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  In-silico assessment tool for reducing the risk of failure of arterio-venous fistula (AVF) in patients subjected to haemodialysis

   School of Mechanical and Design Engineering

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  Dr Martino Pani, Dr Afshin Anssari-Benam, Dr Andrea Bucchi  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Applications are invited for a three year PhD.

The PhD will be based in the Faculty of Technology, and will be supervised by Dr Martino Pani, Dr Afshin Anssari-Benam and Dr Andrea Bucchi.

The work on this project will involve:
● working in close collaboration with clinicians for a 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
● develop and verify a robust and rigorous experimental protocol based on in vitro models that closely simulates clinically relevant conditions to validate the numerical tools

Each year, around 5,400 new patients with kidney failure receive haemodialysis treatment across 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.

General admissions criteria
You’ll need an upper second class honours degree from an internationally recognised university 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.

Specific candidate requirements
Experience with numerical modelling software, CAD software, MATLAB or equivalent structured programming languages, and interest to experimental validation tests is desirable.

How to Apply
We’d encourage you to contact Dr Martino Pani ([Email Address Removed]) 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.

Please quote project code SMDE4620220 when applying.

 About the Project