Improving the diagnosis and treatment of peripheral artery disease using computational modelling

1st Supervisor - Jordi Alastruey
2nd Supervisor - Dr Ronak Rajani

Peripheral artery disease (PAD) is a common condition in which atherosclerotic lesions narrow lower-limb arteries, reducing blood flow to the legs. It is the third leading cause of vascular morbidity and mortality after coronary artery disease (CAD) and stroke. Early detection and effective treatment of PAD are necessary to reduce morbidity and mortality. However, PAD is often under-diagnosed and a clear treatment strategy has yet to be established. Current diagnosis and treatment strategies usually rely on (i) very basic interpretation of imaging data and (ii) data from population studies that do not account for the inter-patient variability in the expression of atherosclerosis, nor the patientspecific responses to occluding lesions.

This project will address these issues using a cardiovascular modelling and computational approach that has previously been effectively employed for an enhanced understanding, diagnosis and treatment of CAD, but which is yet to be applied within the context of PAD.

This project will develop a multi-scale cardiovascular modelling framework tailored to the specificities of PAD that will allow us to provide efficient and accurate diagnostic tools, as well as valuable patient-specific, help-to-decision data for the treatment of PAD. The framework will be based on an approach that has previously been used for CAD.1,2 The project objectives are:

Obj. 1: Development of a computational modelling framework for the study of PAD;
Obj. 2: In vitro validation of the computational framework;
Obj. 3: Clinical feasibility study. The following tasks will be carried out to fulfil these objectives:

Task 1: Computational model development (18 months) – The framework will simulate detailed blood flow patterns in locally complex arterial geometries with stenosis, as well as global flow patterns in the systemic circulation. It will be based on our in-house tools created to assess blood flow in the aorta of hypertensive patients,3,4 and will include the following geometric scales: (i) detailed macro-vascular stenosis scale accounting for anatomically-correct arterial geometries reconstructed from computed tomography angiography (CTA) data; (ii) averaged macro-vascular scale describing pulse wave propagation in the larger systemic arteries; and (iii) micro-vascular scale in the capillary network which is responsible for the collateral perfusion observed in PAD patients and which could play an important role in guiding treatment. The model will be used to develop novel haemodynamic biomarkers for accurate PAD diagnosis and assess the optimal treatment procedure (e.g. pharmacotherapy, stenting, revascularisation).

Task 2: In vitro validation (9 months) – Our EPSRC-funded, state-of-the-art, 1:1 scale cardiovascular simulator rig (CVSR) of the heart and larger systemic arteries includes peripheral arteries of the lower limbs.5 Different types and degrees of lower-limb atherosclerotic lesions (e.g. single, sequential, diffuse) will be (i) designed based on real lesions’ geometry, (ii) manufactured using 3D printing, and (iii) inserted into the lowerlimb arteries of the CVSR. This will be used to validate the simulated blood pressure and flow data, especially in scenarios with complex sequential and diffuse lesions.

Task 3: In vivo validation (9 months) – The novel biomarkers developed in Task 1 and the ability of the computational framework to predict optimal treatment procedures will be tested in existing patient cohorts (with a variety of PAD symptoms) from Dr Rajani’s clinic, for which haemodynamic measurements and CTA imaging data are available pre- and post-operatively. The project will improve PAD diagnosis and treatment by providing clinicians with a novel tool that combines haemodynamic measurements and medical images using validated, state-of-the-art, patient-specific models of blood flow. The integration of such tools in clinical routine is anticipated to improve PAD patient morbidity and mortality.