Article
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Preserved in Portico This version is not peer-reviewed
Evolving a Model for Cochlear Implant Outcome
Version 1
: Received: 27 July 2023 / Approved: 28 July 2023 / Online: 31 July 2023 (11:47:35 CEST)
A peer-reviewed article of this Preprint also exists.
Hoppe, U.; Hast, A.; Hornung, J.; Hocke, T. Evolving a Model for Cochlear Implant Outcome. J. Clin. Med. 2023, 12, 6215. Hoppe, U.; Hast, A.; Hornung, J.; Hocke, T. Evolving a Model for Cochlear Implant Outcome. J. Clin. Med. 2023, 12, 6215.
Abstract
Background: Cochlear implantation is an efficient treatment for postlingually deafened adults who do not benefit sufficiently from acoustic amplification. Implantation is indicated when it can be foreseen that speech recognition with a cochlear implant (CI) is superior to that with a hearing aid. Especially for subjects with residual speech recognition it is desirable to predict CI outcome on the basis of preoperative audiological tests. Purpose: The purpose of the study was to extend and to refine a previously developed model for CI outcome prediction for subjects with preoperative word recognition to include subjects with no residual hearing by incorporat-ing additional results of routine examinations. Results: By introducing the duration of unaided hearing loss (DuHL) the median absolute error (MAE) of the prediction was reduced. While for subjects with preoperative speech recognition the model modification did not change the MAE, for subjects with no residual speech recognition before surgery, the MAE decreased from 23.7% with the previous model to 17.2% with the extended model. Conclusions: Prediction of word recognition with CI is possible within clinically relevant limits. Outcome prediction is particu-larly important for preoperative counselling and in CI aftercare, to support systematic monitor-ing of CI fitting.
Keywords
Word Recognition; CI Outcome; prediction; generalised linear model; adults
Subject
Medicine and Pharmacology, Otolaryngology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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