A predictive model for diagnosing stroke-related apraxia of speech
A predictive model for diagnosing stroke-related apraxia of speech
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DOI：
10.1016/j.neuropsychologia.2015.12.010
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发表时间：
2016-01-29
期刊：
影响因子：
2.6
通讯作者：
Robin, Donald A.
中科院分区：
文献类型：
--
作者：
Ballard, Kirrie J.;Azizi, Lamiae;Robin, Donald A.
言语运动计划/编程障碍、言语失用症 (AOS) 的诊断已被证明具有挑战性，这主要是因为它与基于语言的失语症障碍常见共存。目前，诊断基于感知识别和评估几种语音特征的严重性。目前尚不清楚阳性诊断是否需要全部或部分特征。本研究的目的是评估左半球卒中后 AOS 存在的预测变量，目的是提高诊断的客观性和效率。这项基于人群的病例对照研究涉及 72 个病例样本，使用专家判断 AOS 存在的结果测量，并包括大量独立收集的候选预测变量，代表语言、认知、非言语口腔运动和言语运动能力的行为测量。我们使用多重插补构建了一个预测模型来处理缺失数据；使用最小绝对收缩和选择算子 (Lasso) 技术进行变量选择以定义最相关的预测变量，并通过引导来检查模型稳定性并量化所开发模型的乐观程度。有两项测量足以区分患有 AOS 合并失语症和仅患有失语症的参与者，（1）测量长度逐渐增加的单词的言语错误，以及（2）测量具有弱强重音模式的三音节单词（例如香蕉、土豆）的相对元音持续时间。该模型具有很高的区分能力来区分有和没有 AOS 的情况（c 指数 = 0.93），并且观察到的概率和预测的概率之间具有良好的一致性（校准斜率 = 0.94）。考虑到左半球中风的特定样本相对较小，以及估算缺失数据的局限性，需要谨慎行事。这两种言语测量易于收集和分析，便于在研究和临床环境中使用。 (C) 2015 年，爱思唯尔有限公司出版。
Diagnosis of the speech motor planning/programming disorder, apraxia of speech (AOS), has proven challenging, largely due to its common co-occurrence with the language-based impairment of aphasia. Currently, diagnosis is based on perceptually identifying and rating the severity of several speech features. It is not known whether all, or a subset of the features, are required for a positive diagnosis. The purpose of this study was to assess predictor variables for the presence of AOS after left-hemisphere stroke, with the goal of increasing diagnostic objectivity and efficiency. This population-based case control study involved a sample of 72 cases, using the outcome measure of expert judgment on presence of AOS and including a large number of independently collected candidate predictors representing behavioral measures of linguistic, cognitive, nonspeech oral motor, and speech motor ability. We constructed a predictive model using multiple imputation to deal with missing data; the Least Absolute Shrinkage and Selection Operator (Lasso) technique for variable selection to define the most relevant predictors, and bootstrapping to check the model stability and quantify the optimism of the developed model. Two measures were sufficient to distinguish between participants with AOS plus aphasia and those with aphasia alone, (1) a measure of speech errors with words of increasing length and (2) a measure of relative vowel duration in three-syllable words with weak strong stress pattern (e.g., banana, potato). The model has high discriminative ability to distinguish between cases with and without AOS (c-index =0.93) and good agreement between observed and predicted probabilities (calibration slope =0.94). Some caution is warranted, given the relatively small sample specific to left-hemisphere stroke, and the limitations of imputing missing data. These two speech measures are straightforward to collect and analyse, facilitating use in research and clinical settings. (C) 2015 Published by Elsevier Ltd.