Laboratory of Clinical Neurophysiology, Scientific Institute (IRCCS) S. Maria Nascente, Fondazione don C. Gnocchi, Via Capecelatro 66, 20148 Milan, Italy.
Neurol Sci. 2012 Aug;33(4):887-92. doi: 10.1007/s10072-011-0862-3. Epub 2011 Nov 27.
To devise a multivariate parametric model for short-term prediction of disability using the Expanded Disability Status Scale (EDSS) and multimodal sensory EP (mEP). A total of 221 multiple sclerosis (MS) patients who underwent repeated mEP and EDSS assessments at variable time intervals over a 20-year period were retrospectively analyzed. Published criteria were used to compute a cumulative score (mEPS) of abnormalities for each of 908 individual tests. Data of a statistically balanced sample of 58 patients were fed to a parametrical regression analysis using time-lagged EDSS and mEPS along with other clinical variables to estimate future EDSS scores at 1 year. Whole sample cross-sectional mEPS were moderately correlated with EDSS, whereas longitudinal mEPS were not. Using the regression model, lagged mEPS and lagged EDSS along with clinical variables provided better future EDSS estimates. The R (2) measure of fit was significant and 72% of EDSS estimates showed an error value of ±0.5. A parametrical regression model combining EDSS and mEPS accurately predicts short-term disability in MS patients and could be used to optimize decisions concerning treatment.
制定一个使用扩展残疾状况量表（EDSS）和多模态感觉诱发电位（mEP）进行短期残疾预测的多变量参数模型。回顾性分析了 221 例多发性硬化症（MS）患者，这些患者在 20 年内的不同时间间隔内接受了重复的 mEP 和 EDSS 评估。使用公布的标准计算了 908 项单独测试中每项的异常累积评分（mEPS）。对 58 例具有统计学平衡样本的数据进行参数回归分析，使用时滞 EDSS 和 mEPS 以及其他临床变量来估计 1 年后的 EDSS 评分。整个样本的横断面 mEPS 与 EDSS 中度相关，而纵向 mEPS 则没有相关性。使用回归模型，时滞 mEPS 和时滞 EDSS 以及临床变量为未来 EDSS 提供了更好的估计。拟合的 R（2）度量值是显著的，72%的 EDSS 估计值误差值为±0.5。结合 EDSS 和 mEPS 的参数回归模型可准确预测 MS 患者的短期残疾，并可用于优化治疗决策。
