Gavett Brandon E, Poon Sabrina J, Ozonoff Al, Jefferson Angela L, Nair Anil K, Green Robert C, Stern Robert A
Department of Neurology, Boston University School of Medicine, Boston, Massachusetts 02118-2526, USA.
J Int Neuropsychol Soc. 2009 Jan;15(1):121-9. doi: 10.1017/S1355617708090176.
Measures of episodic memory are often used to identify Alzheimer's disease (AD) and mild cognitive impairment (MCI). The Neuropsychological Assessment Battery (NAB) List Learning test is a promising tool for the memory assessment of older adults due to its simplicity of administration, good psychometric properties, equivalent forms, and extensive normative data. This study examined the diagnostic utility of the NAB List Learning test for differentiating cognitively healthy, MCI, and AD groups. One hundred fifty-three participants (age: range, 57-94 years; M = 74 years; SD, 8 years; sex: 61% women) were diagnosed by a multidisciplinary consensus team as cognitively normal, amnestic MCI (aMCI; single and multiple domain), or AD, independent of NAB List Learning performance. In univariate analyses, receiver operating characteristics curve analyses were conducted for four demographically-corrected NAB List Learning variables. Additionally, multivariate ordinal logistic regression and fivefold cross-validation was used to create and validate a predictive model based on demographic variables and NAB List Learning test raw scores. At optimal cutoff scores, univariate sensitivity values ranged from .58 to .92 and univariate specificity values ranged from .52 to .97. Multivariate ordinal regression produced a model that classified individuals with 80% accuracy and good predictive power. (JINS, 2009, 15, 121-129.).
情景记忆测量方法常用于识别阿尔茨海默病(AD)和轻度认知障碍(MCI)。神经心理评估量表(NAB)的列表学习测试因其施测简单、良好的心理测量特性、等效形式以及广泛的常模数据,是评估老年人记忆的一种有前景的工具。本研究考察了NAB列表学习测试在区分认知健康、MCI和AD组方面的诊断效用。153名参与者(年龄范围57 - 94岁;M = 74岁;标准差8岁;性别:61%为女性)由多学科共识团队诊断为认知正常、遗忘型MCI(aMCI;单领域和多领域)或AD,与NAB列表学习表现无关。在单变量分析中,对四个经人口统计学校正的NAB列表学习变量进行了受试者操作特征曲线分析。此外,使用多变量有序逻辑回归和五重交叉验证,基于人口统计学变量和NAB列表学习测试原始分数创建并验证了一个预测模型。在最佳截断分数下,单变量敏感性值范围为0.58至0.92,单变量特异性值范围为0.52至0.97。多变量有序回归产生了一个分类准确率为80%且具有良好预测能力的模型。(《神经心理学杂志》,2009年,第15卷,第121 - 129页)