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用于辅助神经退行性和功能性记忆障碍鉴别诊断的互动特征分析。

An Interactional Profile to Assist the Differential Diagnosis of Neurodegenerative and Functional Memory Disorders.

机构信息

Department of Neuroscience, University of Sheffield.

School of Allied Health Sciences, De Montfort University, Leicester.

出版信息

Alzheimer Dis Assoc Disord. 2018 Jul-Sep;32(3):197-206. doi: 10.1097/WAD.0000000000000231.

Abstract

OBJECTIVE

Specialist services for dementia are seeing an increasing number of patients. We investigated whether interactional and linguistic features in the communication behavior of patients with memory problems could help distinguish between those with problems secondary to neurological disorders (ND) and those with functional memory disorder (FMD).

METHODS

In part 1 of this study, a diagnostic scoring aid (DSA) was developed encouraging linguists to provide quantitative ratings for 14 interactional features. An optimal cut-off differentiating ND and FMD was established by applying the DSA to 30 initial patient-doctor memory clinic encounters. In part 2, the DSA was tested prospectively in 10 additional cases analyzed independently by 2 conversation analysts blinded to medical information.

RESULTS

In part 1, the median score of the DSA was +5 in ND and -5 in FMD (P<0.001). The optimal numeric DSA cut-off (+1) identified patients with ND with a sensitivity of 86.7% and a specificity of 100%. In part 2, DSA scores of rater 1 correctly predicted 10/10 and those of rater 2 predicted 9/10 diagnoses.

CONCLUSIONS

This study indicates that interactional and linguistic features can help distinguish between patients developing dementia and those with FMD and could aid the stratification of patients with memory problems.

摘要

目的

痴呆症专科服务的就诊人数不断增加。我们研究了记忆问题患者的交流行为中的交互和语言特征是否有助于区分继发于神经障碍(ND)的患者和功能性记忆障碍(FMD)患者。

方法

在这项研究的第一部分,开发了一种诊断评分辅助工具(DSA),鼓励语言学家对 14 种交互特征进行定量评分。通过将 DSA 应用于 30 例初始患者-医生记忆诊所就诊,确定了区分 ND 和 FMD 的最佳截止值。在第二部分,10 例额外的病例前瞻性地使用 DSA 进行测试,由 2 位对话分析师独立分析,他们对医疗信息一无所知。

结果

在第一部分,DSA 的中位数评分在 ND 中为+5,在 FMD 中为-5(P<0.001)。最佳数值 DSA 截止值(+1)可识别出 ND 患者,其敏感性为 86.7%,特异性为 100%。在第二部分,评分者 1 的 DSA 评分正确预测了 10/10 例,评分者 2 的 DSA 评分预测了 9/10 例。

结论

本研究表明,交互和语言特征可帮助区分痴呆症发展患者和 FMD 患者,并有助于对记忆问题患者进行分层。

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Lancet Neurol. 2022 Jun;21(6):537-550. doi: 10.1016/S1474-4422(21)00422-1. Epub 2022 Apr 14.

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