OBJECTIVES: Currently, two key real-world multiple sclerosis (MS) datasets exist that can be utilized in HTA to characterize natural disease course (London Ontario and British Columbia). Their usability in decision-making has been questioned, given their historic nature (1970s and 1980s respectively) and the changes to treatment, and understanding of natural history. This study aims to establish up-to-date UK patient characterization and determine the current treatment paradigm in MS.

METHODS: The UK MS Register (UKMSR) has captured real world data since 2011 and collected longitudinally via Patient Reported Outcomes (PRO) and hospital-linked medical records (clinical). A proportion of patient’s have data sets linked between their PRO responses and clinical submissions. A cross-sectional analysis of UKMSR patients was undertaken to describe their demographics, clinical characteristics, and treatments.

RESULTS: The total number of patients with patient and clinician-recorded data was 21,147 and 11,924, respectively (not mutually exclusive). PRO and clinical linked data was available for 5,500 patients. The overall patients identified spanned the devolved UK Nations, as follows (PRO/Clinical); England (19,932/10,887), Wales (1,354/364), Scotland (2,019/4) and Northern Ireland (842/665).

Mean follow-up time for patients across clinical datasets was 806 days, median at 371 days with portal participants providing mean at 2.4 years data. Less than 10% of patients had longitudinal data >12 years. Data from further analyses will be presented.

CONCLUSIONS: The study will allow future healthcare decisions to be made utilizing high-quality up-to-date real-world evidence to characterize MS patients in the UK. This will ensure the uncertainty involved in assessing the value of new treatments is reduced. Study findings will also provide greater understanding of the UK patient population and treatment paradigms. Future analyses will investigate the longitudinal outcomes in MS, transition between different disease phenotypes and associated resource use. This research will ultimately inform a decision model to evaluate new treatments for MS.