使用美国国立卫生研究院工具箱认知平板电脑电池识别黑人和白人社区老年人的失忆性轻度认知障碍

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Taylor Rigby, Allyson M. Gregoire, Johnathan Reader, Yonatan Kahsay, Jordan Fisher, Anson Kairys, Arijit K. Bhaumik, Annalise Rahman-Filipiak, Amanda Cook Maher, Benjamin M. Hampstead, Judith L. Heidebrink, Voyko Kavcic, Bruno Giordani
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引用次数: 0

摘要

目标:确定美国国立卫生研究院工具箱认知测验(NIHTB-CB)的哪些子测验最能区分健康对照组(HC)和失忆性轻度认知障碍(AMCI)患者:确定哪些 NIH 工具箱认知测试(NIHTB-CB)子测试最能区分健康对照组(HC)和伴有失忆性轻度认知障碍(aMCI)的患者,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,同时在模型中控制年龄、性别、种族/民族和教育程度。此外,还研究了种族差异。研究方法受试者为黑人/非洲裔美国人(B/AA)和白人共识确认(HC = 96;aMCI = 62)的 60-85 岁成年人,他们完成了 NIHTB-CB 药片。在总样本中使用了判别函数分析 (DFA),并分别对黑人/非洲裔美国人(n = 80)和白人参与者(n = 78)进行了分析。结果在所有 DFA 模型中,图片序列记忆(一种外显记忆任务)的负荷系数最高。如果按种族进行分层,则会发现 DFA 中最高负载系数的模式存在差异。然而,DFA 模型在识别 HC 和 aMCI 患者方面的总体判别准确性并没有因种族(B/AA、白人)或模型/评分类型(规范调整与未调整)的不同而产生显著差异。结论:尽管使用了规范化评分或人口统计学协变量,但仍发现了种族差异--这凸显了将代表性不足的群体纳入研究的重要性。虽然模型在识别共识确认的高危人群方面相当准确,但在识别被诊断为 aMCI 的白人参与者方面,模型的准确性较低。在临床环境中,需要进一步优化计算机化电池,建议使用 NIHTB-CB 标准调整分数。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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of amnestic mild cognitive impairment among Black and White community-dwelling older adults using NIH Toolbox Cognition tablet battery
Objectives: 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. Methods: 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 (n = 80) and White participants (n = 78). Results: 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). Conclusions: 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.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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