Predicting the severity of relapsing-remitting MS: The contribution of cross-sectional and short-term follow-up MRI data

• Published in: Multiple sclerosis 2011-06, Vol.17 (6), p.695-701
• Main Authors: Enzinger, C, Fuchs, S, Pichler, A, Wallner-Blazek, M, Khalil, M, Langkammer, C, Ropele, S, Fazekas, F
• Format: Article
• Language: English
• Subjects: Adult; Austria; Biological and medical sciences; Brain - pathology; Chi-Square Distribution; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Disease Progression; Female; Follow-Up Studies; Humans; Linear Models; Magnetic Resonance Imaging; Male; Medical sciences; Middle Aged; Multiple sclerosis and variants. Guillain barré syndrome and other inflammatory polyneuropathies. Leukoencephalitis; Multiple Sclerosis, Chronic Progressive - diagnosis; Multiple Sclerosis, Chronic Progressive - pathology; Multiple Sclerosis, Relapsing-Remitting - diagnosis; Multiple Sclerosis, Relapsing-Remitting - pathology; Neurology; Predictive Value of Tests; Prognosis; Prospective Studies; Severity of Illness Index; Time Factors; Young Adult
• ISSN: 1352-4585; 1477-0970
• DOI: 10.1177/1352458510394454

Background and objective: Predicting the long-term clinical course of multiple sclerosis (MS) is difficult on clinical grounds. Recent studies have suggested magnetic resonance imaging (MRI) metrics to be helpful. We wanted to confirm this. Methods: Contactable individuals (N = 84) from an initial 99 patients with relapsing–remitting MS (RRMS) who had undergone careful baseline and 2-year follow-up examinations including MRI were reassessed after a mean of 10.8 ± 2.7 years. We investigated using multivariate linear regression analyses if clinical and MRI data obtained at the prior time-points and the rates of change in morphologic variables over a mean observational period of 2.5 years could have served to predict a patient’s MS severity score (MSSS) 11 years later. Conversion to secondary progressive MS (SPMS) was a further outcome variable. Results: In univariate analyses, the ‘black hole ratio’ (BHR) at baseline (p = 0.017, beta = 0.148) and at first follow-up (p = 0.007, beta = −0.154) was the only MRI parameter showing a significant correlation with the MSSS. In a multiple regression model, the independent predictive value of imaging variables became statistically non-significant and the latest MSSS was predicted primarily by the baseline EDSS (r 2 = 0.28; p