Donadoni, F;
Pichardo-Almarza, C;
Homer-Vanniasinkam, S;
Dardik, A;
Díaz-Zuccarini, V;
(2020)
Multiscale, patient-specific computational fluid dynamics models predict formation of neointimal hyperplasia in saphenous vein grafts.
Journal of Vascular Surgery Cases and Innovative Techniques
, 6
(2)
pp. 292-306.
10.1016/j.jvscit.2019.09.009.
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Abstract
Stenosis due to neointimal hyperplasia (NIH) is among the major causes of peripheral graft failure. Its link to abnormal hemodynamics in the graft is complex, and isolated use of hemodynamic markers is insufficient to fully capture its progression. Here, a computational model of NIH growth is presented, establishing a link between computational fluid dynamics simulations of flow in the lumen and a biochemical model representing NIH growth mechanisms inside the vessel wall. For all three patients analyzed, NIH at proximal and distal anastomoses was simulated by the model, with values of stenosis comparable to the computed tomography scans.
| Type: | Article |
|---|---|
| Title: | Multiscale, patient-specific computational fluid dynamics models predict formation of neointimal hyperplasia in saphenous vein grafts |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.jvscit.2019.09.009 |
| Publisher version: | https://doi.org/10.1016/j.jvscit.2019.09.009 |
| Language: | English |
| Additional information: | Copyright © 2020 The Authors. Published by Elsevier Inc. on behalf of Society for Vascular Surgery. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). |
| Keywords: | Computational fluid dynamics, Multiscale modeling, Neointimal hyperplasia, Shear stress, Vein grafts |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10103653 |
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