Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Enzinger, C; Fuchs, S; Pichler, A; Wallner-Blazek, M; Khalil, M; Langkammer, C; Ropele, S; Fazekas, F.
Predicting the severity of relapsing-remitting MS: The contribution of cross-sectional and short-term follow-up MRI data.
Mult Scler. 2011; 17(6): 695-701.
Doi: 10.1177/1352458510394454
Web of Science
PubMed
FullText
FullText_MUG
- Führende Autor*innen der Med Uni Graz
- Enzinger Christian
- Fazekas Franz
- Co-Autor*innen der Med Uni Graz
- Fuchs Siegrid
- Khalil Michael
- Langkammer Christian
- Pichler Alexander
- Ropele Stefan
- Wallner-Blazek Mirja
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- 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. Conclusions: We failed to confirm a clear independent contribution of cross-sectional and short-term follow-up MRI data for the prediction of the long-term clinical course of MS. The MSSS is not a stable indicator of disease severity but may increase in converters to SPMS.
- Find related publications in this database (using NLM MeSH Indexing)
- Adult -
- Adult -
- Brain - pathology
- Chi-Square Distribution -
- Disease Progression -
- Female -
- Follow-Up Studies -
- Humans -
- Linear Models -
- Magnetic Resonance Imaging -
- Male -
- Middle Aged -
- Multiple Sclerosis, Chronic Progressive - diagnosis
- Multiple Sclerosis, Relapsing-Remitting - diagnosis
- Predictive Value of Tests -
- Prognosis -
- Prospective Studies -
- Severity of Illness Index -
- Time Factors -
- Young Adult -
- Find related publications in this database (Keywords)
- Expanded Disability Status Scale (EDSS) score
- magnetic resonance imaging (MRI)
- Multiple Sclerosis Severity Score (MSSS)
- relapsing-remitting multiple sclerosis (RRMS)
- secondary progressive multiple sclerosis (SPMS)