LEJEUNE, F.; CHATTON, A.; LAPLAUD, D.-A.; LE PAGE, E.; WIERTLEWSKI, S.; EDAN, G.; KERBRAT, A.; VEILLARD, D.; HAMONIC, S.; JOUSSET, N.; LE FRERE, F.; OUALLET, Jean Christophe; BROCHET, Bruno; RUET, Aurelie; FOUCHER, Y.; MICHEL, L.

doi:10.1007/s00415-020-10154-5

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Journal of Neurology. 2021-02, vol. 268, n° 2, p. 669-679

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Background : 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.< Réduire

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EDSS

Multiple sclerosis

Predictive model

Relapse phenotype

Relapse recovery

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

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