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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Clinical parameters to predict response to interferon in relapsing multiple sclerosis.
A Fromont1, M Debouverie, G Le Teuff
1Department of Neurology, University Hospital of Dijon, Dijon, France.
Neuroepidemiology
|August 22, 2008
Summary
A higher relapse rate in the year before starting beta-interferon (IFN-beta) treatment can predict response in relapsing-remitting multiple sclerosis (RRMS). However, relapse rates from two years prior were not predictive.
Area of Science:
- Neurology
- Immunology
Background:
- Relapsing-remitting multiple sclerosis (RRMS) is a chronic autoimmune disease affecting the central nervous system.
- Beta-interferon (IFN-beta) is a common disease-modifying therapy for RRMS.
Purpose of the Study:
- To identify clinical predictors of response to beta-interferon (IFN-beta) therapy in a cohort of patients with relapsing-remitting multiple sclerosis (RRMS).
Main Methods:
- Retrospective analysis of data from 2,645 RRMS patients treated with IFN-beta for at least 6 months.
- Inclusion criteria: definite RRMS, treatment with IFN-beta.
- Four response criteria evaluated: reduced relapse rate (1-year and 2-year pre-treatment), relapse-free status (2 years), and change in Disability Status Scale (1-year).
- Multivariate logistic regression used for analysis.
Main Results:
- 751 RRMS patients were included in the final analysis.
- A higher relapse rate in the year preceding IFN-beta initiation was significantly associated with treatment response.
- Older age at MS onset and polysymptomatic onset were also associated with response.
- No significant predictors were identified using the other three response criteria.
Conclusions:
- The relapse rate in the year immediately preceding IFN-beta treatment is a significant predictor of treatment response in RRMS.
- Relapse rates from two years prior to treatment initiation do not reliably predict response.
- Further research may refine predictive models for IFN-beta therapy in RRMS.
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