Stroke is the leading cause of adult disability in the UK. Visual impairment is common in stroke affecting two thirds of all stroke survivors of which many are permanent and severe visual impairments. A considerable proportion of stroke survivors who have visual problems go unrecognised, thus receiving no advice or management. Despite consistent findings that inclusion of vision services within the stroke unit is highly beneficial, such visual assessment is not common and services are inconsistent throughout the UK with at least half of stroke units having no, or ad-hoc, services for vision care.
In this study we will exploit available data from the Sentinel Stroke National Audit Programme (SSNAP). In the North West Coast (NWC) we have access to detailed SSNAP data alongside a database with detailed visual impairment data; data which is not available elsewhere in the UK. Further, this project will also link to the prospective Liverpool Heart And bRain (L-HARP) project and academic stroke medicine work from the Liverpool Centre for Cardiovascular Science (https://twitter.com/LiverpoolCCS).
We aim to identify whether the percentage of stroke survivors documented through NWC SSNAP data is comparable to nationwide stroke units and identify differences and similarities of stroke screening data in comparison to results from specialist visual assessment. Clearly specialist visual assessment will detect most, if not all, cases of post-stroke visual impairment. Basic screening does not. This comparative study will, however, inform us about what is missed by screening and identify from detailed visual assessments just how important these ‘missed’ visual impairments are.
Further data to extract will include age, gender, stroke type/laterality/severity, medical history, postcode and socio-economic information. This will enable additional inference about generalisability, in addition to outcome prediction. We will further extract data on length of hospital stay. This is a key area to explore as there is no data yet available as to whether provision of vision care on acute stroke units makes any measureable difference to hospital stay. Retrospective data and risk models from SSNAP will be tested in the prospective L-HARP cohort. Depending on the interests of the candidate, there will be the opportunity to integrate artificial intelligence and machine learning methodology linking post-stroke visual impairment to clinical features and prognosis.
These are important aspects to evaluate in order to guide and improve service provision not only at local level, but also at a national level.
For any enquiries please contact Professor Fiona Rowe at: [Email Address Removed].
Application is by CV and covering letter. The covering letter must detail your interest in the studentship, related experience and training and suitability for the position. Applications are to be sent to Professor Fiona Rowe, [Email Address Removed].