Takechi Hajime, Yoshino Hiroshi
Department of Geriatrics and Cognitive Disorders, Fujita Health University School of Medicine, Toyoake, Japan.
Geriatr Gerontol Int. 2021 Feb;21(2):192-196. doi: 10.1111/ggi.14110. Epub 2020 Dec 17.
This study aimed to assess whether CogEvo, a computerized cognitive assessment and training tool, could distinguish patients with mild Alzheimer's disease and mild cognitive impairment from cognitively normal older people.
This cross-sectional study enrolled 166 participants with Alzheimer's disease, mild cognitive impairment and cognitively normal older people. In CogEvo, five types of cognitive tasks were carried out, and the z-scores were used as a composite score. Logistic regression and receiver operating characteristics analyses were then carried out to evaluate the usefulness of CogEvo in distinguishing between the three groups.
CogEvo and Mini-Mental State Examination scores showed excellent correlation, and could significantly differentiate between the Alzheimer's disease, mild cognitive impairment and cognitively normal older people groups (Mini-Mental State Examination 20.4 ± 3.5, 25.5 ± 1.6 and 27.6 ± 2.0, respectively; CogEvo: -1.9 ± 0.9, -0.8 ± 0.8 and 0.0 ± 1.0, respectively; both P < 0.001 by analysis of variance). Logistic regression analysis adjusted for age, sex and years of education significantly differentiated the mild cognitive dysfunction group (mild cognitive impairment plus mild Alzheimer's disease; n = 78) from the cognitively normal group (n = 88) (P < 0.001), whereas receiver operating characteristics analysis showed moderate accuracy (area under the receiver operating characteristic curve 0.830).
These results suggest that CogEvo, a computerized cognitive assessment tool, is useful for evaluating early-stage cognitive impairment. Further studies are required to assess its effectiveness as a combination assessment and training tool. Geriatr Gerontol Int 2021; 21: 192-196.
本研究旨在评估计算机化认知评估与训练工具CogEvo能否区分轻度阿尔茨海默病患者、轻度认知障碍患者与认知正常的老年人。
这项横断面研究纳入了166名患有阿尔茨海默病、轻度认知障碍以及认知正常的老年人。在CogEvo中进行了五种类型的认知任务,并将z分数用作综合评分。然后进行逻辑回归和受试者工作特征分析,以评估CogEvo在区分这三组人群方面的效用。
CogEvo分数与简易精神状态检查表分数显示出极佳的相关性,并且能够显著区分阿尔茨海默病组、轻度认知障碍组和认知正常的老年人群组(简易精神状态检查表得分分别为20.4±3.5、25.5±1.6和27.6±2.0;CogEvo分数分别为-1.9±0.9、-0.8±0.8和0.0±1.0;方差分析均P<0.001)。经年龄、性别和受教育年限校正的逻辑回归分析显著区分了轻度认知功能障碍组(轻度认知障碍加轻度阿尔茨海默病;n = 78)与认知正常组(n = 88)(P<0.001),而受试者工作特征分析显示其具有中等准确性(受试者工作特征曲线下面积为0.830)。
这些结果表明,计算机化认知评估工具CogEvo有助于评估早期认知障碍。需要进一步研究以评估其作为综合评估与训练工具的有效性。《老年医学与老年病学国际杂志》2021年;21: 192 - 196。