Patient-specific computational haemodynamics associated with surgical creation of an arteriovenous fistula

Abstract

Despite arteriovenous fistulae (AVF) being the preferred vascular access for haemodialysis, high primary failure rates (30-70%) and low one-year patency rates (40-70%) hamper their use. The haemodynamics within the vessels of the fistula change significantly following surgical creation of the anastomosis and can be a surrogate of AVF success or failure. Computational fluid dynamics (CFD) can crucially predict AVF outcomes through robust analysis of a fistula’s haemodynamic patterns, which is impractical in-vivo. We present a proof-of-concept CFD framework for characterising the AVF blood flow prior and following surgical creation of a successful left radiocephalic AVF in a 20-year-old end-stage kidney disease patient. The reconstructed vasculature was generated utilising multiple contrast-enhanced magnetic resonance imaging (MRI) datasets. Large eddy simulations were conducted for establishing the extent of arterial and venous remodelling. Following anastomosis creation, a significant 2-3-fold increase in blood flow rate was induced downstream of the left subclavian artery. This was validated through comparison with post-AVF patient-specific phase-contrast data. The increased flow rate yielded an increase in time-averaged wall shear stress (TAWSS), a key marker of adaptive vascular remodelling. We have demonstrated TAWSS and oscillatory shear distributions of the transitional-flow in the venous anastomosis are predictive of AVF remodelling.

Characterization of Flow Dynamics in the Pulmonary Bifurcation of Patients With Repaired Tetralogy of Fallot: A Computational Approach

“…Magnetic resonance (MR) and computed tomography (CT) has enabled the reconstruction of patient-specific models of detailed anatomic variations (7,8), while 2D phase-contrast MRI is used to extract flow information from a vessel's cross-sectional lumen (9,10). Such techniques are now routinely performed and are used to assess the hemodynamic conditions in both healthy (11) and diseased subjects (12), under normal or exercise conditions (13), in order to compare morphological and geometrical features (14) to predict surgical outcomes (15)(16)(17) and facilitate the treatment and device design (18).…”