LICAMM A machine learning approach to understand the disease trajectories of atrial fibrillation.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, with a lifetime risk of 1 in 4 in the general population and an increasing prevalence as the population ages. Effective treatment of patients with AF includes not only rate control and prevention of stroke, but also management of cardiovascular risk factors and comorbid diseases. Although AF is associated with increased risk of major cardiovascular events such as stroke and heart failure, the absolute and relevant event rates of these competing outcomes are not well described. The disease trajectories and the transitions among disease states after AF are lesser known given the uprising use of direct oral anticoagulants (DOACs) in recent years comparing with traditional anticoagulants, such as warfarin.

<p>The aim of the project is to study, using big health data and health informatics techniques, the disease trajectories of patients with atrial fibrillation. Patients with atrial fibrillation will be identified from Hospital Episode Statistics (estimated analytical cohort n ~ 1.2 million) and linked to primary care for comorbidities and prescription data. A machine learning approach through hierarchical clustering or neural networks will be used to explore the disease trajectory of atrial fibrillation through this big electronic health database. This project will provide a national overview of the profile of patients with atrial fibrillation and quantify the subsequent major cardiovascular and non-cardiovascular events and disease trajectories. Accessing national individual patient data will provide a high resolution and granularity to this AF population.</p> <p><strong>References:</strong></p> <p>John Camm et al. Guidelines for the management of atrial fibrillation: The Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC), European Heart Journal, 2010; 31 (19): 2369&ndash;429, https://doi.org/10.1093/eurheartj/ehq278</p> <p>David Conen. Epidemiology of atrial fibrillation, European Heart Journal, 2018; 39(16): 1323-4, https://doi.org/10.1093/eurheartj/ehy171</p> <p>Paulus Kirchhof. The future of atrial fibrillation management: integrated care and stratified therapy. Lancet, 2017; 290: 1873-87</p>

<p>Please note these are not standalone projects and applicants must apply to the PhD academy directly.</p> <p>Applications can be made at any time. You should complete an <a href="https://medicinehealth.leeds.ac.uk/faculty-graduate-school/doc/apply-2">online application form</a> and attach the following documentation to support your application.&nbsp;</p> <ul> <li>a full academic CV</li> <li>degree certificate and transcripts of marks (or marks so far if still studying)</li> <li>Evidence that you meet the programme&rsquo;s minimum English language requirements (if applicable, see requirement below)</li> <li>Evidence of funding to support your studies</li> </ul> <p>To help us identify that you are applying for this project please ensure you provide the following information on your application form;</p> <ul> <li>Select PhD in Medicine, Health &amp; Human Disease as your planned programme of study</li> <li>Give the full project title and name the supervisors listed in this advert</li> </ul>

A degree in biological sciences, dentistry, medicine, midwifery, nursing, psychology or a good honours degree in a subject relevant to the research topic. A Masters degree in a relevant subject may also be required in some areas of the Faculty. For entry requirements for all other research degrees we offer, please contact us.

Applicants whose first language is not English must provide evidence that their English language is sufficient to meet the specific demands of their study. The minimum requirements for this programme in IELTS and TOEFL tests are: &bull; British Council IELTS - score of 7.0 overall, with no element less than 6.5 &bull; TOEFL iBT - overall score of 100 with the listening and reading element no less than 22, writing element no less than 23 and the speaking element no less than 24.

<p>For further information please contact the Faculty Graduate School<br /> e:<a href="mailto:fmhpgradmissions@leeds.ac.uk">fmhpgradmissions@leeds.ac.uk</a></p>