ABSTRACT

Background: The Western Aphasia Battery (WAB) is one of the most widely used batteries for assessing people with aphasia. Despite longstanding use, it is unclear how the individual components of the battery contribute to the aphasia quotient (AQ), which profiles aphasia severity.

Objectives: The primary objective of this study was to explore the individual contributions of the four major components (Spontaneous Speech, Auditory Verbal Comprehension, Repetition, Naming) and the 10 subtests of the WAB to the WAB AQ using relative weight analysis (RWA). The second objective was to evaluate whether the contributions that these components and subtests make to the WAB AQ are influenced by aphasia type (fluent vs non-fluent).

Methods & Procedures: Data for 288 persons with aphasia obtained from the AphasiaBank were analyzed in this study. RWA was performed with the R relaimpo package 2.2–3 using WAB AQ scores, scores from the major components, and scores from the10 subtests that are used to calculate the AQ. A second RWA was completed to examine whether the relative weights for these measures vary as a function of aphasia type (fluent vs non-fluent).

Outcomes & Results: Results of RWA for the four major components of the AQ indicated that Spontaneous Speech contributes ~30%, Auditory Verbal Comprehension 20%, Repetition 25%, and Naming/Word Finding 25% to the AQ. RWA for the 10 WAB subtests revealed the major contributors to be Fluency (14.4%), Repetition (14.1%), Information Content (13.1%), and Object Naming (10.5%). In comparisons of the four major areas between individuals with fluent vs non-fluent aphasia, only the contribution of Spontaneous Speech differed between the two groups with this area contributing 31% to the AQ in those individuals with fluent aphasia and 28% in those with non-fluent aphasia.

Conclusions: RWA demonstrated that the major components of the WAB do not contribute equally to the AQ but that the contribution of Spontaneous Speech is less than has been suggested previously. The strongest contributors to the AQ are primarily measures of expressive language. This greater influence of the expressive language measures on the AQ should be considered carefully when interpreting the AQ and aphasia severity.

摘要

背景：西部失语症电池 (WAB) 是用于评估失语症患者的最广泛使用的电池之一。尽管长期使用，但尚不清楚电池的各个组件如何影响失语商数 (AQ)，该商数描述失语症的严重程度。

目标：本研究的主要目标是使用相对权重分析 (RWA) 探索四个主要组成部分（自发言语、听觉口头理解、重复、命名）和 WAB 的 10 个子测试对 WAB AQ 的个人贡献。第二个目标是评估这些组件和子测试对 WAB AQ 的贡献是否受失语类型（流利与非流利）的影响。

方法和程序：本研究分析了从失语银行获得的 288 名失语症患者的数据。RWA 使用 R relaimpo 包 2.2-3 执行，使用 WAB AQ 分数、主要组件的分数以及用于计算 AQ 的 10 个子测试的分数。完成了第二个 RWA 以检查这些测量的相对权重是否随失语类型（流利与非流利）而变化。

结果和结果： AQ 四个主要组成部分的 RWA 结果表明，自发言语对 AQ 的贡献约为 30%，听觉语言理解为 20%，重复 25%，命名/单词查找为 25%。10 个 WAB 子测试的 RWA 显示主要贡献者是流畅性 (14.4%)、重复性 (14.1%)、信息内容 (13.1%) 和对象命名 (10.5%)。在流利性失语症与非流利性失语症个体的四个主要区域的比较中，只有自发言语的贡献在两组之间有所不同，该区域对流利性失语症个体的 AQ 贡献率为 31%，非流利性失语症个体的 AQ 贡献率为 28%。 - 流利的失语症。

结论： RWA 表明 WAB 的主要组成部分对 AQ 的贡献并不相等，但自发语音的贡献低于之前建议的。AQ 的最大贡献者主要是表达性语言的衡量标准。在解释 AQ 和失语症严重程度时，应仔细考虑表达性语言测量对 AQ 的更大影响。