Predicting the severity of relapsing-remitting MS: the contribution of cross-sectional and short-term follow-up MRI data
Abstract
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<0.001). The BHR at baseline explained 9.4% of variance of conversion to SPMS (p=0.033). Over the observational period the MSSS remained stable in patients remaining RRMS, but increased in converters to SPMS from 4.0 to 6.4.…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 99 participants or records.
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