Cochlear Implant Upper Stimulation Levels: eSRT vs. Loudness Scaling.

OBJECTIVE: To assess the difference in speech recognition and sound quality between programming upper stimulation levels using behavioral measures (loudness scaling) and electrically evoked stapedial reflex thresholds (eSRTs). STUDY DESIGN: Double-blinded acute comparison study. SETTING: Cochlear implant (CI) program at a tertiary medical center. PATIENTS: Eighteen adult (mean age = 60 years) CI users and 20 ears. MAIN OUTCOME MEASURES: Speech recognition scores and sound quality ratings. RESULTS: Mean word and sentence in noise recognition scores were 8 and 9 percentage points higher, respectively, for the eSRT-based map. The sound quality rating was 1.4 points higher for the eSRT-based map. Sixteen out of 20 participants preferred the eSRT-based map. CONCLUSIONS: Study results show significantly higher speech recognition and more favorable sound quality using an eSRT-based map compared with a loudness-scaling map using a double-blinded testing approach. Additionally, results may be understated as 18 of 20 ears had eSRTs measured before study enrollment. Results underscore the importance of incorporating eSRTs into standard clinical practice to promote best outcomes for CI recipients.

Early Datalogging Predicts Cochlear Implant Performance: Building a Recommendation for Daily Device Usage.

OBJECTIVE: To quantify the effect of datalogging on speech recognition scores and time to achievement for a "benchmark" level of performance within the first year, and to provide a data-driven recommendation for minimum daily cochlear implant (CI) device usage to better guide patient counseling and future outcomes. STUDY DESIGN: Retrospective cohort. SETTING: Tertiary referral center. PATIENTS: Three hundred thirty-seven adult CI patients with data logging and speech recognition outcome data who were implanted between August 2015 and August 2020. MAIN OUTCOME MEASURES: Processor datalogging, speech recognition scores, achievement of "benchmark speech recognition performance" defined as 80% of the median score for speech recognition outcomes at our institution. RESULTS: The 1-month datalogging measure correlated positively with word and sentences scores at 1, 3, 6, and 12 months postactivation. Compared with age, sex, and preoperative performance, datalogging was the largest predictive factor of benchmark achievement on multivariate analysis. Each hour/day increase of device usage at 1 month resulted in a higher likelihood of achieving benchmark consonant-nucleus-consonant and AzBio scores within the first year (odds ratio = 1.21, p < 0.001) as well as earlier benchmark achievement. Receiver operating characteristic curve analysis identified the optimal data logging threshold at an average of 12 hours/day. CONCLUSIONS: Early CI device usage, as measured by 1-month datalogging, predicts benchmark speech recognition achievement in adults. Datalogging is an important predictor of CI performance within the first year postimplantation. These data support the recommended daily CI processor utilization of at least 12 hours/day to achieve optimal speech recognition performance for most patients.