Journal Article

3116 Longitudinal trajectories of digital upper limb biomarkers for multiple sclerosis

Foong, Yi Chao, Beek, Johan van, Walt, Anneke van der, Taylor, Bruce, Barnett, Michael, Gresle, Melissa, Buzzard, Katherine, Darby, David, Lechner-Scott, Jeannette, Zhu, Chao, Dobay, Pamela, Kilpatrick, Trevor, Kalincik, Tomas, Butzkueven, Helmut, Hyde, Robert, Merlo, Daniel

2024

Overview

BackgroundUpper limb dysfunction is a common debilitating feature of relapsing remitting multiple sclerosis (RRMS). We aimed to examine the longitudinal trajectory of the iPad-based Manual Dexterity Test (MDT) and predictors of change over time.MethodsWe prospectively enrolled relapsing-remitting multiple sclerosis (RRMS) patients with an EDSS score of less than four. Longitudinal data was analysed with mixed effect modelling and latent class mixed models. We examined whether group membership in latent classes were predictive of a confirmed decrease in MDT.ResultsAt a population level, MDT remained stable over time. No practice effect was seen. Baseline disability and T2 lesion volume were the strongest predictors of longitudinal MDT performance.Latent class analysis identified 2 classes of MDT trajectories. In the slower trajectory, greater variability and a weak association with sustained worsening of MDT was present. Group trajectory based on latent class analysis and baseline MDT was different, signifying the importance of latent processes in upper limb function in pwMS.ConclusionIn this cohort of mild to moderate RRMS, MDT scores remained stable over time with no evidence of a practice effect at a population level. Trajectory analysis based on latent class identified a cohort with greater variability and risk of sustained worsening. Our findings show the importance of latent processes in determining upper limb function trajectories in pwMS.

• Publisher: BMJ Publishing Group Ltd,BMJ Publishing Group LTD,BMJ Publishing Group
• Subject: Latent class analysis / Multiple sclerosis / Poster Abstracts