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SMILE: a predictive model for Scoring the severity of relapses in MultIple scLErosis

dc.rights.licenseopenen_US
dc.contributor.authorLEJEUNE, F.
dc.contributor.authorCHATTON, A.
dc.contributor.authorLAPLAUD, D.-A.
dc.contributor.authorLE PAGE, E.
dc.contributor.authorWIERTLEWSKI, S.
dc.contributor.authorEDAN, G.
dc.contributor.authorKERBRAT, A.
dc.contributor.authorVEILLARD, D.
dc.contributor.authorHAMONIC, S.
dc.contributor.authorJOUSSET, N.
dc.contributor.authorLE FRERE, F.
hal.structure.identifierNeurocentre Magendie : Physiopathologie de la Plasticité Neuronale [U1215 Inserm - UB]
dc.contributor.authorOUALLET, Jean Christophe
hal.structure.identifierNeurocentre Magendie : Physiopathologie de la Plasticité Neuronale [U1215 Inserm - UB]
dc.contributor.authorBROCHET, Bruno
hal.structure.identifierNeurocentre Magendie : Physiopathologie de la Plasticité Neuronale [U1215 Inserm - UB]
dc.contributor.authorRUET, Aurelie
dc.contributor.authorFOUCHER, Y.
dc.contributor.authorMICHEL, L.
dc.date.accessioned2022-03-25T13:43:40Z
dc.date.available2022-03-25T13:43:40Z
dc.date.issued2021-02
dc.identifier.issn1434-1459 (online)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/136540
dc.description.abstractEnBackground : In relapsing–remitting multiple sclerosis (RRMS), relapse severity and residual disability are difficult to predict. Nevertheless, this information is crucial both for guiding relapse treatment strategies and for informing patients. Objective : We, therefore, developed and validated a clinical-based model for predicting the risk of residual disability at 6 months post-relapse in MS. Methods : We used the data of 186 patients with RRMS collected during the COPOUSEP multicentre trial. The outcome was an increase of ≥ 1 EDSS point 6 months post-relapse treatment. We used logistic regression with LASSO penalization to construct the model, and bootstrap cross-validation to internally validate it. The model was externally validated with an independent retrospective French single-centre cohort of 175 patients. Results : The predictive factors contained in the model were age > 40 years, shorter disease duration, EDSS increase ≥ 1.5 points at time of relapse, EDSS = 0 before relapse, proprioceptive ataxia, and absence of subjective sensory disorders. Discriminative accuracy was acceptable in both the internal (AUC 0.82, 95% CI [0.73, 0.91]) and external (AUC 0.71, 95% CI [0.62, 0.80]) validations. Conclusion : The predictive model we developed should prove useful for adapting therapeutic strategy of relapse and follow-up to individual patients.
dc.language.isoENen_US
dc.subject.enEDSS
dc.subject.enMultiple sclerosis
dc.subject.enPredictive model
dc.subject.enRelapse phenotype
dc.subject.enRelapse recovery
dc.title.enSMILE: a predictive model for Scoring the severity of relapses in MultIple scLErosis
dc.title.alternativeJ Neurol .en_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1007/s00415-020-10154-5en_US
dc.subject.halSciences du Vivant [q-bio]/Neurosciences [q-bio.NC]en_US
dc.identifier.pubmed32902734en_US
bordeaux.journalJournal of Neurologyen_US
bordeaux.page669-679en_US
bordeaux.volume268en_US
bordeaux.hal.laboratoriesNeurocentre Magendie - UMR-S 1215en_US
bordeaux.issue2en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.exportfalse
dc.rights.ccPas de Licence CCen_US
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