Clinical Assessment of Characteristics of Apraxia of Speech in Primary Progressive Aphasia.

Purpose We sought to examine interrater reliability in clinical assessment of apraxia of speech (AOS) in individuals with primary progressive aphasia and to identify speech characteristics predictive of AOS diagnosis. Method Fifty-two individuals with primary progressive aphasia were recorded performing a variety of speech tasks. These recordings were viewed by 2 experienced speech-language pathologists, who independently rated them on the presence and severity of AOS as well as 14 associated speech characteristics. We calculated interrater reliability (percent agreement and Cohen's kappa) for these ratings. For each rater, we used stepwise regression to identify speech characteristics significantly predictive of AOS diagnosis. We used the overlap between raters to create a more parsimonious model, which we evaluated with multiple linear regression. Results Results yielded high agreement on the presence (90%) and severity of AOS (weighted Cohen's κ = .834) but lower agreement for specific speech characteristics (weighted Cohen's κ ranging from .036 to .582). Stepwise regression identified 2 speech characteristics predictive of AOS diagnosis for both raters (articulatory groping and increased errors with increased length/complexity). These alone accounted for ≥ 50% of the variance of AOS severity in the constrained model. Conclusions Our study adds to a growing body of research that highlights the difficulty in objective clinical characterization of AOS and perceptual characterization of speech features. It further supports the need for consensus diagnostic criteria with standardized testing tools and for the identification and validation of objective markers of AOS. Additionally, these findings underscore the need for a training protocol if diagnostic tools are to be effective when shared beyond the research teams that develop and test them and disseminated to practicing speech-language pathologists, in order to ensure consistent application.