(2018) Long-term disability trajectories in primary progressive MS patients: A latent class growth analysis. Multiple sclerosis (Houndmills, Basingstoke, England). pp. 642-652. ISSN 1477-0970 (Electronic) 1352-4585 (Linking)
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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.
Item Type: | Article |
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Keywords: | Primary progressive multiple sclerosis clinical trials disability heterogeneity long-term trajectories |
Subjects: | WL Nervous System |
Divisions: | Faculty of Medicine > Departments of Clinical Sciences > Department of Neurosurgery |
Page Range: | pp. 642-652 |
Journal or Publication Title: | Multiple sclerosis (Houndmills, Basingstoke, England) |
Journal Index: | Pubmed, ISI |
Volume: | 24 |
Number: | 5 |
Identification Number: | https://doi.org/10.1177/1352458517703800 |
ISSN: | 1477-0970 (Electronic) 1352-4585 (Linking) |
Depositing User: | مهندس مهدی شریفی |
URI: | http://eprints.mui.ac.ir/id/eprint/1620 |
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