Early prediction of unfavorable evolution after a first clinical episode suggestive of multiple sclerosis: the EUMUS score.
First attack of MS
First episode of MS
Multiple sclerosis (MS)
No evidence of disease activity (NEDA)
Prognostic factors
Journal
Journal of neurology
ISSN: 1432-1459
Titre abrégé: J Neurol
Pays: Germany
ID NLM: 0423161
Informations de publication
Date de publication:
26 Mar 2024
26 Mar 2024
Historique:
received:
30
11
2023
accepted:
05
03
2024
revised:
04
03
2024
medline:
27
3
2024
pubmed:
27
3
2024
entrez:
27
3
2024
Statut:
aheadofprint
Résumé
Predicting disease progression in patients with the first clinical episode suggestive of multiple sclerosis (MS) is crucial for personalized therapeutic approaches. This study aimed to develop the EUMUS score for accurately estimating the risk of early evidence of disease activity and progression (EDA). Retrospective analysis was conducted on data from 221 patients with a first clinical MS episode collected from four Italian MS centers. Various variables including socio-demographics, clinical features, cerebrospinal fluid analysis, evoked potentials, and brain MRI were considered. A prognostic multivariate regression model was identified to develop the EUMUS score. The optimal cutoff for predicting the transition from no evidence of disease activity (NEDA3) to EDA was determined. The accuracy of the prognostic model and score were tested in a separate UK MS cohort. After 12 months, 61.54% of patients experienced relapses and/or new MRI lesions. Younger age (OR 0.96, CI 0.93-0.99; p = 0.005), MRI infratentorial lesion(s) at baseline (OR 2.21, CI 1.27-3.87; p = 0.005), positive oligoclonal bands (OR 2.89, CI 1.47-5.69; p = 0.002), and abnormal lower limb somatosensory-evoked potentials (OR 2.77, CI 1.41-5.42; p = 0.003) were significantly associated with increased risk of EDA. The EUMUS score demonstrated good specificity (72%) and correctly classified 80% of patients with EDA in the independent UK cohort. The EUMUS score is a simple and useful tool for predicting MS evolution within 12 months of the first clinical episode. It has the potential to guide personalized therapeutic approaches and aid in clinical decision-making.
Sections du résumé
BACKGROUND
BACKGROUND
Predicting disease progression in patients with the first clinical episode suggestive of multiple sclerosis (MS) is crucial for personalized therapeutic approaches. This study aimed to develop the EUMUS score for accurately estimating the risk of early evidence of disease activity and progression (EDA).
METHODS
METHODS
Retrospective analysis was conducted on data from 221 patients with a first clinical MS episode collected from four Italian MS centers. Various variables including socio-demographics, clinical features, cerebrospinal fluid analysis, evoked potentials, and brain MRI were considered. A prognostic multivariate regression model was identified to develop the EUMUS score. The optimal cutoff for predicting the transition from no evidence of disease activity (NEDA3) to EDA was determined. The accuracy of the prognostic model and score were tested in a separate UK MS cohort.
RESULTS
RESULTS
After 12 months, 61.54% of patients experienced relapses and/or new MRI lesions. Younger age (OR 0.96, CI 0.93-0.99; p = 0.005), MRI infratentorial lesion(s) at baseline (OR 2.21, CI 1.27-3.87; p = 0.005), positive oligoclonal bands (OR 2.89, CI 1.47-5.69; p = 0.002), and abnormal lower limb somatosensory-evoked potentials (OR 2.77, CI 1.41-5.42; p = 0.003) were significantly associated with increased risk of EDA. The EUMUS score demonstrated good specificity (72%) and correctly classified 80% of patients with EDA in the independent UK cohort.
CONCLUSIONS
CONCLUSIONS
The EUMUS score is a simple and useful tool for predicting MS evolution within 12 months of the first clinical episode. It has the potential to guide personalized therapeutic approaches and aid in clinical decision-making.
Identifiants
pubmed: 38532143
doi: 10.1007/s00415-024-12304-5
pii: 10.1007/s00415-024-12304-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.
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