Predicting Cognitive Impairment in Cerebrovascular Disease Using Spoken Discourse Production

IF 1.8 4区医学 Q1 LINGUISTICS

Topics in Language Disorders Pub Date : 2021-01-01 DOI:10.1097/TLD.0000000000000242

A. Roberts, Katharine Aveni, Shalane R Basque, J. Orange, P. McLaughlin, J. Ramirez, A. Troyer, Stephanie Gutierrez, Angie Chen, R. Bartha, M. Binns, S. Black, L. Casaubon, D. Dowlatshahi, A. Hassan, D. Kwan, B. Levine, J. Mandzia, D. Sahlas, C. Scott, S. Strother, K. Sunderland, S. Symons, R. Swartz

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引用次数: 2

Abstract

Supplemental Digital Content is Available in the Text. Purpose: Dementia due to cerebrovascular disease (CVD) is common. Detecting early cognitive decline in CVD is critical because addressing risk factors may slow or prevent dementia. This study used a multidomain discourse analysis approach to determine the spoken language signature of CVD-related cognitive impairment. Method: Spoken language and neuropsychological assessment data were collected prospectively from 157 participants with CVD as part of the Ontario Neurodegenerative Disease Research Initiative, a longitudinal, observational study of neurodegenerative disease. Participants were categorized as impaired (n = 92) or cognitively normal for age (n = 65) based on neuropsychology criteria. Spoken language samples were transcribed orthographically and annotated for 13 discourse features, across five domains. Discriminant function analyses were used to determine a minimum set of discourse variables, and their estimated weights, for maximizing diagnostic group separation. Results: The optimal discriminant function that included 10 of 13 discourse measures correctly classified 78.3% of original cases (69.4% cross-validated cases) with a sensitivity of 77.2% and specificity of 80.0%. Conclusion: Spoken discourse appears to be a sensitive measure for detecting cognitive impairment in CVD with measures of productivity, information content, and information efficiency heavily weighted in the final algorithm.

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利用口语语篇生成预测脑血管病患者的认知障碍

文本中提供了补充数字内容。目的：脑血管疾病引起的痴呆是常见的。发现CVD的早期认知能力下降至关重要，因为解决风险因素可以减缓或预防痴呆。本研究采用多领域语篇分析方法来确定心血管疾病相关认知障碍的口语特征。方法：前瞻性地收集157名CVD参与者的口语和神经心理评估数据，作为安大略省神经退行性疾病研究计划的一部分，该计划是一项关于神经退行性病变的纵向观察性研究。根据神经心理学标准，参与者被分为受损（n=92）或认知正常（n=65）。口语样本被正字法转录并注释了五个领域的13个话语特征。判别函数分析用于确定最小的话语变量集及其估计权重，以最大限度地提高诊断组的分离度。结果：包含13个话语测量中的10个的最优判别函数正确地分类了78.3%的原始病例（69.4%的交叉验证病例），敏感性为77.2%，特异性为80.0%，并且信息效率在最终算法中被严重加权。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Topics in Language Disorders Multiple-

CiteScore

3.50

自引率

0.00%

发文量

期刊介绍： Topics in Language Disorders (TLD) is a double-blind peer-reviewed topical journal that has dual purposes: (1) to serve as a scholarly resource for researchers and clinicians who share an interest in spoken and written language development and disorders across the lifespan, with a focus on interdisciplinary and international concerns; and (2) to provide relevant information to support theoretically sound, culturally sensitive, research-based clinical practices.