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

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.