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

ABSTRACT

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.