Progression risk stratification with six-minute walk gait speed trajectory in multiple sclerosis

Abstract

BackgroundMultiple Sclerosis (MS) disease progression has notable heterogeneity among patients and over time. There is no available single method to predict the risk of progression, which represents a significant and unmet need in MS.MethodsMS and healthy control (HC) participants were recruited for a 2-year observational study. A latent-variable growth mixture model (GMM) was applied to cluster baseline 6-min walk gait speed trajectories (6MWGST). MS patients within different 6 MWGST clusters were identified and stratified. The group membership of these MS patients was compared against 2-year confirmed-disease progression (CDP). Clinical and patient-reported outcome (PRO) measures were compared between HC and MS subgroups over 2 years.Results62 MS and 41 HC participants completed the 2-year study. Within the MS cohort, 90% were relapsing MS. Two distinct patterns of baseline 6 MWGST emerged, with one cluster displaying a faster gait speed and a typical “U” shape, and the other showing a slower gait speed and a “flattened” 6 MWGST curve. We stratified MS participants in each cluster as low- and high-risk progressors (LRP and HRP, respectively). When compared against 2-year CDP, our 6 MWGST approach had 71% accuracy and 60% positive predictive value. Compared to the LRP group, those MS participants stratified as HRP (15 out of 62 MS participants), were on average 3.8 years older, had longer MS disease duration and poorer baseline performance on clinical outcomes and PROs scores. Over the subsequent 2 years, only the HRP subgroup showed a significant worsened performance on 6 MW, clinical measures and PROs from baseline.ConclusionBaseline 6 MWGST was useful for stratifying MS participants with high or low risks for progression over the subsequent 2 years. Findings represent the first reported single measure to predict MS disease progression with important potential applications in both clinical trials and care in MS.