Patient-specific Computer Simulation: An Emerging Technology for Guiding the Transcatheter Treatment of Patients with Bicuspid Aortic Valve.
Aortic valve stenosis
bicuspid aortic valve
computer simulation
finite element analysis
heart valve prosthesis implantation
transcatheter aortic valve replacement
Journal
Interventional cardiology (London, England)
ISSN: 1756-1485
Titre abrégé: Interv Cardiol
Pays: England
ID NLM: 101559687
Informations de publication
Date de publication:
Apr 2021
Apr 2021
Historique:
received:
08
03
2021
accepted:
19
07
2021
entrez:
1
11
2021
pubmed:
2
11
2021
medline:
2
11
2021
Statut:
epublish
Résumé
Transcatheter aortic valve implantation (TAVI) is increasingly being used to treat younger, lower-risk patients, many of whom have bicuspid aortic valve (BAV). As TAVI begins to enter these younger patient cohorts, it is critical that clinical outcomes from TAVI in BAV are matched to those achieved by surgery. Therefore, the identification of patients who, on an anatomical basis, may not be suitable for TAVI, would be desirable. Furthermore, clinical outcomes of TAVI in BAV might be improved through improved transcatheter heart valve sizing and positioning. One potential solution to these challenges is patient-specific computer simulation. This review presents the methodology and clinical evidence surrounding patient-specific computer simulation of TAVI in BAV.
Identifiants
pubmed: 34721665
doi: 10.15420/icr.2021.09
pmc: PMC8419845
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
e26Informations de copyright
Copyright © 2021, Radcliffe Cardiology.
Déclaration de conflit d'intérêts
Disclosure: CD has received grants from Medtronic, outside the submitted work. RG has received personal fees from Boston Scientific, outside the submitted work. SJB has received grants and personal fees from Medtronic, outside the submitted work. All other authors have no conflicts of interest to declare. Funding: This study is part of a project that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 945698.
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