Suppr超能文献

一项关于CogEvo对老年人认知衰退预测的纵向研究。

A Longitudinal Study of CogEvo's Prediction of Cognitive Decline in Older Adults.

作者信息

Ichii Sadanobu, Oba Hikaru, Sugimura Yoshikuni, Yang Yichi, Shoji Mikio, Ihara Kazushige

机构信息

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.

出版信息

Healthcare (Basel). 2024 Jul 10;12(14):1379. doi: 10.3390/healthcare12141379.

Abstract

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.

摘要

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。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验