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): 2369–429, 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. To apply for this project applicants should complete a<a href="https://medicinehealth.leeds.ac.uk/downloads/download/129/faculty_graduate_school_-_application_form"> Faculty Application Form</a> and send this alongside a full academic CV, degree transcripts (or marks so far if still studying) and degree certificates to the Faculty Graduate School <a href="mailto:email@example.com">firstname.lastname@example.org</a></p> <p>We also require 2 academic references to support your application. Please ask your referees to send these <a href="https://medicinehealth.leeds.ac.uk/downloads/download/130/faculty_graduate_school_-_scholarship_reference_form">references</a> on your behalf, directly to <a href="mailto:email@example.com">firstname.lastname@example.org</a></p> <p>If you have already applied for other projects using the Faculty Application Form this academic session you do not need to complete this form again. Instead you should email fmhgrad to inform us you would like to be considered for this project.</p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p>
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<h3 class="heading heading--sm">Linked research areas</h3>