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Effect of status-dependent variation in admission and discharge times for cohort studies of stroke survivors


   Faculty of Biology, Medicine and Health

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  Prof A Vail, Dr Matt Gittins, Prof Craig Smith, Dr Amit Kishore  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Stroke is a common and frequently severe condition. Characterised by sudden onset of clinical symptoms, stroke progresses rapidly in the hours following ictus, leading to the mantra “time is brain”. Health services seek to provide specialist stroke care as early as possible.

There is global interest in studying prognosis for stroke survivors and in seeking therapeutic interventions to minimise the long-term consequences of resulting brain damage. Studies necessarily rely on assessments of stroke severity, both clinical and brain imaging, made as early as possible following hospital admission. However, the time taken to arrive in hospital is variable and, to some extent, dependent itself on the severity of the stroke. Furthermore, only those arriving within a set time-window are eligible for some evidence-based interventions such as thrombectomy and thrombolysis.

Patient outcomes are routinely recorded at discharge. Time to discharge is also highly variable. As with time to admission, time to discharge is partially dependent on patient condition. However, substantial in-hospital routine data may provide valuable insights into the nature and extent of this dependency.

This project will explore the statistical consequences of this dependence on patient status for the timing of both ‘baseline’ and ‘outcome’ in cohort studies. First, the student will review statistical methods published within this context. A search of methods applied in other fields will identify candidates for consideration in a method-comparison study. Motivating data will be available from the Northern Care Alliance, which hosts the Greater Manchester Comprehensive Stroke Centre and has electronic patient records detailing stroke admissions for approximately 3000 Salford residents. As well as developing statistical models of times to admission and discharge, these data may be used to determine ranges for key parameters within simulation studies.

Entry Requirements

Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related area/subject. Candidates with previous laboratory experience are particularly encouraged to apply.

How To Apply

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor. On the online application form select the appropriate subject title.

For international students, we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences.

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/”


Funding Notes

Applications are invited from self-funded students. This project has a Band 1 fee. Details of our different fee bands can be found on our website https://www.bmh.manchester.ac.uk/study/research/fees/

References

Pullenayegum & Lim. SMMR 2016; 25(6) 2992–3014. DOI: 10.1177/0962280214536537
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