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