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Transitional Flow in the Venous Side of Patient-Specific Arteriovenous Fistulae for Hemodialysis

Overview of attention for article published in Annals of Biomedical Engineering, December 2015
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Title
Transitional Flow in the Venous Side of Patient-Specific Arteriovenous Fistulae for Hemodialysis
Published in
Annals of Biomedical Engineering, December 2015
DOI 10.1007/s10439-015-1525-y
Pubmed ID
Authors

Michela Bozzetto, Bogdan Ene-Iordache, Andrea Remuzzi

Abstract

Arteriovenous fistula (AVF) is the first choice for providing vascular access for hemodialysis patients, but maintaining its patency is challenging. AVF failure is primarily due to development of neointimal hyperplasia (NH) and subsequent stenosis. Using idealized models of AVF we previously suggested that reciprocating hemodynamic wall shear is implicated in vessel stenosis. The aim of the present study was to investigate local hemodynamics in patient-specific side-to-end AVF. We reconstructed realistic geometrical models of four AVFs from magnetic resonance images acquired in a previous clinical study. High-resolution computational fluid dynamics simulations using patient-specific blood rheology and flow boundary conditions were performed. We then characterized the flow field and categorized disturbed flow areas by means of established hemodynamic wall parameters. In all AVF, either in upper or lower arm location, we consistently observed transitional laminar to turbulent-like flow developing in the juxta-anastomotic vein and damping towards the venous outflow, but not in the proximal artery. High-frequency fluctuations of the velocity vectors in these areas result in eddies that induce similar oscillations of wall shear stress vector. This condition may importantly impair the physiological response of endothelial cells to blood flow and be responsible for NH formation in newly created AVF.

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Geographical breakdown

Country Count As %
Netherlands 1 1%
Unknown 84 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 18%
Student > Master 14 16%
Researcher 9 11%
Student > Bachelor 8 9%
Student > Doctoral Student 4 5%
Other 14 16%
Unknown 21 25%
Readers by discipline Count As %
Engineering 34 40%
Medicine and Dentistry 11 13%
Nursing and Health Professions 2 2%
Agricultural and Biological Sciences 2 2%
Psychology 2 2%
Other 4 5%
Unknown 30 35%