Supervisors: Prof Julian Gunn, Prof Rod Hose
Physiological measures are of critical importance to make the right therapeutic decisions for individuals. For patients with coronary artery disease, fractional flow reserve (FFR), a measure of the restriction of blood flow through a narrowed vessel using a pressure-sensitive wire, has proved more successful than simply making a visual assessment of the degree of anatomical narrowing on medical images (the angiogram) because the human eye is inaccurate. In general, an FFR value of 0.80 has been validated as useful threshold at a given lesion, below which treatment is valuable, and above which it confers little or no benefit.
We have developed a track record in the computational estimation of FFR from medical images (angiograms). Angiograms are obtained on almost all patients with severe coronary disease, (250,000 in the UK p.a.) whereas FFRs, using a pressure wire, are not. Therefore our FFR models have the potential to dispense with the need for inserting the pressure wire, but more importantly provide an FFR for patients who otherwise would not have one at all. We can currently accurately model FFR in a well imaged coronary artery. Other systems have been developed, but ours incorporates clinical parameters to allow personalised values.
The inputs will be clinical parameters collected from patients with coronary artery disease undergoing assessment in the PI’s NHS clinic. These will include a full set of basic demographic and clinical data and angiograms obtained before and after intervention. A subset of these will have FFR measured with a pressure wire, comprising the validation set. The student will construct a model of comprehensive coronary blood supply at rest and during physical demand, incorporating the consequence of disease which might limit flow, a jeopardy score to indicate where the lesions are, and their impact, and then proceed to model any proposed treatment that might restore flow, to one or more lesions and arteries. The work will have three stages:
- To develop the comprehensive model. Modelled FFRs will be constructed for all major vessels. Individual modelled vessels will be combined to create a model of the overall ischaemic burden for the patient. Finally the change in FFR (ie change in flow) after intervention upon all the lesions requiring stenting (FFR<0.80) will be modelled, and the change in overall ischaemic burden estimated.
- To validate the system in a small number of individuals from the PIs’ cohort of patients undergoing angiography, baseline measured FFR, stenting, post stent angiography and post stent FFR, all in >1 vessel and for >1 lesion.
- To deploy the perfected system in 50 patients with multi-vessel disease who underwent coronary angiography only, and determine the appropriateness (or otherwise) of the treatment offered and given. This will reveal the true value of this new modelling technology.
A comprehensive model of coronary blood flow will provide a rationalised integrated treatment planning package enabling patients to have a ‘one stop shop’ of diagnosis with imaging and flow assessment, with the possibility of stenting at the same sitting.
The Faculty of Medicine, Dentistry and Health has received an allocation of three EPSRC studentships for 2019 entry from the Doctoral Training Partnership grant that is awarded to the University of Sheffield to fund PhD studentships in the EPSRC remit. These studentships will be 42 months in duration, and include home fee, stipend at RCUK rates and a research training support grant (RTSG) of £4,500.
Home/EU students must have spent the 3 years immediately preceding the start of their course in the UK to receive the full funding.
How to apply?
Please complete a University Postgraduate Research Application form available here: http://www.shef.ac.uk/postgraduate/research/apply Please clearly state the prospective main supervisor in the respective box and select Department of Infection, Immunity and Cardiovascular Disease as the department.
Interested candidates should in the first instance contact Professor Julian Gunn (firstname.lastname@example.org)