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.

The Impact of Daily Processor Use on Adult Cochlear Implant Outcomes: Reexamining the Roles of Duration of Deafness and Age at Implantation.

OBJECTIVE: To quantify the roles and relationships between age at implantation, duration of deafness (DoD), and daily processor use via data logging on speech recognition outcomes for postlingually deafened adults with cochlear implants. STUDY DESIGN: Retrospective case review. SETTING: Cochlear implant (CI) program at a tertiary medical center. PATIENTS: Six-hundred fourteen postlingually deafened adult ears with CIs (mean age, 63 yr; 44% female) were included. MAIN OUTCOME MEASURES: A stepwise multiple regression analysis was completed to investigate the combined effects of age, DoD, and daily processor use on CI-aided speech recognition (Consonant-Nucleus-Consonant monosyllables and AzBio sentences). RESULTS: Results indicated that only daily processor use was significantly related to Consonant-Nucleus-Consonant word scores ( R2 = 0.194, p < 0.001) and AzBio in quiet scores ( R2 = 0.198, p < 0.001), whereas neither age nor DoD was significantly related. In addition, there was no significant relationship between daily processor use, age at implantation, or DoD and AzBio sentences in noise ( R2 = 0.026, p = 0.005). CONCLUSIONS: Considering the clinical factors of age at implantation, DoD, and daily processor use, only daily processor use significantly predicted the ~20% of variance in postoperative outcomes (CI-aided speech recognition) accounted for by these clinical factors.

Functional localization of audiovisual speech using near infrared spectroscopy.

Visual cues are especially vital for hearing impaired individuals such as cochlear implant (CI) users to understand speech in noise. Functional Near Infrared Spectroscopy (fNIRS) is a light-based imaging technology that is ideally suited for measuring the brain activity of CI users due to its compatibility with both the ferromagnetic and electrical components of these implants. In a preliminary step toward better elucidating the behavioral and neural correlates of audiovisual (AV) speech integration in CI users, we designed a speech-in-noise task and measured the extent to which 24 normal hearing individuals could integrate the audio of spoken monosyllabic words with the corresponding visual signals of a female speaker. In our behavioral task, we found that audiovisual pairings provided average improvements of 103% and 197% over auditory-alone listening conditions in -6 and -9 dB signal-to-noise ratios consisting of multi-talker background noise. In an fNIRS task using similar stimuli, we measured activity during auditory-only listening, visual-only lipreading, and AV listening conditions. We identified cortical activity in all three conditions over regions of middle and superior temporal cortex typically associated with speech processing and audiovisual integration. In addition, three channels active during the lipreading condition showed uncorrected correlations associated with behavioral measures of audiovisual gain as well as with the McGurk effect. Further work focusing primarily on the regions of interest identified in this study could test how AV speech integration may differ for CI users who rely on this mechanism for daily communication.