Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share By
Long-Term Disability Trajectories in Primary Progressive Ms Patients: A Latent Class Growth Analysis Publisher



Signori A1 ; Izquierdo G2 ; Lugaresi A3, 4 ; Hupperts R5 ; Grandmaison F6 ; Sola P7 ; Horakova D8 ; Havrdova E8 ; Prat A9 ; Girard M9 ; Duquette P9 ; Boz C10 ; Grammond P11 ; Terzi M12 Show All Authors
Authors
  1. Signori A1
  2. Izquierdo G2
  3. Lugaresi A3, 4
  4. Hupperts R5
  5. Grandmaison F6
  6. Sola P7
  7. Horakova D8
  8. Havrdova E8
  9. Prat A9
  10. Girard M9
  11. Duquette P9
  12. Boz C10
  13. Grammond P11
  14. Terzi M12
  15. Singhal B13
  16. Alroughani R14
  17. Petersen T15
  18. Ramo C16
  19. Orejaguevara C17
  20. Spitaleri D18
  21. Shaygannejad V19
  22. Butzkueven H20, 21
  23. Kalincik T21
  24. Jokubaitis V21
  25. Slee M22
  26. Fernandez Bolanos R23
  27. Sanchezmenoyo JL24
  28. Pucci E25
  29. Granella F26
  30. Lechnerscott J27
  31. Iuliano G28
  32. Hughes S29
  33. Bergamaschi R30
  34. Taylor B31
  35. Verheul F32
  36. Edite Rio M33
  37. Amato MP34
  38. Sajedi SA35
  39. Majdinasab N36
  40. Van Pesch V37
  41. Sormani MP1
  42. Trojano M38

Source: Multiple Sclerosis Journal Published:2018


Abstract

Background: Several natural history studies on primary progressive multiple sclerosis (PPMS) patients detected a consistent heterogeneity in the rate of disability accumulation. Objectives: To identify subgroups of PPMS patients with similar longitudinal trajectories of Expanded Disability Status Scale (EDSS) over time. Methods: All PPMS patients collected within the MSBase registry, who had their first EDSS assessment within 5 years from onset, were included in the analysis. Longitudinal EDSS scores were modeled by a latent class mixed model (LCMM), using a nonlinear function of time from onset. LCMM is an advanced statistical approach that models heterogeneity between patients by classifying them into unobserved groups showing similar characteristics. Results: A total of 853 PPMS (51.7% females) from 24 countries with a mean age at onset of 42.4 years (standard deviation (SD): 10.8 years), a median baseline EDSS of 4 (interquartile range (IQR): 2.5–5.5), and 2.4 years of disease duration (SD: 1.5 years) were included. LCMM detected three different subgroups of patients with a mild (n = 143; 16.8%), moderate (n = 378; 44.3%), or severe (n = 332; 38.9%) disability trajectory. The probability of reaching EDSS 6 at 10 years was 0%, 46.4%, and 81.9% respectively. Conclusion: Applying an LCMM modeling approach to long-term EDSS data, it is possible to identify groups of PPMS patients with different prognosis. © 2017, © The Author(s), 2017.
Other Related Docs
16. Early Predictors of Conversion to Secondary Progressive Multiple Sclerosis, Multiple Sclerosis and Related Disorders (2021)
18. Lateonset Multiple Sclerosis in Isfahan, Iran, Archives of Iranian Medicine (2012)