Stroke is a leading cause of death and disability globally. Lower limb impairments following stroke can have a significant impact on participation in daily activities, increase foot-related disability, and underpin poorer health outcomes. Interventions targeted at foot and lower limb impairments are advocated, but very little is understood about the typical problems in stroke survivors, their associations with outcomes like disability and quality of life, or which patients are most severely affected. This PhD programme will adopt a data-driven approach to supporting the transformation of rehabilitation of lower limb problems in stroke survivors. Through the analysis of large electronic health records (EHR) databases this will provide vital insights into the types of problems stroke survivors experience, while linkage to detailed stroke research registry data will deliver broader understanding of the impact of different problems in different groups of patients leading to strategies to personalise intervention.
1. Systematically review current evidence on the prevalence and impact of lower limb problems in stroke survivors
2. Estimate the prevalence and incidence of foot and ankle problems in stroke survivors using relevant code lists applied to national primary care data
3. Model the association between foot and ankle problems and outcomes including, disability, quality of life and mortality from local primary care and stroke register data
4. Apply statistical and machine learning methods to a prediction model to predict lower limb problems from linked acute stroke and primary care data

