González David Andrés, Gonzales Mitzi M, Jennette Kyle J, Soble Jason R, Fongang Bernard
Department of Neurology University of Texas Health Science Center at San Antonio San Antonio Texas USA.
Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases University of Texas Health Science Center at San Antonio San Antonio Texas USA.
Alzheimers Dement (Amst). 2021 Dec 8;13(1):e12250. doi: 10.1002/dad2.12250. eCollection 2021.
Cognitive screening measures often lack sensitivity and are hampered by inequities across ethnoracial groups. A multitrait multimethod (MTMM) classification may attenuate these shortcomings.
A sample of 7227 participants across the diagnostic spectrum were selected from the National Alzheimer's Coordinating Center cohort. Random forest ensemble methods were used to predict diagnosis across the sample and within Black American (= 1025) and non-Hispanic White groups (= 5263) based on: (1) a demographically corrected Montreal Cognitive Assessment (MoCA), (2) MoCA and Functional Assessment Questionnaire (FAQ), (3) MoCA and FAQ with demographic correction.
The MTMM approach with demographic correction had the highest diagnostic accuracy for the cognitively unimpaired (area under curve [AUC] [95% confidence interval (CI)]): 0.906 [0.892, 0.920]) and mild cognitive impairment (AUC: 0.835 [0.810, 0.860]) groups and reduced racial disparities.
With further validation, the MTMM approach combining cognitive screening and functional status assessment may serve to improve diagnostic accuracy and extend opportunities for early intervention with greater equity.
认知筛查措施往往缺乏敏感性，且受到不同种族群体间不平等现象的阻碍。多特质多方法（MTMM）分类法可能会减轻这些缺点。
从国家阿尔茨海默病协调中心队列中选取了7227名涵盖不同诊断范围的参与者作为样本。基于以下因素，使用随机森林集成方法在整个样本以及美国黑人组（=1025）和非西班牙裔白人组（=5263）内预测诊断结果：（1）经人口统计学校正的蒙特利尔认知评估量表（MoCA）；（2）MoCA和功能评估问卷（FAQ）；（3）经人口统计学校正的MoCA和FAQ。
经人口统计学校正的MTMM方法对认知未受损组（曲线下面积[AUC][95%置信区间（CI）]：0.906[0.892，0.920]）和轻度认知障碍组（AUC：0.835[0.810，0.860]）具有最高的诊断准确性，并减少了种族差异。
经过进一步验证，结合认知筛查和功能状态评估的MTMM方法可能有助于提高诊断准确性，并以更大的公平性扩大早期干预的机会。
