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Computational phenotyping of cardiovascular disease following acute myocardial infarction in primary care: a large scale electronic healthcare record

PGR-P-181

Key facts

Type of research degree
4 year PhD
Application deadline
Ongoing deadline
Country eligibility
International (outside UK)
Funding
Non-funded
Supervisors
Professor Chris Gale and Dr Marlous Hall
Schools
School of Medicine
Research groups/institutes
Leeds Institute of Cardiovascular and Metabolic Medicine
<h2 class="heading hide-accessible">Summary</h2>

Cardiovascular disease remains the leading cause of death worldwide, contributing to 30% of all deaths globally. Presently, most patients with acute myocardial infarction (AMI; heart attacks) are elderly and more patients are living longer following AMI due to increased use of guideline recommended care. To date, there are no large scale population based studies providing high resolution insights into the healthcare burden following AMI. Such large scale population based studies are increasingly important as they provide insights into patterns of care and outcomes for &amp;lsquo;real world&amp;rsquo; populations in clinical settings, unlike the strictly controlled environments of randomised clinical trials.

<h2 class="heading hide-accessible">Full description</h2>

<p>This project will apply advanced statistical techniques to large scale electronic health record data from primary care to discover the multimorbidity disease profiles of patients following AMI. Such disease profiles may result in the development of novel disease phenotypes (so called &lsquo;data-driven computational phenotypes&rsquo;) which can inform future clinical trial designs as well as lead to development of clinical guidelines in areas where representative trials spanning a number of multimorbid conditions are unlikely or impractical.</p> <p>&nbsp;</p> <p>The student will work within a multidisciplinary team of statisticians, epidemiologists and clinicians under the overarching Cardiovascular Survivorship theme. The project offers the opportunity to lead on, and contribute to, high impact peer-reviewed publications (for example [1-3]), as well as the opportunity to further develop advanced applied epidemiological and data analytical skills for the effective, efficient and clinically relevant use of data to ultimately improve patient outcomes.</p> <p><strong>References:</strong></p> <p>M Hall, TB Dondo, AT Yan, MA Mamas, AD Timmis, JE Deanfield, T Jernberg, H Hemingway, KAA Fox, CP Gale. Multimorbidity and survival for patients with acute myocardial infarction in England and Wales: Latent class analysis of a nationwide population-based cohort. Plos Medicine [In Press]</p> <p>TB Dondo, M Hall, RM West et al. Beta-Blockers and mortality after acute myocardial infarction in patients without heart failure or ventricular dysfunction. Journal of the American College of Cardiology (2017) 69(22):2710-2720.</p> <p>M. Hall, TB Dondo, A. Yan et al. Association of clinical factors and therapeutic strategies with improvements in survival following non ST-elevation myocardial infarction, 2003-2013. Journal of the American Medical Association (2016) 316(10):1073-1082.</p>

<h2 class="heading">How to apply</h2>

<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:fmhpgradmissions@leeds.ac.uk">fmhpgradmissions@leeds.ac.uk</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:fmhpgradmissions@leeds.ac.uk">fmhpgradmissions@leeds.ac.uk</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 fmhpgradmissions 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&#39;s minimum English language requirements (below).</p>

<h2 class="heading heading--sm">Entry requirements</h2>

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.

<h2 class="heading heading--sm">English language requirements</h2>

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 Faculty of Medicine and Health minimum requirements in IELTS and TOEFL tests for PhD, MSc, MPhil, MD are: &acirc;&euro;&cent; British Council IELTS - score of 7.0 overall, with no element less than 6.5 &acirc;&euro;&cent; 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.

<h2 class="heading">Contact details</h2>

<p>For further information please contact the Graduate School Office<br /> e:<a href="mailto:fmhpgradmissions@leeds.ac.uk">fmhpgradmissions@leeds.ac.uk</a>, t: +44 (0)113 343 8221.</p>


<h3 class="heading heading--sm">Linked research areas</h3>