Rodden Middleton

Background: As of September 2022, there was no globally recommended set of core data elements for use in multiple sclerosis (MS) healthcare and research. As a result, data harmonisation across observational data sources and scientific collaboration is limited. Objectives: To define and agree upon a core dataset for real-world data (RWD) in MS from observational registries and cohorts. Methods: A three-phase process approach was conducted combining a landscaping exercise with dedicated discussions within a global multi-stakeholder task force consisting of 20 experts in the field of MS and its RWD to define the Core Dataset. Results: A core dataset for MS consisting of 44 variables in eight categories was translated into a data dictionary that has been published and disseminated for emerging and existing registries and cohorts to use. Categories include variables on demographics and comorbidities (patient-specific data), disease history, disease status, relapses, magnetic resonance imaging (MRI) and treatment data (disease-specific data). Conclusion: The MS Data Alliance Core Dataset guides emerging registries in their dataset definitions and speeds up and supports harmonisation across registries and initiatives. The straight-forward, time-efficient process using a dedicated global multi-stakeholder task force has proven to be effective to define a concise core dataset.

Background New interventions for multiple sclerosis (MS) commonly require a demonstration of cost-effectiveness using health-related quality of life (HRQoL) utility values. The EQ-5D is the utility measure approved for use in the UK NHS funding decision-making. There are also MS-specific utility measures – e.g., MS Impact Scale Eight Dimensions (MSIS-8D) and MSIS-8D-Patient (MSIS-8D-P). Objectives Provide EQ-5D, MSIS-8D and MSIS-8D-P utility values from a large UK MS cohort and investigate their association with demographic/clinical characteristics. Methods UK MS Register data from 14,385 respondents (2011 to 2019) were analysed descriptively and using multivariable linear regression, with self-report Expanded Disability Status Scale (EDSS) scores. Results The EQ-5D and MSIS-8D were both sensitive to differences in demographic/clinical characteristics. An inconsistency found in previous studies whereby mean EQ-5D values were higher for an EDSS score of 4 rather than 3 was not observed. Similar utility values were observed between MS types at each EDSS score. Regression showed EDSS score and age were associated with utility values from all three measures. Conclusions This study provides generic and MS-specific utility values for a large UK MS sample, with the potential for use in cost-effectiveness analyses of treatments for MS.