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The relation between cochlear implant programming levels and speech perception performance in post-lingually deafened

Enrico Migliorini^1,2, Bastiaan van Dijk³, Birgit Philips⁴

¹Cochlear Technology Centre Belgium, Mechelen, Belgium. emigliorini@cochlear.com.

European Archives of Oto-Rhino-Laryngology : Official Journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : Affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery

|September 4, 2023

Summary

This study found a link between wider electrical dynamic range and better speech perception in adult cochlear implant users. This suggests a need for evidence-based fitting guidelines to improve outcomes and reduce variability.

Keywords:

Cochlear implants Deafness Prosthetics fitting Retrospective Speech perception

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Area of Science:

Audiology
Neuroscience
Biomedical Engineering

Background:

Cochlear implant (CI) fitting is crucial for speech perception but lacks evidence-based guidelines, causing practice variability.
This variability may impact CI users' outcomes and contribute to unexplained poor performance.

Purpose of the Study:

To investigate the relationship between CI fitting parameters and speech perception outcomes in adults with post-lingual deafness.
To explore a data-driven approach to identify key fitting parameters influencing CI user performance.

Main Methods:

Retrospective analysis of 298 data points from experienced adult CI users, including fitting parameters and speech audiometry results.
Correlation analysis followed by Mann-Whitney-Wilcoxon u test comparing parameters between top-scoring and bottom-scoring groups.

Main Results:

Weak correlations were found between dynamic range and speech audiometry outcomes (p<0.05).
A significant difference in electrical dynamic range was observed, with higher-performing subjects exhibiting a wider range.

Conclusions:

The study highlights a potential association between electrical dynamic range and speech perception in CI users.
Further research into evidence-driven CI fitting is needed to minimize outcome variability and address performance issues.