Abstract
Atrial fibrillation (AF) is one of the most common cardiac arrhythmias and is associated to an increasing risk of stroke. Most AF-related strokes are formed in the left atrial appendage (LAA). To prevent thrombus formation, LAA occlusion (LAAO) is considered a suitable alternative for AF patients with contraindications for anti-coagulation treatment. Nevertheless, LAAO is linked to a non-negligible risk of generating thrombus at the surface near the device (i.e., device-related thrombus, DRT), depending on the implantation settings. For instance, it has been shown that not covering the pulmonary ridge (PR) with the LAAO increases the risk of DRT. In-silico analysis is a useful tool to better understand the blood flow patterns after LAAO and predict the risk of DRT for a given patient and device configuration. In the present work we designed a modelling pipeline based on fluid simulations, including a thrombus model using discrete phase modelling, to analyse the risk of DRT in six patient-specific geometries for different LAAO settings. In particular, we studied the possible incidence of DRT depending on the device positioning (covering/uncovering the PR) and type (Amplatzer Amulet and Watchman FLX). The resulting in-silico indices demonstrated that covering the PR entails less thrombogenic patterns than uncovering it. In our study, disk-based devices had better adaptability to complex LAA morphologies and a slightly minor associated risk of DRT than non-disk devices.
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Acknowledgments
This work was supported by the Agency for Management of University and Research Grants of the Generalitat de Catalunya under the Grants for the Contracting of New Research Staff Programme - FI (2020 FI_B 00608) and the Spanish Ministry of Economy and Competitiveness under the Programme for the Formation of Doctors (PRE2018-084062), the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and the Retos Investigación project (RTI2018-101193-B-I00). Additionally, this work was supported by the H2020 EU SimCardioTest project (Digital transformation in Health and Care SC1-DTH-06-2020; grant agreement No. 101016496).
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Planas, E. et al. (2022). In-silico Analysis of Device-Related Thrombosis for Different Left Atrial Appendage Occluder Settings. In: Puyol Antón, E., et al. Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science(), vol 13131. Springer, Cham. https://doi.org/10.1007/978-3-030-93722-5_18
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DOI: https://doi.org/10.1007/978-3-030-93722-5_18
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