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From the Department of Neuroscience (D.M., J.S., C.Z., M.G., H.B., A.V.d.W.), Central Clinical School, Monash University; CORe (C.B., T. Kalincik), Department of Medicine at RMH, University of Melbourne; Department of Neurology (T. Kalincik, T. Kilpatrick), Royal Melbourne Hospital, Parkville; Department of Neurology (J.L.-S.), John Hunter Hospital; School of Medicine and Public Health (J.L.-S.), University of Newcastle; Florey Institute of Neuroscience and Mental Health (T. Kilpatrick, D.D.), Melbourne; Brain and Mind Centre (M.B.), University of Sydney; Department of Neurology (B.T.), Royal Hobart Hospital; Department of Neurology (D.D.), Box Hill Hospital; and Department of Neurology (H.B., A.V.d.W.), MSNI Service, Alfred Health, Melbourne, Australia.
Published: November 2021
AI Article Synopsis
Article Abstract
Background And Objectives: Longitudinal cognitive trajectories in multiple sclerosis are heterogeneous and difficult to measure. We aimed to identify discrete longitudinal reaction time trajectories in relapsing-remitting multiple sclerosis using a computerized cognitive battery and to assess the association between trajectories of reaction time and disability progression.
Methods: All participants serially completed computerized reaction time tasks measuring psychomotor speed, visual attention, and working memory. Participants completed at least 3 testing sessions over a minimum of 180 days. Longitudinal reaction times were modeled with latent class mixed models to identify groups of individuals sharing similar latent characteristics. Optimal models were validated for consistency and baseline associations with class membership tested using multinomial logistic regression. Interclass differences in the probability of reaction time worsening and the probability of 6-month confirmed disability progression were assessed with survival analysis.
Results: A total of 460 people with relapsing-remitting multiple sclerosis were included in the analysis. For each task of the MSReactor battery, the optimal model comprised 3 latent classes. All MSReactor tasks could identify a group with high probability of reaction time slowing. The visual attention and working memory tasks could identify a group of participants who were 3.7 and 2.6 times more likely to experience a 6-month confirmed disability progression, respectively. Participants could be classified into predicted cognitive trajectories after just 5 tests with 64% to 89% accuracy.
Discussion: Latent class modeling of longitudinal cognitive data collected by a computerized battery identified patients with worsening reaction times and increased risk of disability progression. Slower baseline reaction time, age, and disability increased assignment into this trajectory. Monitoring of cognition in clinical practice with computerized tests may enable detection of cognitive change trajectories and people with relapsing-remitting multiple sclerosis at risk of disability progression.
