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011 Worsening longitudinal reaction time trajectories using the MSReactor computerised battery predicts confirmed EDSS progression
  1. Daniel Merlo1,
  2. Jim Stankovich1,
  3. Claire Bai2,
  4. Tomas Kalincik2,
  5. Melissa Gresle1,
  6. Jeannette Lechner-Scott3,
  7. Trevor Kilpatrick4,
  8. Michael Barnett5,
  9. Bruce Taylor6,
  10. David Darby4,
  11. Helmut Butzkueven1 and
  12. Anneke van der Walt1
  1. 1MSNI, Central Clinical School, Monash University, Melbourne, VIC, Australia
  2. 2CORe, Department of Medicine at RMH, University of Melbourne, Melbourne, VIC, Australia
  3. 3Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia
  4. 4Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
  5. 5Brain and Mind Centre, Sydney, NSW, Australia
  6. 6Department of Neurology, Royal Hobart Hospital, Hobart, TAS, Australia


Objectives To identify and validate longitudinal reaction time trajectories in relapsing remitting multiple sclerosis using a computerised cognitive battery and latent class mixed modelling, and to assess the association between reaction time trajectories and disability progression.

Methods Participants serially completed web-based computerised reaction time tasks measuring psychomotor speed, visual attention and working memory. Testing sessions were completed 6-monthly with the option of additional home based testing. Participants who completed at least three testing sessions over a minimum of 180 days were included in the analysis. Longitudinal reaction times were modelled using Latent Class Mixed Models to group individuals sharing similar latent characteristics. Models were tested for consistency using a cross-validation approach. Inter-class differences in the probability of reaction time worsening and the probability of 6-month confirmed disability progression were assessed using survival analysis.

Results A total of 460 relapsing remitting multiple sclerosis patients were included. For each task of the MSReactor computerised cognitive battery, the optimal model comprised of 3 latent classes. All 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 between 64% and 89% accuracy.

Conclusion Latent class modelling of longitudinal cognitive data collected by the MSReactor battery identified a group of patients with worsening reaction times and increased risk of disability progression.

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