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SMILE: a predictive model for Scoring the severity of relapses in MultIple scLErosis.
Journal of Neurology ( IF 6 ) Pub Date : 2020-09-09 , DOI: 10.1007/s00415-020-10154-5
F Lejeune 1, 2 , A Chatton 3 , D-A Laplaud 1, 2 , E Le Page 4 , S Wiertlewski 1, 2 , G Edan 4 , A Kerbrat 4 , D Veillard 5 , S Hamonic 5 , N Jousset 6 , F Le Frère 6 , J-C Ouallet 7 , B Brochet 7 , A Ruet 7 , Y Foucher 3, 8 , Laure Michel 4, 9, 10
Affiliation  

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.



中文翻译:

SMILE:用于评估多发性硬化症复发严重程度的预测模型。

背景

在复发缓解型多发性硬化症(RRMS)中,很难预测复发的严重程度和残障。然而,该信息对于指导复发治疗策略和告知患者都是至关重要的。

目的

因此,我们开发并验证了一种基于临床的模型,用于预测MS复发6个月后残留残障的风险。

方法

我们使用了在COPOUSEP多中心试验期间收集的186例RRMS患者的数据。结果是,复发治疗后6个月,EDSS增加了≥1。我们将逻辑回归与LASSO罚分法一起使用来构建模型,并使用bootstrap交叉验证法对其进行内部验证。该模型由175名患者的独立回顾性法国单中心队列进行了外部验证。

结果

该模型中包含的预测因素为年龄> 40岁,病程较短,复发时EDSS增加≥1.5点,复发前EDSS = 0,本体感受性共济失调和无主观感觉障碍。在内部(AUC 0.82,95%CI [0.73,0.91])和外部(AUC 0.71,95%CI [0.62,0.80])验证中,判别精度都是可以接受的。

结论

我们开发的预测模型应证明对适应个体患者的复发和随访治疗策略很有用。

更新日期:2020-09-10
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