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量化认知和疲劳,以提高复发期间 EDSS 的敏感性。

Quantifying cognition and fatigue to enhance the sensitivity of the EDSS during relapses.

机构信息

Department of Clinical Neurological Sciences, Western University, London, ON, Canada/London Health Sciences Center, London, ON, Canada.

Mellen Center for Multiple Sclerosis Treatment, Neurological Institute, Cleveland Clinic, Cleveland, OH, USAV.

出版信息

Mult Scler. 2021 Jun;27(7):1077-1087. doi: 10.1177/1352458520973618. Epub 2020 Dec 1.

Abstract

BACKGROUND

Cognition is affected by relapses in persons with multiple sclerosis (PwMS), yet the Expanded Disability Status Scale (EDSS) does not readily detect cognitive changes.

OBJECTIVE

The objective of this study is to improve the detection of cognitive decline during relapses, by incorporating the Symbol Digit Modalities Test (SDMT) into the cerebral Functional System Score (CFSS) of the EDSS.

METHODS

This prospective study recruited PwMS from three dedicated MS centers. All subjects had EDSS, SDMT, and Fatigue Severity Scale (FSS) administered. Subjects experiencing a relapse were assigned to the relapse group (RG). Matched controls from the larger cohort were assigned to the stable group (SG). RG and SG subjects underwent the same evaluation at relapse and 3 months later. Our main outcomes were a modified CFSS (m-CFSS) and modified EDSS (m-EDSS), incorporating SDMT and FSS, accounting for cognitive performance and fatigue rating, during relapse.

RESULTS

The full cohort included 592 subjects; 80 qualified for RG and 72 were matched to the SG. The m-CFSS was significantly higher than CFSS at baseline (median = 2 vs. median = 0,  < 0.001) and relapse (median = 2 vs. median = 1,  < 0.001). The m-EDSS was higher than EDSS (median 3.0 vs. 2.5,  = 0.02) at relapse, where 35 RG subjects (43.8%) had higher m-EDSS than EDSS at relapse.

CONCLUSION

This study demonstrates that incorporating the SDMT and FSS improves the accuracy of the EDSS, by accounting for cognitive changes, during relapse activity.

摘要

背景

认知功能受多发性硬化症(MS)患者复发的影响,但扩展残疾状态量表(EDSS)并不能轻易检测到认知变化。

目的

本研究旨在通过将符号数字模态测试(SDMT)纳入 EDSS 的大脑功能系统评分(CFSS),提高复发期间认知能力下降的检测能力。

方法

这项前瞻性研究招募了来自三个专门的 MS 中心的 MS 患者。所有受试者均接受 EDSS、SDMT 和疲劳严重程度量表(FSS)评估。经历复发的受试者被分配到复发组(RG)。从更大的队列中匹配的对照组被分配到稳定组(SG)。RG 和 SG 受试者在复发时和 3 个月后接受相同的评估。我们的主要结局是改良 CFSS(m-CFSS)和改良 EDSS(m-EDSS),纳入 SDMT 和 FSS,在复发期间评估认知表现和疲劳评分。

结果

全队列包括 592 名受试者;80 名符合 RG 标准,72 名与 SG 匹配。m-CFSS 在基线时(中位数=2 比中位数=0, < 0.001)和复发时(中位数=2 比中位数=1, < 0.001)均显著高于 CFSS。m-EDSS 在复发时高于 EDSS(中位数 3.0 比 2.5, = 0.02),其中 35 名 RG 受试者(43.8%)在复发时的 m-EDSS 高于 EDSS。

结论

这项研究表明,纳入 SDMT 和 FSS 通过在复发活动期间评估认知变化,提高了 EDSS 的准确性。

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