time-steps per cycle seem necessary (i.e., one order of magnitude higher than normally used in both space and time). In conclusion, attention to fundamental numerical aspects is essential toward establishing a plausible, robust, and reliable model of LA flows.

Domain expert evaluation of advanced visual computing solutions and 3D printing for the planning of the left atrial appendage occluder interventions.

Advanced visual computing solutions and three-dimensional (3D) printing are moving from engineering to clinical pipelines for training, planning, and guidance of complex interventions. 3D imaging and rendering, virtual reality (VR), and in-silico simulations, as well as 3D printing technologies provide complementary information to better understand the structure and function of the organs, thereby improving and personalizing clinical decisions. In this study, we evaluated several advanced visual computing solutions, such as web-based 3D imaging visualization, VR, and computational fluid simulations, together with 3D printing, for the planning of the left atrial appendage occluder (LAAO) device implantations. Six cardiologists tested different technologies in pre-operative data of five patients to identify the usability, limitations, and requirements for the clinical translation of each technology through a qualitative questionnaire. The obtained results demonstrate the potential impact of advanced visual computing solutions and 3D printing to improve the planning of LAAO interventions as well as the need for their integration into a single workflow to be used in a clinical environment.

Joint Analysis of Morphological Parameters and In Silico Haemodynamics of the Left Atrial Appendage for Thrombogenic Risk Assessment.

Background: Atrial fibrillation (AF) is considered the most common human arrhythmia. In nonvalvular AF, around 99% of thrombi are formed in the left atrial appendage (LAA). Nevertheless, there is not a consensus in the community about the relevant factors to stratify the AF population according to thrombogenic risk. Objective: To demonstrate the need for combining left atrial morphological and haemodynamics indices to improve the thrombogenic risk assessment in nonvalvular AF patients. Methods: A cohort of 71 nonvalvular AF patients was analysed. Statistical analysis, regression models, and random forests were used to analyse the differences between morphological and haemodynamics parameters, extracted from computational simulations built on 3D rotational angiography images, between patients with and without transient ischemic attack (TIA) or cerebrovascular accident (CVA). Results: The analysis showed that models composed of both morphological and haemodynamic factors were better predictors of TIA/CVA compared with models based on either morphological or haemodynamic factors separately. Maximum ostium diameter, length of the centreline, blood flow velocity within the LAA, oscillatory shear index, and time average wall shear stress parameters were found to be key risk factors for TIA/CVA prediction. In addition, TIA/CVA patients presented more flow stagnation within the LAA. Conclusion: Thrombus formation in the LAA is the result of multiple factors. Analyses based only on morphological or haemodynamic parameters are not precise enough to predict such a phenomenon, as demonstrated in our results; a better patient stratification can be obtained by jointly analysing morphological and haemodynamic features.

Deep Learning Framework for Real-Time Estimation of in-silico Thrombotic Risk Indices in the Left Atrial Appendage.

Patient-specific computational fluid dynamics (CFD) simulations can provide invaluable insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics, and the formation of thrombi in atrial fibrillation (AF) patients. Nonetheless, CFD solvers are notoriously time-consuming and computationally demanding, which has sparked an ever-growing body of literature aiming to develop surrogate models of fluid simulations based on neural networks. The present study aims at developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), an in-silico index linked to the risk of thrombosis, typically derived from CFD simulations, solely from the patient-specific LAA morphology. To this end, a set of popular DL approaches were evaluated, including fully connected networks (FCN), convolutional neural networks (CNN), and geometric deep learning. While the latter directly operated over non-Euclidean domains, the FCN and CNN approaches required previous registration or 2D mapping of the input LAA mesh. First, the superior performance of the graph-based DL model was demonstrated in a dataset consisting of 256 synthetic and real LAA, where CFD simulations with simplified boundary conditions were run. Subsequently, the adaptability of the geometric DL model was further proven in a more realistic dataset of 114 cases, which included the complete patient-specific LA and CFD simulations with more complex boundary conditions. The resulting DL framework successfully predicted the overall distribution of the ECAP in both datasets, based solely on anatomical features, while reducing computational times by orders of magnitude compared to conventional CFD solvers.

In silico Optimization of Left Atrial Appendage Occluder Implantation Using Interactive and Modeling Tools.

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.