Ninno, Federica; (2025) Patient-specific hemodynamics and neointimal hyperplasia predictions in vascular grafts. Doctoral thesis (Ph.D), UCL (University College London).

Preview

Text Ninno_10207186_Thesis.pdf Download (62MB) | Preview

Abstract

Excessive neointimal hyperplasia (NIH) growth, caused by smooth muscle cell proliferation and extracellular matrix deposition, is the primary cause of failure in vascular grafts, leading to vessel narrowing and reduced blood flow. Despite its prevalence, the timescale of clinical stenosis (≥ 50% vessel diameter reduction) remains poorly understood. In this context, in silico models can be instrumental in computing hemodynamic markers, such as wall shear stress (WSS)-related indices, as potential predictors of stenosis. However, the reliability of these indices is hindered by fragmented clinical datasets. This thesis aims to address this challenge by leveraging available clinical datasets and inform computational fluid dynamics (CFD) simulations to predict areas more prone to NIH development in two commonly encountered vascular grafts: lower-limb vein and arteriovenous grafts. The first part of the thesis examines how varying inlet boundary conditions (BCs) influence the calculation of WSS-related indices in CFD simulations of hemodynamics in lower-limb vein grafts. Patient-specific reconstructions were obtained from computed tomography angiography (CTA) scans, while duplex ultrasound (DUS) images provided inlet BCs. Results showed that, even with fragmented datasets (CTA and DUS acquired at different times), the same areas prone to stenosis were consistently identified through hemodynamic analyses. Building on these results, an existing multiscale mechanistic model — linking patient hemodynamics to biochemical mechanisms of NIH growth — was refined to predict NIH development in two patients undergoing lower-limb vein graft surgery. The model sensitivity to varying inlet BCs was assessed to account for the common scenario of fragmented datasets, and the model showed promise in predicting the timescale for stenosis progression, particularly in the long-term stenosis case. Finally, a fully patient-specific CFD workflow was developed to link altered hemodynamics to NIH development in arteriovenous grafts, addressing the limitations of prior studies relying on idealised models. Using a retrospective dataset of CTA scans and DUS images, the workflow was applied to a single patient, leading to the identification of some hemodynamic indices as stronger potential predictors of NIH growth. This thesis represents the first effort to leverage fragmented clinical data to identify areas at higher risk of stenosis and quantify NIH growth in lower-limb vein grafts. Additionally, it establishes a robust patient-specific CFD workflow that can be applied to larger datasets in future studies on arteriovenous grafts.

Download activity - last month

Export as CSVXMLJSON

Download activity - last 12 months

Export as XMLJSONCSV

Downloads by country - last 12 months

Export as XMLJSONCSV

Archive Staff Only

View Item