Leach Justin M, Cutter Gary, Golan Daniel, Doniger Glen, Zarif Myassar, Bumstead Barbara, Buhse Marijean, Kaczmarek Olivia, Sethi Avtej, Covey Thomas, Penner Iris-Katharina, Wilken Jeffrey, Gudesblatt Mark
Department of Biostatistics, University of Alabama at Birmingham, 1665 University Blvd., 1665 University Blvd. Suite #327, Birmingham, Alabama 35294, United States.
Department of Biostatistics, University of Alabama at Birmingham, 1665 University Blvd., 1665 University Blvd. Suite #327, Birmingham, Alabama 35294, United States.
Mult Scler Relat Disord. 2022 Apr;60:103704. doi: 10.1016/j.msard.2022.103704. Epub 2022 Feb 20.
The Symbol Digit Modalities Test (SDMT) is a common screen of cognitive function for people with Multiple Sclerosis (pwMS) but growing acknowledgement that people with cognitive impairment are a heterogeneous population suggests that a single screen may provide limited information.
To assess the adequacy of the SDMT in capturing impairment across specific cognitive domains as measured by a multi-domain cognitive assessment battery (CAB, NeuroTrax).
113 pwMS were assessed with SDMT and the CAB. Cognitive impairment in each CAB domain was defined as ≥1.5 SD below the normalized mean. Logistic regression models were fit for each CAB domain with domain-specific cognitive impairment as the outcome and SDMT as the predictor, and a classifier created by selecting cutpoints using the Youden Index. Model performance was assessed by predicting domain-specific cognitive impairment in an independent data set consisting of 81 pwMS.
SDMT was a significant predictor of cognitive impairment in all outcomes considered (Odds Ratio: 0.885-0.950), but prediction metrics such as area under the receiver operating curve (AUC) were modest (0.623-0.778), and the alignment between observed/predicted impairment was less than optimal.
The SDMT is not sufficient to differentiate between impaired and non-impaired pwMS across several cognitive domains.
符号数字模态测验(SDMT)是对多发性硬化症患者(pwMS)认知功能的一种常见筛查方法,但越来越多的人认识到认知障碍患者是一个异质性群体,这表明单一筛查可能提供的信息有限。
通过多领域认知评估量表(CAB,NeuroTrax)测量,评估SDMT在捕捉特定认知领域损伤方面的充分性。
对113名pwMS患者进行了SDMT和CAB评估。每个CAB领域的认知障碍定义为低于标准化均值≥1.5个标准差。以特定领域的认知障碍为结果、SDMT为预测指标,为每个CAB领域拟合逻辑回归模型,并使用约登指数选择切点创建一个分类器。通过预测由81名pwMS患者组成的独立数据集中特定领域的认知障碍来评估模型性能。
在所有考虑的结果中,SDMT都是认知障碍的显著预测指标(比值比:0.885 - 0.950),但诸如受试者工作特征曲线下面积(AUC)等预测指标一般(0.623 - 0.778),且观察到的/预测的损伤之间的一致性并非最佳。
SDMT不足以区分多个认知领域中受损和未受损的pwMS。