Purpose

To identify the clinical and paraclinical markers of employment status in multiple sclerosis (MS).

Methods

This was a cross-sectional sub-study investigating 1226 MS patients. To minimalized confounding effect, two groups of patients, matched by sex, age, and education, were selected: 307 patients with full time employment and 153 unemployed patients receiving disability pension. We explored associations between employment status and Expanded Disability Status Scale (EDSS), 25 Foot Walk Test (25FWT), Nine Hole Peg Test (9HPT), Brief International Cognitive Assessment for MS (BICAMS), Paced Auditory Serial Addition Test (PASAT), Beck Depression Inventory (BDI), SLOAN charts (SLOAN), and brain volumetric MRI measures.

Results

Both groups differed significantly on all variables of interest (p < 0.001). In the univariate analyses, EDSS, SDMT (Symbol Digit Modalities Test) adjusted for BDI, 25FWT, and 9HPT best explained variability in vocational status. In multivariate analyses, the combination of EDSS, 25FWT, SDMT, BDI, and corpus callosum fraction (CCF) explained the greatest variability. As a next step, after patients were matched by EDSS, differences in SDMT, 25FWT (both p < 0.001), 9HPT, CCF, and T2 lesion volume were still present (all p < 0.005) between both groups. The best multivariate model consisted of SDMT, BDI, and T2 lesion volume.

Conclusions

EDSS, walking ability, cognitive performance, and MRI volumetric parameters are independently associated with employment status.

中文翻译：

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多发性硬化症就业状况的临床和副临床相关性

目的

确定多发性硬化症 (MS) 就业状况的临床和准临床标志物。

方法

这是一项横断面子研究，调查了 1226 名 MS 患者。为了最小化混杂效应，选择了性别、年龄和教育相匹配的两组患者：307 名全职工作患者和 153 名领取残疾抚恤金的失业患者。我们探讨了就业状况与扩展残疾状况量表 (EDSS)、25 脚步行测试 (25FWT)、九孔钉测试 (9HPT)、MS 简要国际认知评估 (BICAMS)、有节奏的听觉连续添加测试 (PASAT) 之间的关联，贝克抑郁量表 (BDI)、SLOAN 图表 (SLOAN) 和脑容量 MRI 测量。

结果

两组在所有感兴趣的变量上都有显着差异（p  < 0.001）。在单变量分析中，针对 BDI、25FWT 和 9HPT 调整的 EDSS、SDMT（符号数字模态测试）最能解释职业状态的变异性。在多变量分析中，EDSS、25FWT、SDMT、BDI 和胼胝体分数 (CCF) 的组合解释了最大的变异性。作为下一步，在患者通过 EDSS 匹配后，两组 之间 仍然存在SDMT、25FWT（均p < 0.001）、9HPT、CCF 和 T2 病灶体积的差异（均p < 0.005）。最好的多变量模型由 SDMT、BDI 和 T2 病灶体积组成。

结论

EDSS、步行能力、认知能力和 MRI 体积参数与就业状况独立相关。