In-silico assessment tool for reducing the risk of failure of arterio-venous fistula (AVF) in patients subjected to haemodialysis
PhDs and postgraduate research
Funded PhD Project (UK and EU students only)
School of Mechanical and Design Engineering
4 May 2021 (12pm GMT)
Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these cover tuition fees at the UK rate for three years and a stipend in line with the UKRI rate (£15,609 for 2021/22). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables.
The work on this project will involve:
- Working in close collaboration with clinicians for an high impact multidisciplinary research project
- Elaborating a comprehensive subject-specific computational modelling procedure to inform clinicians in planning critical vascular surgeries, improving the clinical outcomes
- Developing and verifying a robust and rigorous experimental protocol to validate the numerical tools. In vitro models will be defined and refined to closely simulate clinically relevant conditions
Each year, around 5,400 new patients with kidney failure receive haemodialysis treatment across the UK. Effective haemodialysis requires access to the patient’s blood supply in large flow volumes (typically >600 ml/min). To this purpose, an access point is surgically created by connecting a vein and an artery. This is known as “Arterio-Venous Fistula” (AVF). Currently, surgeons mainly rely on their experience to create the AVF in a site they believe will result in the required flow-rate. However, according to the latest UK Renal Registry report (2016), on average only 27.8% of patients start their treatment with a successful AVF, demonstrating the inadequacy of the current clinical assessment.
The principal aim of this project is to demonstrate proof of concept for a predictive computational tool to inform the surgeon of the optimal location for creating an AVF. The model will be based on subject-specific data about arm vasculature, and will simulate the blood flow in different AVFs simulated locations. The optimal AVF site which produces the maximum flow-rate will be then indicated. To calibrate and validate the model, an in vitro setup of the patient’s local vasculature will be developed, using a Pulsatile Blood Pump, silicon or 3D printed blood vessels, water as the working fluid to assess (and compare) the flow rate in the different AVF locations considered by the numerical model.
This project is a joint collaboration between the Cardiovascular Engineering Research Laboratory (CERL) based at the School of Mechanical and Design Engineering, and the Vascular Assessment Unit at the Queen Alexandra Hospital. Patient specific data will be provided by the Queen Alexandra Hospital, and the model predictions will be fed-back to their consultants. Comparisons between the model outputs and the surgeon independent assessment will permit additional calibration.
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in Medical Engineering, Mechanical Engineering or related discipline. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
Solid experience with numerical modelling software, CAD software, MATLAB or equivalent structured programming languages, and interest in experimental validation tests is desirable.
How to apply
We’d encourage you to contact Dr Martino Pani at email@example.com to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. An extended statement as to how you might address the proposal would be welcomed.
Our ‘How to Apply’ page offers further guidance on the PhD application process.
If you want to be considered for this funded PhD opportunity you must quote project code SMDE6020521 when applying.