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简单地计算言语流畅性错误即可区分正常认知、轻度认知障碍和阿尔茨海默病。

A simple counting of verbal fluency errors discriminates between normal cognition, mild cognitive impairment and Alzheimer's disease.

机构信息

Department of Neurology and Neurosurgery, Hospital São Paulo, Federal University of São Paulo (UNIFESP), São Paulo, Brazil.

Human Cognitive Neuroscience, Psychology Department, University of Edinburgh, Edinburgh, UK.

出版信息

Neuropsychol Dev Cogn B Aging Neuropsychol Cogn. 2023 May;30(3):370-387. doi: 10.1080/13825585.2022.2035668. Epub 2022 Feb 17.

Abstract

For this observational cross-sectional study, different modalities of verbal fluency tasks (VFTs) were compared between 143 participants: 35 cognitively healthy controls (CHCs), 71 mild cognitive impairment (MCI) and 37 mild Alzheimer's disease (AD) patients. Binomial logistic regression models were defined to identify VFT variables associated with MCI and AD, with respect to CHC. The results showed that the best errors/repetitions variable associated with MCI and AD was the phonemic task, and with every error the odds of being in the MCI group increased 9.9 times and 12.2 times in AD group, accompanied by high accuracy values (MCI: AUC = 0.824, sensitivity = 0.676, specificity = 0.943; AD: AUC = 0.883, sensitivity = 0.784, specificity = 0.943). The results suggest that, in addition to solely register raw scores, a simple counting of errors and repetitions during VFT can offer valuable clues in detecting MCI and AD, especially in the phonemic task.

摘要

在这项观察性横断面研究中,比较了 143 名参与者的不同言语流畅性任务(VFT)模式:35 名认知健康对照者(CHC)、71 名轻度认知障碍(MCI)和 37 名轻度阿尔茨海默病(AD)患者。针对 CHC,定义了二项逻辑回归模型,以确定与 MCI 和 AD 相关的 VFT 变量。结果表明,与 MCI 和 AD 相关的最佳错误/重复变量是语音任务,每出现一个错误,MCI 组的几率增加 9.9 倍,AD 组的几率增加 12.2 倍,同时准确率较高(MCI:AUC=0.824,灵敏度=0.676,特异性=0.943;AD:AUC=0.883,灵敏度=0.784,特异性=0.943)。结果表明,除了仅记录原始分数外,在 VFT 过程中简单地计数错误和重复次数可以提供有价值的线索,有助于检测 MCI 和 AD,尤其是在语音任务中。

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