Horton Daniel K, Hynan Linda S, Lacritz Laura H, Rossetti Heidi C, Weiner Myron F, Cullum C Munro
a Department of Psychiatry , The University of Texas Southwestern Medical Center , Dallas , TX , USA.
Clin Neuropsychol. 2015;29(4):413-25. doi: 10.1080/13854046.2015.1043349. Epub 2015 May 15.
The Montreal Cognitive Assessment (MoCA) is a cognitive screening instrument growing in popularity, but few studies have conducted psychometric item analyses or attempted to develop abbreviated forms. We sought to derive and validate a short-form MoCA (SF-MoCA) and compare its classification accuracy to the standard MoCA and Mini-Mental State Examination (MMSE) in mild cognitive impairment (MCI), Alzheimer disease (AD), and normal aging.
408 subjects (MCI n = 169, AD n = 87, and normal n = 152) were randomly divided into derivation and validation samples. Item analysis in the derivation sample identified most sensitive MoCA items. Receiver Operating Characteristic (ROC) analyses were used to develop cut-off scores and evaluate the classification accuracy of the SF-MoCA, standard MoCA, and MMSE. Net Reclassification Improvement (NRI) analyses and comparison of ROC curves were used to compare classification accuracy of the three measures.
Serial subtraction (Cramer's V = .408), delayed recall (Cramer's V = .702), and orientation items (Cramer's V = .832) were included in the SF-MoCA based on largest effect sizes in item analyses. Results revealed 72.6% classification accuracy of the SF-MoCA, compared with 71.9% for the standard MoCA and 67.4% for the MMSE. Results of NRI analyses and ROC curve comparisons revealed that classification accuracy of the SF-MoCA was comparable to the standard version and generally superior to the MMSE.
Findings suggest the SF-MoCA could be an effective brief tool in detecting cognitive impairment.
蒙特利尔认知评估量表（MoCA）是一种越来越受欢迎的认知筛查工具，但很少有研究进行过心理测量学项目分析或尝试开发简版。我们试图推导并验证一个简版MoCA（SF-MoCA），并比较其与标准MoCA及简易精神状态检查表（MMSE）在轻度认知障碍（MCI）、阿尔茨海默病（AD）和正常衰老中的分类准确性。
408名受试者（MCI患者169名、AD患者87名、正常人152名）被随机分为推导样本和验证样本。推导样本中的项目分析确定了最敏感的MoCA项目。受试者工作特征（ROC）分析用于确定临界分数，并评估SF-MoCA、标准MoCA和MMSE的分类准确性。净重新分类改善（NRI）分析和ROC曲线比较用于比较这三种测量方法的分类准确性。
基于项目分析中最大的效应量，SF-MoCA纳入了连续减法（克莱默V值=0.408）、延迟回忆（克莱默V值=0.702）和定向项目（克莱默V值=0.832）。结果显示，SF-MoCA的分类准确性为72.6%，标准MoCA为71.9%，MMSE为67.4%。NRI分析和ROC曲线比较的结果显示，SF-MoCA的分类准确性与标准版本相当，总体上优于MMSE。
研究结果表明，SF-MoCA可能是检测认知障碍的一种有效的简短工具。
