Title: Developing tomographic 3-D ultrasound for cardiovascular screening
Funder: Health Education England

Abstract: Research question: Can, in combination with patient risk factors, tomographic 3-D ultrasound (tUS) accurately/automatically detect features of plaque instability in asymptomatic carotid disease (CAD), therefore identify patients at increased risk of stroke/cardiovascular events (CVE), be good value-for-money for the NHS by measuring atherosclerotic burden/volume. Background: Cardiovascular disease (CVD) is the leading cause of death in the Western World; costing the UK economy 29 billion/year. As the long-term effects of CVD are largely preventable, early detection by population screening could massively reduce the frequency and cost of CVE. We know that screening the general population is not cost effective at a stroke rate of below 1.7% but in high-risk groups with greater disease prevalence it may be. CT and MR angiography will never be appropriate for screening because they involve radiation or are not quick. Alternatively, standard duplex ultrasound can quickly detect asymptomatic carotid disease but requires highly trained Vascular Scientists, of which there is a national shortage. Tomographic 3-D ultrasound (tUS) is quicker, requires less skill, and with development, could be deployed in the community. Principally, screening practitioners could be trained within a matter of weeks. However, measuring arterial disease is slow and currently still requires Vascular Scientists. Aims: To develop and understand the potential clinical and cost effectiveness of automating tUS to detect asymptomatic carotid artery disease (CAD) plus measure atherosclerotic burden in the clinical setting to predict patients who are at risk of stroke. Methods: I will measure carotid atherosclerotic burden (Carotid Plaque Volume or CPV) in 2570 high-risk patients from peripheral vascular or abdominal aortic aneurysm clinics. The prevalence of CAD is as high as 25%. Each patient will have tUS imaging of their aorta and carotid arteries to train AI to calculate CPV. CPV measured using artificial intelligence (AI) machine learning techniques will then be validated against manual measures on MRI and tUS imaging. Models able to predict CVE based on CPV and other established risk factors will then be developed. The cost-effectiveness/value-for money of utilising tUS for the NHS will be calculated under supervision from the Manchester Centre for Health Economics. Timelines: The research will take place over 3 years within the world centre of excellence for tUS vascular imaging alongside world leading experts. All patients will be recruited over the first 12 months and then followed up for 2 years before history on CVE is recorded. In project year 2 the measurements of atherosclerotic disease will be performed and automated. A risk prediction model and value-for-money will be calculated in year 3. Impact and dissemination: My project will deliver an automated CVD screening tool ready to be validated within a national screening programme that is proven to be cost-effective for the NHS. It will require little training to be utilised by screening practitioners and identify individual patients at high-risk of stroke. Results will be presented at international vascular conferences and published open access as well as advertised via social media, all with PPI involvement.

Lay abstract: Background: Diseases of the heart and circulation of the blood is the leading cause of death in Europe costing the UK, 29 billion per year. Treating the disease when patients get symptoms adds to the high cost and also causes harm to patients but these diseases can be prevented. Preventable diseases can be identified early and patients can be informed to enable them to take action about their lifestyle and if needed take medicines before the disease becomes significant. We know from previous research that when shown a diagram of their own disease, patients will take medications better, eat healthier and exercise more. Ultrasound scanning is a perfect tool to use to find disease before it becomes harmful (screening) as it is safe and portable My previous work tells me that a specific type of ultrasound that produces three-dimensional images can detect disease and measure it accurately. , However, there are not enough people trained to perform accurate scans to produce the pictures needed near where patients live. With development it may be possible to automate the measurements so that expensive and highly trained staff are not needed making it suitable for use in the NHS. Aim: to develop a personalised, cost-effective and automated screening tool using tomographic 3D ultrasound that will be combined with other risk factors to see if it can predict major cardiovascular events, such as stroke, amputation or death The overarching goal is to develop a new 3D ultrasound machine capable of screening people automatically to see if they have problems with their heart or blood circulation and assess if using this new machine is good value-for-money for the NHS to implement it into practice. Design: The research will take place within the world centre of excellence for tomographic 3D ultrasound imaging of the arteries/veins alongside world leading experts in understanding the costs to the NHS and the benefits to patients so we can understand if it is good value for money. I aim to recruit 2570 patients who are seeing a Vascular Doctor because they have either narrowing of the arteries in their legs, called Peripheral Arterial Disease (PAD), or a bulge of the main artery in the tummy, called an Abdominal Aortic Aneurysm (AAA). We know 25% of these patients will have a significant narrowing of the artery that supply's blood to the brain. This means they are high-risk for having a stroke. I will perform very quick (less than 1 min) tomographic 3D ultrasound scans and collect past medical histories. I will use the scans to develop the 3D ultrasound to be able to measure the narrowing automatically using artificial intelligence. This will be combined with medical history to see if we can predict who will have a stroke. PPI involvement: In setting my research priority I had the opportunity to meet with our existing patient and participant committees. Their overwhelming priority was for screening to detect problems before they develop. The committee helped limit the number of visits participants take and ensured I re-worded the questionnaires to ensure they are completed properly. Two members from each committee will sit on the study steering group that will meet regularly. Dissemination: Abstracts and presentations of results will be given at international conferences and our publications will be open access. Members of the PPI/steering groups will have input into approving the abstracts and publications.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CARDIOVASCULAR