Patient-specific flow simulation analysis to predict device-related thrombosis in left atrial appendage occluders
ABSTRACT Introduction and objectives: Left atrial appendage occlusion (LAAO) can be an efficient treatment to prevent strokes in patients who suffer from atrial fibrillation, especially those at risk of bleeding. A non-negligible number of patients treated with LAAO develop device-related thrombosis...
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN ES |
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2021
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| Acceso en línea: | https://doaj.org/article/81b55de5e80046549cd1bcc99909b3d8 |
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| Sumario: | ABSTRACT Introduction and objectives: Left atrial appendage occlusion (LAAO) can be an efficient treatment to prevent strokes in patients who suffer from atrial fibrillation, especially those at risk of bleeding. A non-negligible number of patients treated with LAAO develop device-related thrombosis (DRT) after device implantation. Our study aimed to identify the key blood flow characteristics leading to DRT using patient-specific flow simulations. Methods: Patients treated with LAAO between 2014 and 2019 at a single center with preoperative and follow-up computerized tomography images and ultrasound imaging (US) were used to create patient-specific flow simulations. Amulet LAAO devices were implanted in the study patients. Flow simulations were blindly assessed to discard the presence of DRT in the follow-up imaging. Results: A total of 6 patients were processed in this pivotal study, half of them with DRT at the follow-up according to the imaging analysis. After a comprehensive analysis of the simulations, the most relevant in silico indices associated with DRT were the presence of stagnant blood flow, recirculation with low flow velocities (< 0.20 m/s) next to the device surface, and regions with high flow complexity combined with low wall shear stress. Conclusions: Patient-specific flow simulations of LAAO were successfully used to predict blood flow patterns with different device configurations. The results show the potential of the present modelling and simulation approach to recommend optimal settings capable of minimizing the risk of DRT. |
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