A composite score to predict short-term disease activity in patients with relapsing-remitting MS
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作者:
Sormani, Maria Pia
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Ist Sci San Raffaele, Dept Neurol, Neuroimaging Res Unit, I-20132 Milan, Italy
Univ Genoa, Biostat Unit, DISSAL, I-16126 Genoa, ItalyIst Sci San Raffaele, Dept Neurol, Neuroimaging Res Unit, I-20132 Milan, Italy
Sormani, Maria Pia
[1
,2
]
Rovaris, Marco
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Ist Sci San Raffaele, Dept Neurol, Neuroimaging Res Unit, I-20132 Milan, ItalyIst Sci San Raffaele, Dept Neurol, Neuroimaging Res Unit, I-20132 Milan, Italy
Rovaris, Marco
[1
]
Comi, Giancarlo
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Ist Sci San Raffaele, Dept Neurol, Neuroimaging Res Unit, I-20132 Milan, ItalyIst Sci San Raffaele, Dept Neurol, Neuroimaging Res Unit, I-20132 Milan, Italy
Comi, Giancarlo
[1
]
Filippi, Massimo
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Ist Sci San Raffaele, Dept Neurol, Neuroimaging Res Unit, I-20132 Milan, ItalyIst Sci San Raffaele, Dept Neurol, Neuroimaging Res Unit, I-20132 Milan, Italy
Filippi, Massimo
[1
]
机构:
[1] Ist Sci San Raffaele, Dept Neurol, Neuroimaging Res Unit, I-20132 Milan, Italy
[2] Univ Genoa, Biostat Unit, DISSAL, I-16126 Genoa, Italy
Objective: To generate and validate a composite (clinical and MRI-based) score able to identify individual patients with relapsing-remitting multiple sclerosis (RRMS) with a high risk of experiencing relapses in the short term. Methods: The study was conducted using data from a working and a validation dataset. The former consisted of 539 patients from the placebo arm of a double-blind, placebo-controlled trial of oral glatiramer acetate (GA) in RRMS. The validation sample consisted of 117 patients from the placebo arm of a double-blind, placebo-controlled trial of subcutaneous GA in RRMS. In the working sample, regression analysis was performed to identify clinical or MRI variables independently predicting the occurrence of relapses. A linear predictive score was calculated using the variables included in the multivariable model and the corresponding estimated coefficients. Such a score was then applied to the validation sample. Results: The variables included in the final model as independent predictors of relapse occurrence were the number of enhancing lesions on a baseline MRI (p < 0.001) and the number of relapses during the previous 2 years (p < 0.001). The resulting score was able to identify patients at high and low risk of relapse occurrence both in the working and in the validation samples. Conclusions: The composite, clinical/MRI score presented here, which allows us to estimate the short-term risk of relapses in patients with relapsing-remitting multiple sclerosis, may provide us with an additional and useful piece of information for a better planning of phase III trials in multiple sclerosis.