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Predictors of long-term clinical response to interferon beta therapy in relapsing multiple sclerosis

Tomassini, Valentina ORCID: https://orcid.org/0000-0002-7368-6280, Paolillo, A., Russo, P., Giugni, E., Prosperini, L., Gasperini, C., Antonelli, G., Bastianello, S. and Pozzilli, C. 2006. Predictors of long-term clinical response to interferon beta therapy in relapsing multiple sclerosis. Journal of Neurology 253 (3) , pp. 287-293. 10.1007/s00415-005-0979-5

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Abstract

Objective The aim of this study was to identify clinical, magnetic resonance imaging (MRI) and biological markers predictive of long-term clinical response to interferon beta (IFN beta) therapy in patients with relapsing-remitting multiple sclerosis (RRMS). Methods Sixty-eight patients treated with IFN beta were followed over a 6-year period. Relapse rate and disability progression were evaluated throughout the study. We considered suboptimal clinical response to be either the presence of sustained disability progression, or more than two relapses. Baseline and 12-month demographic, clinical and MRI findings, as well as the development of neutralizing antibodies (NAbs) against IFN beta during the first year of therapy were analyzed as predictors of long-term clinical outcome. Results '' Black holes '' on MRI were the best baseline predictor of disability progression (odds ratio [OR] 6.8; p < 0.001). At 1 year, both male gender (OR 4.9; p = 0.009) and NAbs (OR 7.3; p = 0.003) were independently associated with a high risk of developing subsequent disability. The presence of gadolinium enhancement, both at baseline (OR 4.7; p = 0.005) and on the 1-year MRI scan (OR 7.9; p = 0.002), was the unique variable associated with the number of relapses over the study period. Conclusions Variables assessable within the first year of treatment significantly influence relapse rate and disability progression in patients with RRMS treated with IFN beta. These findings may help clinicians to make decisions regarding therapy regimen over time, and highlight the need for a prognostic algorithm.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Subjects: R Medicine > R Medicine (General)
Publisher: Springer
ISSN: 0340-5354
Last Modified: 31 Oct 2022 10:02
URI: https://orca.cardiff.ac.uk/id/eprint/83378

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