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 refine a previously developed model for CI outcome prediction for subjects with preoperative word recognition to include subjects with no residual hearing by incorporating 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 particularly important for preoperative counseling and in CI aftercare to support systematic monitoring of CI fitting.

Background: The cochlear implant (CI) is an established treatment option for patients with inadequate speech understanding and insufficient aided scores. Nevertheless, reliable predictive models and specific therapy goals regarding achievable speech understanding are still lacking. Method: In this retrospective study, 601 cases of CI fittings between 2005 and 2021 at the University Medical Center Freiburg were analyzed. We investigated the preoperative unaided maximum word recognition score (mWRS) as a minimum predictor for post-interventional scores at 65 dB SPL, WRS₆₅(CI). The WRS₆₅(CI) was compared with the preoperative-aided WRS, and a previously published prediction model for the WRS₆₅(CI) was reviewed. Furthermore, the effect of duration of hearing loss, duration of HA fitting, and etiology on WRS₆₅(CI) were investigated. Results: In 95.5% of the cases, a significant improvement in word recognition was observed after CI. WRS₆₅(CI) achieved or exceeded mWRS in 97% of cases. Etiology had a significant impact on WRS₆₅(CI). The predicted score was missed by more than 20 percentage points in 12.8% of cases. Discussion: Our results confirmed the minimum prediction via mWRS. A more precise prediction of the expected WRS₆₅(CI) is possible. The etiology of hearing loss should be considered in the indication and postoperative care to achieve optimal results.

Background: To assess the performance of cochlear implant users, speech comprehension benefits are generally measured in controlled sound room environments of the laboratory. For field-based assessment of preference, questionnaires are generally used. Since questionnaires are typically administered at the end of an experimental period, they can be inaccurate due to retrospective recall. An alternative known as ecological momentary assessment (EMA) has begun to be used for clinical research. The objective of this study was to determine the feasibility of using EMA to obtain in-the-moment responses from cochlear implant users describing their technology preference in specific acoustic listening situations. Methods: Over a two-week period, eleven adult cochlear implant users compared two listening programs containing different sound processing technologies during everyday take-home use. Their task was to compare and vote for their preferred program. Results: A total of 205 votes were collected from acoustic environments that were classified into six listening scenes. The analysis yielded different patterns of voting among the subjects. Two subjects had a consistent preference for one sound processing technology across all acoustic scenes, three subjects changed their preference based on the acoustic scene, and six subjects had no conclusive preference for either technology. Conclusion: Results show that EMA is suitable for quantifying real-world self-reported preference, showing inter-subject variability in different listening environments. However, there is uncertainty that patients will not provide sufficient spontaneous feedback. One improvement for future research is a participant forced prompt to improve response rates.

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.

Background: In cochlear implant (CI) treatment, there is a large variability in outcome. The aim of our study was to identify the independent audiometric measures that are most directly relevant for describing this variability in outcome characteristics of CI recipients. An extended audiometric test battery was used with selected adult patients in order to characterize the full range of CI outcomes. Methods: CI users were recruited for this study on the basis of their postoperative results and divided into three groups: low (1st quartile), moderate (medium decentile), and high hearing performance (4th quartile). Speech recognition was measured in quiet by using (i) monosyllabic words (40–80 dB SPL), (ii) speech reception threshold (SRT) for numbers, and (iii) the German matrix test in noise. In order to reconstruct demanding everyday listening situations in the clinic, the temporal characteristics of the background noise and the spatial arrangements of the signal sources were varied for tests in noise. In addition, a survey was conducted using the Speech, Spatial, and Qualities (SSQ) questionnaire and the Listening Effort (LE) questionnaire. Results: Fifteen subjects per group were examined (total N = 45), who did not differ significantly in terms of age, time after CI surgery, or CI use behavior. The groups differed mainly in the results of speech audiometry. For speech recognition, significant differences were found between the three groups for the monosyllabic tests in quiet and for the sentences in stationary (S0°N0°) and fluctuating (S0°NCI) noise. Word comprehension and sentence comprehension in quiet were both strongly correlated with the SRT in noise. This observation was also confirmed by a factor analysis. No significant differences were found between the three groups for the SSQ questionnaire and the LE questionnaire results. The results of the factor analysis indicate that speech recognition in noise provides information highly comparable to information from speech intelligibility in quiet. Conclusions: The factor analysis highlighted three components describing the postoperative outcome of CI patients. These were (i) the audiometrically measured supra-threshold speech recognition and (ii) near-threshold audibility, as well as (iii) the subjective assessment of the relationship to real life as determined by the questionnaires. These parameters appear well suited to setting up a framework for a test battery to assess CI outcomes.