Multimodal Loading Environment Predicts Bioresorbable Vascular Scaffolds’ Durability
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10439_2020_2673_ReferencePDF.pdf
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Author(s)
Wang, Pei-Jiang
Berti, Francesca
Antonini, Luca
Nezami, Farhad R
Petrini, Lorenza
Migliavacca, Francesco
Edelman, Elazer R
Date Issued
October 29, 2020
Publisher
Springer International Publishing
Version
Author's final manuscript
Abstract
Abstract
Bioresorbable vascular scaffolds were considered the fourth generation of endovascular implants deemed to revolutionize cardiovascular interventions. Yet, unexpected high risk of scaffold thrombosis and post-procedural myocardial infractions quenched the early enthusiasm and highlighted the gap between benchtop predictions and clinical observations. To better understand scaffold behavior in the mechanical environment of vessels, animal, and benchtop tests with multimodal loading environment were conducted using industrial standard scaffolds. Finite element analysis was also performed to study the relationship among structural failure, scaffold design, and load types. We identified that applying the combination of bending, axial compression, and torsion better reflects incidence observed in-vivo, far more than tranditional single mode loads. Predication of fracture locations is also more accurate when at least bending and axial compression are applied during benchtop tests (>60% fractures at connected peak). These structural failures may be initiated by implantation-induced microstructural damages and worsened by cyclic loads from the beating heart. Ignoring the multi-modal loading environment in benchtop fatigue tests and computational platforms can lead to undetected potential design defects, calling for redefining consensus evaluation strategies for scaffold performance. With the robust evaluation strategy presented herein, which exploits the results of in-vivo, in-vitro and in-silico investigations, we may be able to compare alternative designs of prototypes at the early stages of device development and optimize the performance of endovascular implants according to patients-specific vessel dynamics and lesion configurations in the future.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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DOI of Published Version
https://doi.org/10.1007/s10439-020-02673-z
