032 Systematic review of prediction models in relapsing remitting multiple sclerosis. Issue 6 (27th May 2022) - Brown, Fraser; Kearns, Patrick; Glasmacher, Stella; Hunt, David; Connick, Peter; Chandran, Siddharthan
Abstract : Background: Prediction of individual disease course in relapsing remitting multiple sclerosis (RRMS) is chal- lenging. This has important implications for informed decision making in clinical practice. Objectives: To conduct a systematic review in order to establish methodological quality of published prediction models in RRMS. Methods: We searched Medline, Embase and Web of Science. Reviewers screened abstracts and full text for eligibility and assessed risk of bias. Studies reporting development or validation of prediction models for adults with RRMS were included. Data collection was guided by checklist for critical appraisal and data extraction for systematic reviews and applicability and methodological quality assessment by prediction model risk of bias assessment tool. Results: 30 studies were included. Applicability was assessed as high risk of concern in 29 studies. Risk of bias was assessed as high for all studies. The single most frequently included predictor was baseline EDSS (n=11). T2 Lesion volume or number and brain atrophy were each retained in seven studies. Five studies included external validation. None included impact analysis. Conclusions: Although a number of prediction models for RRMS have been reported, most are at high risk of bias and lack external validation and impact analysis, restricting their application in clinical practice. fraser@tonnard.co.uk