Gosling, R., Morris, P., Lawford, P. et al. (2 more authors) (2018) Predictive Physiological Modeling of Percutaneous Coronary Intervention - Is Virtual Treatment Planning the Future? Frontiers in Physiology, 9. 1107. ISSN 1664-042X
Abstract
Computational modeling has been used routinely in the pre-clinical development of medical devices such as coronary artery stents. The ability to simulate and predict physiological and structural parameters such as flow disturbance, wall shear-stress, and mechanical strain patterns is beneficial to stent manufacturers. These methods are now emerging as useful clinical tools, used by physicians in the assessment and management of patients. Computational models, which can predict the physiological response to intervention, offer clinicians the ability to evaluate a number of different treatment strategies in silico prior to treating the patient in the cardiac catheter laboratory. For the first time clinicians can perform a patient-specific assessment prior to making treatment decisions. This could be advantageous in patients with complex disease patterns where the optimal treatment strategy is not clear. This article reviews the key advances and the potential barriers to clinical adoption and translation of these virtual treatment planning models.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Copyright © 2018 Gosling, Morris, Lawford, Hose and Gunn. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | computational modeling; coronary artery disease; percutaneous coronary intervention; coronary physiology; predictive modeling |
Dates: |
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Institution: | The University of Sheffield |
Funding Information: | Funder Grant number BRITISH HEART FOUNDATION FS/16/48/32306 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Aug 2018 09:38 |
Last Modified: | 27 Aug 2020 00:14 |
Status: | Published |
Publisher: | Frontiers Media |
Refereed: | Yes |
Identification Number: | 10.3389/fphys.2018.01107 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134647 |