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Statistical and Machine Learning Methods to Predict Glucocorticoid Toxicity Outcomes for Patients with Immune-Mediated Inflammatory Diseases


Key facts

Type of research degree
Application deadline
Ongoing deadline
Country eligibility
International (open to all nationalities, including the UK)
Competition funded
Dr Sofya Titarenko
Additional supervisors
Professor Ann Morgan, Dr Mark Iles
School of Mathematics
Research groups/institutes
Modern applied statistics, Statistics
<h2 class="heading hide-accessible">Summary</h2>

Immune-Mediated Inflammatory Diseases (IMIDs) represent a group of disorders which cause inflammation of various parts of the body due to the triggered immune system response. They affect more than 2 million people worldwide and make a serious impact on families, resulting in an additional burden on the health system. <br /> <br /> For example, Polymyalgia Rheumatica (PMR) and Giant Cell Arthritis (GCA) are very common types of autoimmune rheumatic disorders, one of the groups of IMIDs. PMR affects muscles around the neck, shoulders and hips, while GCA affects arteries, usually in the human head. These two disorders are mostly observed in the elderly and cause such symptoms as severe headaches, tiredness, vision problems, loss of weight, etc. <br /> <br /> Currently, there are no cures for IMIDs. However, certain medications are used to help with the symptoms. Glucocorticoids (GCs) are a class of steroid hormones predominantly used in the UK to reduce inflammation in IMIDs patients. However, it has been shown that the use of high doses of GCs is associated with an increased risk of Cardiovascular Disease (CVD). With recent advancements in data-driven techniques researchers look for patterns and hidden relationships between patients&rsquo; data and the risk/prediction of particular diagnoses. For example, in [1] a higher risk of CVD was confirmed for patients with 6 immune-mediated diseases on lower doses of GCs. In [2] are demonstrated models which aggregate predictions of CVD risk from a set of ML methods. <br /> <br /> In this project, the student will work with the Clinical Practice Research Datalink (CPRD). The data contain demographics, lifestyle, medication, and diagnosis (type of IMIDs and CVDs) and are linked with hospital records (HES, and mortality data taken from the Office of National Statistics (ONS,;size=10). The student will test how Statistical and Machine Learning methods help with the prediction of CVDs outcome/severity for IMIDs patients who are taking GC medication. The student will look into methods to identify important features which influence CVDs outcome/severity and work on the development of predictive ML-based models. The methodology is to be validated using UK Biobank data ( <br />

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

<p><span style="font-size:12pt"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="font-size:14.0pt"><span style="font-family:&quot;Calibri&quot;,sans-serif">References</span></span></span></span></p> <p><span style="font-size:12pt"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="font-family:&quot;Calibri&quot;,sans-serif">[1] Pujades-Rodriguez&nbsp;M, Morgan&nbsp;AW, Cubbon&nbsp;RM, Wu&nbsp;J (2020)&nbsp;<em><span style="font-family:&quot;Calibri&quot;,sans-serif">Dose-dependent oral glucocorticoid cardiovascular risks in people with immune-mediated inflammatory diseases: A population-based cohort study.</span></em> PLOS Medicine 17(12): e1003432.&nbsp;<a href="" style="color:blue; text-decoration:underline"></a></span></span></span></p> <p><span style="font-size:12pt"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="font-family:&quot;Calibri&quot;,sans-serif">[2] Alaa, A. M., Bolton, T., Di Angelantonio, E., Rudd, J., &amp; van der Schaar, M. (2019). <em><span style="font-family:&quot;Calibri&quot;,sans-serif">Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.</span></em>&nbsp;PloS one,&nbsp;14(5), e0213653. <a href="" style="color:blue; text-decoration:underline"></a></span></span></span></p> <p style="margin-bottom:11px">&nbsp;</p> <h3>&nbsp;</h3> <p>&nbsp;</p> <p>&nbsp;</p>

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

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="">University&#39;s website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>PhD Statistics FT</strong></em> and in the research information section&nbsp;that the research degree you wish to be considered for is <em><strong>Statistical and Machine Learning Methods to Predict Glucocorticoid Toxicity Outcomes for Patients with Immune-Mediated Inflammatory Diseases</strong></em> as well as&nbsp;<em><strong>Dr Sofya Titarenko</strong></em>&nbsp;as your proposed supervisor.</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> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p class="MsoNoSpacing">Applications will be considered on an ongoing basis. &nbsp;Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application. &nbsp;We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p>Please note that you must provide the following documents at the point you submit your application:</p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> <li>Funding information including any alternative sources of funding that you are applying for or if you are able to pay your own fees and maintenance</li> </ul>

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

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

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

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<h2 class="heading">Funding on offer</h2>

<p style="margin-bottom:12px"><strong>Self-Funded or externally sponsored students are welcome to apply.</strong></p> <p><strong>UK&nbsp;</strong>&ndash;&nbsp;The&nbsp;<a href="">Leeds Doctoral Scholarships</a> and <a href="">Leeds Opportunity Research Scholarship</a> are available to UK applicants. <a href="">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p><strong>Non-UK</strong> &ndash;The&nbsp;<a href="">China Scholarship Council - University of Leeds Scholarship</a>&nbsp;is available to nationals of China. The&nbsp;<a href="">Leeds Marshall Scholarship</a>&nbsp;is available to support US citizens. <a href="">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p><strong>Important:</strong>&nbsp; Any costs associated with your arrival at the University of Leeds to start your PhD including flights, immigration health surcharge/medical insurance and Visa costs are <strong>not </strong>covered under these studentships.</p> <p>Please refer to the <a href="">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals.</p>

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

<p>For general enquiries about applications, contact our admissions team:<br /> <a href=""></a></p> <p>For questions about the research project, please contact Dr Sofya Titarenko at <a href=""></a></p>

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