Alessandra M Bavo1, Benjamin T Wilkins2, Philippe Garot3, Sander De Bock4, Jacqueline Saw5, Lars Søndergaard2, Ole De Backer2, Francesco Iannaccone4. 1. FEops NV, Ghent, Belgium. Electronic address: alessandra.bavo@feops.com. 2. The Heart Centre, Rigshospitalet, Copenhagen, Denmark. 3. Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud (ICPS), Ramsay-Générale de santé, Massy, France. 4. FEops NV, Ghent, Belgium. 5. Division of Cardiology, Vancouver General Hospital, University of British Columbia, Vancouver, Canada.
Abstract
BACKGROUND: Percutaneous left atrial appendage (LAA) closure can be optimised through diligent preprocedural planning. Cardiac computational tomography (CCT) is increasingly recognised as a valuable tool in this process. A CCT-based computational model (FEops HEARTguide™, Belgium) has been developed to simulate the deployment of the two most commonly used LAA closure devices into patient-specific LAA anatomies. OBJECTIVE: The aim of this study was to validate this computational model based on real-life percutaneous LAA closure procedures and post-procedural CCT imaging. METHODS: Thirty patients having undergone LAA closure (Amulet™ n = 15, Watchman™ n = 15) and having a pre- and post-procedural CCT-scan were selected for this validation study. Virtually implanted devices were directly compared to actual implants for device frame deformation and LAA wall apposition. RESULTS: The coefficient of determination (R2) and the difference in measurements between model and actual device (area, perimeter, minimum diameter, maximum diameter) were ≥0.91 and ≤ 5%, respectively. For both device types, the correlation coefficient between predicted and observed measurements was higher than 0.90. Furthermore, predicted device apposition correlated well with observed leaks based on post-procedural CCT. CONCLUSION: Computational modelling accurately predicts LAA closure device deformation and apposition and may therefore potentiate more accurate LAA closure device sizing and better preprocedural planning.
BACKGROUND: Percutaneous left atrial appendage (LAA) closure can be optimised through diligent preprocedural planning. Cardiac computational tomography (CCT) is increasingly recognised as a valuable tool in this process. A CCT-based computational model (FEops HEARTguide™, Belgium) has been developed to simulate the deployment of the two most commonly used LAA closure devices into patient-specific LAA anatomies. OBJECTIVE: The aim of this study was to validate this computational model based on real-life percutaneous LAA closure procedures and post-procedural CCT imaging. METHODS: Thirty patients having undergone LAA closure (Amulet™ n = 15, Watchman™ n = 15) and having a pre- and post-procedural CCT-scan were selected for this validation study. Virtually implanted devices were directly compared to actual implants for device frame deformation and LAA wall apposition. RESULTS: The coefficient of determination (R2) and the difference in measurements between model and actual device (area, perimeter, minimum diameter, maximum diameter) were ≥0.91 and ≤ 5%, respectively. For both device types, the correlation coefficient between predicted and observed measurements was higher than 0.90. Furthermore, predicted device apposition correlated well with observed leaks based on post-procedural CCT. CONCLUSION: Computational modelling accurately predicts LAA closure device deformation and apposition and may therefore potentiate more accurate LAA closure device sizing and better preprocedural planning.
Authors: Todd C Villines; Subhi J Al'Aref; Daniele Andreini; Marcus Y Chen; Andrew D Choi; Carlo N De Cecco; Damini Dey; James P Earls; Maros Ferencik; Heidi Gransar; Harvey Hecht; Jonathon A Leipsic; Michael T Lu; Mohamed Marwan; Pál Maurovich-Horvat; Edward Nicol; Gianluca Pontone; Jonathan Weir-McCall; Seamus P Whelton; Michelle C Williams; Armin Arbab-Zadeh; Gudrun M Feuchtner Journal: J Cardiovasc Comput Tomogr Date: 2021-02-22
Authors: Nicholas P Aroney; Ronak Rajani; Tiffany Patterson; Christopher J Allen; Harminder Gill; Julia Grapsa; Jane Hancock; Bernard Prendergast; Simon Redwood Journal: JACC Case Rep Date: 2021-06-30
Authors: José Ramón López-Mínguez; Ginés Martínez-Cáceres; Reyes González-Fernández; Juan Manuel Nogales-Asensio; Victoria Millán-Núñez Journal: Int J Cardiovasc Imaging Date: 2021-05-06 Impact factor: 2.357