Are Speech Perception Scores in Cochlear Implant Recipients Consistent Across Different Tests?

Journal: Otology & Neurotology : Official Publication Of The American Otological Society, American Neurotology Society [And] European Academy Of Otology And Neurotology

Objective: Cochlear implant (CI) candidacy and postoperative outcomes are assessed using sets of speech perception tests that vary from center to center, limiting comparisons across institutions and time periods. The objective of this study was to determine if scores on one speech perception test could be reliably predicted from scores on another test.

Methods: Arizona Biomedical (AzBio) Sentence Test, Consonant-Nucleus-Consonant word (CNCw), and Hearing in Noise Test (HINT) scores in quiet for the implanted ear were collected for individuals who received a CI between 1985 and 2019. Scores collected during the same testing session were analyzed using Bland-Altman plots to assess agreement between testing methods. Simple linear regression with logit transformation was used to generate predictive functions and 95% confidence intervals for expected mean and individual scores. Methods: Single academic medical center. Methods: A total of 1,437 individuals with a median age of 59.9 years (range, 18-95 yr) and 46% (654 of 1,437) male. Methods: N.A. Methods: Agreement as a function of test score, mean, variance, and correlation coefficients.

Results: A total of 2,052 AzBio/CNCw, 525 AzBio/HINT, and 7,187 CNCw/HINT same-session score pairings were identified. Pairwise test comparisons demonstrated limited agreement between different tests performed in the same session, and a score correlation between different speech tests revealed large variances.

Conclusions: Transformation functions between test batteries were predictive of mean scores but performed poorly for prediction of individual scores. Point-wise comparisons of scores across CI test batteries should be used with caution in clinical and research settings.

Nicholas Andresen, Varun Vohra, Deepa Galaiya, Courtney Carver, Dawn Marsiglia, Jennifer Yeagle, Francis Creighton, Nae-yuh Wang, Stephen Bowditch, Charles Della Santina, Daniel Sun

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