SMILE: a predictive model for Scoring the severity of relapses in MultIple scLErosis.
J Neurol
; 268(2): 669-679, 2021 Feb.
Article
in En
| MEDLINE
| ID: mdl-32902734
ABSTRACT
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Multiple Sclerosis, Relapsing-Remitting
/
Multiple Sclerosis
Type of study:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Adult
/
Humans
Language:
En
Journal:
J Neurol
Year:
2021
Document type:
Article
Affiliation country:
France