Ichii Sadanobu, Oba Hikaru, Sugimura Yoshikuni, Yang Yichi, Shoji Mikio, Ihara Kazushige
Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan.
Graduate School of Health Sciences, Hirosaki University, Hirosaki 036-8564, Japan.
Healthcare (Basel). 2024 Jul 10;12(14):1379. doi: 10.3390/healthcare12141379.
The predictive abilities of computer-based screening devices for early cognitive decline (CD) in older adults have rarely been longitudinally examined. Therefore, this study examined the ability of CogEvo, a short-duration, computer-based cognitive screening device requiring little professional involvement, to predict CD among community-dwelling older adults. We determined whether 119 individuals aged ≥ 65 years living in Japanese rural communities who scored ≥ 24 on the Mini-Mental State Examination (MMSE) at baseline developed CD by annually administering the MMSE to them. CD was defined as an MMSE score of ≤23. At baseline, the overall CogEvo judgment grade, with lower grades indicating better cognitive function, was calculated from the results of various cognitive tasks. Over 2 years, 10 participants developed CD. Participants with grades of 4 had a higher percentage of CD cases than those with grades of ≤3 ( < 0.01). This relationship remained significant after controlling for possible confounders, including the MMSE score at baseline. The sensitivity and specificity of the CogEvo grade cutoff of 4 were 50.0% and 93.6%, respectively. In conclusion, CogEvo may be an efficient tool for identifying individuals at a high risk for dementia. The possibility of missing CD cases should be considered when using CogEvo for screening.
基于计算机的筛查设备对老年人早期认知衰退（CD）的预测能力鲜有纵向研究。因此，本研究考察了CogEvo这一短时长、基于计算机的认知筛查设备（几乎无需专业人员参与）对社区居住老年人中CD的预测能力。我们对119名年龄≥65岁、居住在日本农村社区且在基线时简易精神状态检查表（MMSE）得分≥24的个体，通过每年对其进行MMSE测试，来确定他们是否发生了CD。CD定义为MMSE得分≤23。在基线时，根据各种认知任务的结果计算出总体CogEvo判断等级，等级越低表明认知功能越好。在2年多的时间里，有10名参与者发生了CD。等级为4的参与者中CD病例的百分比高于等级≤3的参与者（<0.01）。在控制了包括基线时MMSE得分在内的可能混杂因素后，这种关系仍然显著。CogEvo等级临界值为4时的敏感性和特异性分别为50.0%和93.6%。总之，CogEvo可能是一种识别痴呆高风险个体的有效工具。使用CogEvo进行筛查时应考虑漏诊CD病例的可能性。
