自動化されたコグニビュー認知評価の人工内耳術後成績予測における価値 - 個人開業耳鼻咽喉科診療所における予備的検討
Barak M Spector1, Riley Gilbertson2, Jedidiah Grisel3
1Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, 1215 21st Avenue South, Suite 10420, Medical Center East, South Tower, Nashville, TN 37232, USA.
Objectives:
Broad variability exists in the outcomes of adult cochlear implant (CI) recipients. This study assessed performance of adult CI recipients on Cognivue Thrive, a quick, automated cognitive screening device applied in a private Otolaryngology practice, and assessed its ability to predict CI outcomes.
Methods:
Thirty-one adults who received CIs between September 2020 and December 2022 were enrolled and tested using Cognivue Thrive before implantation. Consonant Nucleus Consonant (CNC) words and AzBio sentence recognition scores were collected early (1-3 months) and late (6-12 months) after cochlear implantation. .
Results:
The group demonstrated poor-to-moderate mean scores relative to normative scores in the Cognivue domains of Memory, Visuospatial, and Executive Function and good performance in Reaction Time and Processing Speed. Controlling for age, the pre-operative Cognivue Memory domain score correlated positively with early post-CI CNC word score (rho = .6, P = .009) and AzBio sentence score (rho = .71, P = .002) with large effect sizes. The pre-operative Visuospatial domain score also correlated with early post-CI AzBio sentence score (rho = .62, P = .01) with large effect size. Correlations between Cognivue scores and late post-CI speech recognition were mostly weaker and non-significant.
Discussion:
A pre-operative 5-minute, automated, visual cognitive screening test yields scores that are predictive of short-term adaptation after cochlear implantation but not longer-term speech recognition. A prospective study with larger sample is needed to validate findings. .
Conclusion:
This preliminary study demonstrates proof of concept of applying Cognivue Thrive in a private CI program and its potential to help clinicians and patients to predict CI outcomes.


