Michigan Alzheimer's Disease Research Center, Ann Arbor, MI, USA.
Department of Veterans Affairs Medical Center, Geriatric Research Education and Clinical Center, Ann Arbor, MI, USA.
J Int Neuropsychol Soc. 2024 Aug;30(7):689-696. doi: 10.1017/S1355617724000213. Epub 2024 Sep 18.
Identify which NIH Toolbox Cognition Battery (NIHTB-CB) subtest(s) best differentiate healthy controls (HC) from those with amnestic mild cognitive impairment (aMCI) and compare the discriminant accuracy between a model using a priori "Norm Adjusted" scores versus "Unadjusted" standard scores with age, sex, race/ethnicity, and education controlled for within the model. Racial differences were also examined.
Participants were Black/African American (B/AA) and White consensus-confirmed (HC = 96; aMCI = 62) adults 60-85 years old that completed the NIHTB-CB for tablet. Discriminant function analysis (DFA) was used in the Total Sample and separately for B/AA ( = 80) and White participants ( = 78).
Picture Sequence Memory (an episodic memory task) was the highest loading coefficient across all DFA models. When stratified by race, differences were noted in the pattern of the highest loading coefficients within the DFAs. However, the overall discriminant accuracy of the DFA models in identifying HCs and those with aMCI did not differ significantly by race (B/AA, White) or model/score type (Norm Adjusted versus Unadjusted).
Racial differences were noted despite the use of normalized scores or demographic covariates-highlighting the importance of including underrepresented groups in research. While the models were fairly accurate at identifying consensus-confirmed HCs, the models proved less accurate at identifying White participants with an aMCI diagnosis. In clinical settings, further work is needed to optimize computerized batteries and the use of NIHTB-CB norm adjusted scores is recommended. In research settings, demographically corrected scores or within model correction is suggested.
确定 NIH 工具包认知电池（NIHTB-CB）中的哪些子测验最能区分健康对照组（HC）和遗忘型轻度认知障碍（aMCI）患者，并比较使用先验“规范调整”分数与“未调整”标准分数的模型之间的判别准确性，同时控制模型内的年龄、性别、种族/民族和教育因素。还检查了种族差异。
参与者为黑人/非裔美国人（B/AA）和白人共识确认的（HC=96；aMCI=62）60-85 岁成年人，他们完成了 NIHTB-CB 的平板电脑测试。判别函数分析（DFA）用于总样本，以及 B/AA（n=80）和白人参与者（n=78）的样本。
图片序列记忆（一种情景记忆任务）是所有 DFA 模型中最高的加载系数。按种族分层时，DFA 中最高加载系数的模式存在差异。然而，DFA 模型在识别 HC 和 aMCI 患者方面的整体判别准确性在种族（B/AA、白人）或模型/分数类型（规范调整与未调整）方面没有显著差异。
尽管使用了标准化分数或人口统计学协变量，但仍注意到了种族差异，这突出了在研究中纳入代表性不足的群体的重要性。虽然这些模型在识别共识确认的 HC 方面相当准确，但在识别患有 aMCI 诊断的白人参与者方面，这些模型的准确性较低。在临床环境中，需要进一步努力优化计算机化电池，并建议使用 NIHTB-CB 规范调整分数。在研究环境中，建议使用人口统计学校正分数或模型内校正。
