Abstract
In-silico fluid simulations of the left atria (LA) in atrial fibrillation (AF) patients can help to describe and relate patient-specific morphologies and complex flow haemodynamics with the pathophysiological mechanisms behind thrombus formation. Even in AF patients, LA wall motion plays a non-negligible role in LA function and blood flow patterns. However, obtaining 4D LA wall dynamics from patient-specific data is not an easy task due to current image resolution limitations. Therefore, several approaches have been proposed in the literature to include left atrial wall motion in fluid simulations, being necessary to benchmark them in relation to their estimations on thrombogenic risk. In this work, we present results obtained with computational fluid dynamic simulations of LA geometries from a control and an AF patient with different left atrial wall motion approaches: 1) assuming rigid walls; 2) with a passive movement of the mitral valve annulus plane from a dynamic mesh approach based on springs (DM-SB); and 3) imposing LA wall deformation extracted from dynamic computed tomography (DM-dCT) images. Different strategies for the inlet/outlet boundary conditions were also tested. The DM-dCT approach was the one providing simulation results closer to velocity curves extracted from the reference echocardiographic data, whereas the rigid wall strategy over-estimated the risk of thrombus formation.
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References
Aminian, A., et al.: Incidence, characterization, and clinical impact of device-related thrombus following left atrial appendage occlusion in the prospective global AMPLATZER amulet observational study. JACC: Cardiovasc. Interv. 12(11), 1003–1014 (2019)
Bosi, G.M., et al.: Computational fluid dynamic analysis of the left atrial appendage to predict thrombosis risk. Front. Cardiovasc. Med. 5, 34 (2018)
Cresti, A., et al.: Prevalence of extra-appendage thrombosis in non-valvular atrial fibrillation and atrial flutter in patients undergoing cardioversion: a large transoesophageal echo study. EuroIntervention 15(3), e225–e230 (2019)
Fang, R., Li, Y., Zhang, Y., Chen, Q., Liu, Q., Li, Z.: Impact of left atrial appendage location on risk of thrombus formation in patients with atrial fibrillation. Biomech. Model. Mechanobiology 20(4), 1431–1443 (2021). https://doi.org/10.1007/s10237-021-01454-4
Fauchier, L., et al.: Device-related thrombosis after percutaneous left atrial appendage occlusion for atrial fibrillation. J. Am. Coll. Cardiol. 71(14), 1528–1536 (2018)
Fyrenius, A., Wigström, L., Ebbers, T., Karlsson, M., Engvall, J., Bolger, A.F.: Three dimensional flow in the human left atrium. Heart 86(4), 448–455 (2001)
Garcia, J., et al.: Left atrial vortex size and velocity distributions by 4d flow MRI in patients with paroxysmal atrial fibrillation: Associations with age and CHA2DS2-VASc risk score. J. Magn. Reson. Imaging 51(3), 871–884 (2020)
García-Isla, G., et al.: Sensitivity analysis of geometrical parameters to study haemodynamics and thrombus formation in the left atrial appendage. Int. J. Numer. Methods Biomed. Eng. 34(8), e3100 (2018)
García-Villalba, M., et al.: Demonstration of patient-specific simulations to assess left atrial appendage thrombogenesis risk. Front. Physiol. 12, 596596 (2021)
Gonzalo, A., et al.: Non-newtonian blood rheology impacts left atrial stasis in patient-specific simulations. Int. J. Numer. Methods Biomed. Eng., e3597 (2022)
Haeusler, K.G., et al.: Expert opinion paper on atrial fibrillation detection after ischemic stroke. Clin. Res. Cardiol. 107(10), 871–880 (2018). https://doi.org/10.1007/s00392-018-1256-9
Markl, M., et al.: Assessment of left atrial and left atrial appendage flow and stasis in atrial fibrillation. J. Cardiovasc. Magn. Reson. 17(1), 1–2 (2015)
Masci, A., et al.: A proof of concept for computational fluid dynamic analysis of the left atrium in atrial fibrillation on a patient-specific basis. J. Biomech. Eng. 142(1) (2020)
Mill, J., et al.: Patient-specific flow simulation analysis to predict device-related thrombosis in left atrial appendage occluders. REC: Interv. Cardiol. 3(4), 278–85 (2021)
Mill, J., et al.: Sensitivity analysis of in silico fluid simulations to predict thrombus formation after left atrial appendage occlusion. Mathematics 9(18), 2304 (2021)
Nagueh, S.F., et al.: Recommendations for the evaluation of left ventricular diastolic function by echocardiography. Eur. J. Echocardiogr. 10(2), 165–193 (2009)
Otani, T., Al-Issa, A., Pourmorteza, A., McVeigh, E.R., Wada, S., Ashikaga, H.: A computational framework for personalized blood flow analysis in the human left atrium. Ann. Biomed. Eng. 44(11), 3284–3294 (2016)
Veronesi, F., et al.: Quantification of mitral apparatus dynamics in functional and ischemic mitral regurgitation using real-time 3-dimensional echocardiography. J. Am. Soc. Echocardiogr. 21(4), 347–354 (2008)
Viceconti, M., Pappalardo, F., Rodriguez, B., Horner, M., Bischoff, J., Tshinanu, F.M.: In silico trials: verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods 185, 120–127 (2021)
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016496 (SimCardioTest).
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Validation of the different boundary condition (BC) scenarios in a control and an AF patient. (a) and (b) represents the velocity curves at the mitral valve (MV) and the left superior pulmonary vein (LSPV), respectively, provided by the different approaches for incorporating left atrial wall motion into fluid simulations. Configuration 1: velocity profile at the inlet pulmonary veins (PV) and pressure constant values at the MV outlet. Configuration 2: pressure at the inlet PV and velocities at the MV outlet. GT: Velocity curve extracted from dynamic computed tomography taken as a ground-truth for comparison purposes. R: rigid walls. DM-dCT and DM-SB: dynamic mesh approaches guided by dynamic computed tomography images and spring-based method, respectively.
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Albors, C. et al. (2022). Sensitivity Analysis of Left Atrial Wall Modeling Approaches and Inlet/Outlet Boundary Conditions in Fluid Simulations to Predict Thrombus Formation. In: Camara, O., et al. Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers. STACOM 2022. Lecture Notes in Computer Science, vol 13593. Springer, Cham. https://doi.org/10.1007/978-3-031-23443-9_17
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