Literature DB >> 30322772

Pre-procedural fit-testing of TAVR valves using parametric modeling and 3D printing.

Ahmed Hosny1, Joshua D Dilley2, Tatiana Kelil3, Moses Mathur4, Mason N Dean5, James C Weaver6, Beth Ripley7.   

Abstract

BACKGROUND: Successful transcatheter aortic valve replacement (TAVR) requires an understanding of how a prosthetic valve will interact with a patient's anatomy in advance of surgical deployment. To improve this understanding, we developed a benchtop workflow that allows for testing of physical interactions between prosthetic valves and patient-specific aortic root anatomy, including calcified leaflets, prior to actual prosthetic valve placement.
METHODS: This was a retrospective study of 30 patients who underwent TAVR at a single high volume center. By design, the dataset contained 15 patients with a successful annular seal (defined by an absence of paravalvular leaks) and 15 patients with a sub-optimal seal (presence of paravalvular leaks) on post-procedure transthoracic echocardiogram (TTE). Patients received either a balloon-expandable (Edwards Sapien or Sapien XT, n = 15), or a self-expanding (Medtronic CoreValve or Core Evolut, n = 14, St. Jude Portico, n = 1) valve. Pre-procedural computed tomography (CT) angiograms, parametric geometry modeling, and multi-material 3D printing were utilized to create flexible aortic root physical models, including displaceable calcified valve leaflets. A 3D printed adjustable sizing device was then positioned in the aortic root models and sequentially opened to larger valve sizes, progressively flattening the calcified leaflets against the aortic wall. Optimal valve size and fit were determined by visual inspection and quantitative pressure mapping of interactions between the sizer and models.
RESULTS: Benchtop-predicted "best fit" valve size showed a statistically significant correlation with gold standard CT measurements of the average annulus diameter (n = 30, p < 0.0001 Wilcoxon matched-pairs signed rank test). Adequateness of seal (presence or absence of paravalvular leak) was correctly predicted in 11/15 (73.3%) patients who received a balloon-expandable valve, and in 9/15 (60%) patients who received a self-expanding valve. Pressure testing provided a physical map of areas with an inadequate seal; these corresponded to areas of paravalvular leak documented by post-procedural transthoracic echocardiography.
CONCLUSION: We present and demonstrate the potential of a workflow for determining optimal prosthetic valve size that accounts for aortic annular dimensions as well as the active displacement of calcified valve leaflets during prosthetic valve deployment. The workflow's open source framework offers a platform for providing predictive insights into the design and testing of future prosthetic valves. Published by Elsevier Inc.

Entities:  

Keywords:  3-D printing; 3D printing; Additive manufacturing; Aortic leaflets; Aortic stenosis; Aortic valve; Calcifications; Multi-material printing; Parametric modeling

Mesh:

Year:  2018        PMID: 30322772     DOI: 10.1016/j.jcct.2018.09.007

Source DB:  PubMed          Journal:  J Cardiovasc Comput Tomogr        ISSN: 1876-861X


  14 in total

Review 1.  Three-dimensional printing in structural heart disease and intervention.

Authors:  Yiting Fan; Randolph H L Wong; Alex Pui-Wai Lee
Journal:  Ann Transl Med       Date:  2019-10

Review 2.  3D Printing Applications for Transcatheter Aortic Valve Replacement.

Authors:  Dmitry Levin; G Burkhard Mackensen; Mark Reisman; James M McCabe; Danny Dvir; Beth Ripley
Journal:  Curr Cardiol Rep       Date:  2020-02-17       Impact factor: 2.931

3.  Accuracy of cardiac magnetic resonance generated 3D models of the aortic annulus compared to cardiovascular computed tomography generated 3D models.

Authors:  Marco Gatti; Aurelio Cosentino; Erik Cura Stura; Laura Bergamasco; Domenica Garabello; Giovanni Pennisi; Mattia Puppo; Stefano Salizzoni; Simona Veglia; Ottavio Davini; Mauro Rinaldi; Paolo Fonio; Riccardo Faletti
Journal:  Int J Cardiovasc Imaging       Date:  2020-05-30       Impact factor: 2.357

Review 4.  The Role of 3D Printing in Medical Applications: A State of the Art.

Authors:  Anna Aimar; Augusto Palermo; Bernardo Innocenti
Journal:  J Healthc Eng       Date:  2019-03-21       Impact factor: 2.682

5.  Improved co-registration of ex-vivo and in-vivo cardiovascular magnetic resonance images using heart-specific flexible 3D printed acrylic scaffold combined with non-rigid registration.

Authors:  John Whitaker; Radhouene Neji; Nicholas Byrne; Esther Puyol-Antón; Rahul K Mukherjee; Steven E Williams; Henry Chubb; Louisa O'Neill; Orod Razeghi; Adam Connolly; Kawal Rhode; Steven Niederer; Andrew King; Cory Tschabrunn; Elad Anter; Reza Nezafat; Martin J Bishop; Mark O'Neill; Reza Razavi; Sébastien Roujol
Journal:  J Cardiovasc Magn Reson       Date:  2019-10-10       Impact factor: 5.364

Review 6.  Clinical Applications of Patient-Specific 3D Printed Models in Cardiovascular Disease: Current Status and Future Directions.

Authors:  Zhonghua Sun
Journal:  Biomolecules       Date:  2020-11-20

7.  Use of Patient-Specific 3-Dimensional Printed Models for Planning a Valve-in-Valve Transcatheter Aortic Valve Replacement and Educating Health Personnel, Patients, and Families.

Authors:  Jose D Tafur Soto; Silvia Patricia Gironza Betancourt
Journal:  Ochsner J       Date:  2021

8.  3D printed patient-specific aortic root models with internal sensors for minimally invasive applications.

Authors:  Ghazaleh Haghiashtiani; Kaiyan Qiu; Jorge D Zhingre Sanchez; Zachary J Fuenning; Priya Nair; Sarah E Ahlberg; Paul A Iaizzo; Michael C McAlpine
Journal:  Sci Adv       Date:  2020-08-28       Impact factor: 14.136

Review 9.  Three-dimensional printing for cardiovascular diseases: from anatomical modeling to dynamic functionality.

Authors:  Hao Wang; Hongning Song; Yuanting Yang; Quan Cao; Yugang Hu; Jinling Chen; Juan Guo; Yijia Wang; Dan Jia; Sheng Cao; Qing Zhou
Journal:  Biomed Eng Online       Date:  2020-10-07       Impact factor: 2.819

10.  Automatic Aortic Valve Cusps Segmentation from CT Images Based on the Cascading Multiple Deep Neural Networks.

Authors:  Gakuto Aoyama; Longfei Zhao; Shun Zhao; Xiao Xue; Yunxin Zhong; Haruo Yamauchi; Hiroyuki Tsukihara; Eriko Maeda; Kenji Ino; Naoki Tomii; Shu Takagi; Ichiro Sakuma; Minoru Ono; Takuya Sakaguchi
Journal:  J Imaging       Date:  2022-01-14
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.