The presence of helical flow can suppress areas of disturbed shear in parameterised models of an arteriovenous fistula.

The presence of helical flow can suppress areas of disturbed shear in parameterised models of an arteriovenous fistula.

Cunnane, Connor V;Cunnane, Eoghan M;Moran, Daniel T;Walsh, Michael T;
international journal for numerical methods in biomedical engineering 2019 pp. e3259
221
cunnane2019theinternational

Abstract

Areas of disturbed shear that develop following arteriovenous fistula (AVF) creation are believed to trigger the onset of intimal hyperplasia (IH), leading to AVF dysfunction. The presence of helical flow can suppress the flow disturbances that lead to disturbed shear in other areas of the vasculature. However, the relationship between helical flow and disturbed shear remains unevaluated in AVF. In this study, computational fluid dynamics (CFD) is used to evaluate the relationship between geometry, helical flow and disturbed shear in parameterised models of an AVF characterised by 4 different anastomosis angles. The AVF models with a small anastomosis angle demonstrate the lowest distribution of low/oscillating shear and are characterised by a high helical intensity coupled with a strong balance between helical structures. Contrastingly, the models with a large anastomosis angle experience the least amount of high shear, multidirectional shear, as well as spatial and temporal gradients of shear. Furthermore, the intensity of helical flow correlates strongly with curvature (r = 0.73, p < 0.001), whereas it is strongly and inversely associated with taper (r = -0.87, p < 0.001). In summary, a flow field dominated by a high helical intensity coupled with a strong balance between helical structures can suppress exposure to low/oscillating shear but is ineffective when it comes to other types of shear. This highlights the clinical potential of helical flow as diagnostic marker of exposure to low/oscillating shear, as helical flow can be identified in-vivo with the use of ultrasound imaging.

Access

Citation

ID: 42238
Ref Key: cunnane2019theinternational
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
42238
Unique Identifier:
10.1002/cnm.3259
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet