Objective: To introduce and discuss implementation strategy for the Complete Cochlear Implant Care (CCIC) model, a highly-coordinated cochlear implant (CI) care delivery model requiring a single on-site visit for preoperative workup, surgery, and postoperative programming. Study Design: Prospective, nonrandomized, two-arm clinical trial. Setting: Tertiary referral CI center. Patients: Adults who meet audiologic criteria for cochlear implantation. Interventions: Cochlear implantation, coordinated care delivery, including remote programming. Main outcome measures: Care delivery model feasibility and process implementation. Results: Patients determined to be likely CI candidates based on routine audiometry are eligible for enrollment. The CCIC model uses telemedicine and electronic educational materials to prepare patients for same-day on-site consultation with CI surgery, same or next-day activation, and postoperative remote programming for 12 months. Implementation challenges include overcoming inertia related to the implementation of a new clinical workflow, whereas scalability of the CCIC model is limited by current hardware requirements for remote programming technology. A dedicated CCIC process coordinator is critical for overcoming obstacles in implementation and process improvement through feedback and iterative changes. Team and patient-facing materials are included and should be tailored to fit each unique CI program looking to implement CCIC. Conclusion: The CCIC model has the potential to dramatically streamline hearing healthcare delivery. Implementation requires an adaptive approach, as obstacles may vary according to institutional infrastructure and policies.

Background/Objectives: Adult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition under the best hearing aid conditions, either in quiet or noisy environments, with speech and noise presented through a single speaker. This study examines the influence of deep neural network-based (DNN-based) noise reduction on cochlear implant evaluation. Methods: Speech perception was assessed using AzBio sentences in both quiet and noisy conditions (multi-talker babble) at 5 and 10 dB signal-to-noise ratios (SNRs) through one loudspeaker. Sentence recognition scores were measured for 10 hearing-impaired patients using three hearing aid programs: calm situation, speech in noise, and spheric speech in loud noise (DNN-based noise reduction). Speech perception results were compared to bench analyses comprising the phase inversion technique, employed to predict SNR improvement, and the Hearing-Aid Speech Perception Index (HASPI v2), utilized to predict speech intelligibility. Results: The spheric speech in loud noise program improved speech perception by 20 to 32% points as compared to the calm situation program. Thus, DNN-based noise reduction can improve speech perception in noisy environments, potentially reducing the need for cochlear implants in some cases. The phase inversion method showed a 4–5 dB SNR improvement for the DNN-based noise reduction program compared to the other two programs. HASPI v2 predicted slightly better speech intelligibility than was measured in this study. Conclusions: DNN-based noise reduction might make it difficult for some patients with significant residual hearing to qualify for cochlear implantation, potentially delaying its adoption or eliminating the need for it entirely.

Background/Objectives: Electrode impedance is crucial for optimizing cochlear implant (CI) stimulation and hearing outcomes. While typically stable, some patients experience unexplained impedance fluctuations. This study used electrode impedance subcomponent analysis to identify the subcomponents contributing to these impedance fluctuations. Methods: This study analyzed clinical electrode impedances and transimpedance matrix (TIM) measurements in 10 CI patients with Nucleus devices (CI422, CI522, or CI622 electrode arrays) who exhibited fluctuating or rising electrode impedances. TIM measurements used a cathodic-leading biphasic pulse (110 CLs, 75 µs/phase, 7 µs interphase interval). Electrode impedances were determined at 6, 12, 18, 24, and 75 µs, and subcomponents (access resistance [near-field/far-field] and polarization impedance [Warburg capacitance/Faraday resistance]) were calculated. Results: Both access resistance and polarization impedance changes contributed to impedance fluctuations. Large changes in near-field resistance compared to far-field resistance were associated with increased resistance to current flow closer to the surface of the electrode. The decreased double-layer capacitance and slightly increased Faraday resistance further suggested increased resistance to charge transfer at the electrode–electrolyte interface. Conclusions: Electrode impedance subcomponent analysis reveals changes in the electrochemical reaction at the electrode surface that cause fluctuating or rising CI electrode impedances.

Background/Objectives: Speech perception is measured in quiet and in the presence of background noise, such as multi-talker babble (MTB), to establish cochlear implant (CI) candidacy and measure post-operative outcomes. Testing protocols often use interrupted noise that begins just before and ends shortly after the target sentence, providing insufficient time for the noise-reduction algorithms to activate in the hearing device and provide an improved signal-to-noise ratio (SNR). Furthermore, interrupted noise does not allow the hearing-impaired user to acclimatize to the abrupt onset of noise or to focus on the target sentence, a challenge related to auditory stream segregation. The present study compares speech perception in bimodal CI users across interrupted and continuous MTB noise conditions to highlight performance differences between these two distinct listening environments. Methods: Speech perception was evaluated for two different bimodal CI groups. Group 1: This group evaluated an automatic hearing aid program across three environments: quiet, continuous MTB, and interrupted MTB. Group 2: This group evaluated two manual hearing aid programs across the same three environments (quiet, continuous, and interrupted MTB). Results: For Group 1, scores in quiet reached 88%, significantly declined in interrupted MTB (38%), but improved in continuous MTB (67%). For Group 2, no significant mean differences were found between interrupted and continuous noise across the two manual programs, though individual variations persisted. Conclusions: Based on the findings of this study, continuous noise rather than interrupted noise should be utilized for establishing CI candidacy and evaluating post-implantation speech perception.

Background/Objectives Adult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition in their best hearing aid condition, either in quiet or noisy environments, with speech and noise presented through a single speaker. This study examines the influence of deep neural network-based (DNN-based) based noise reduction on cochlear implant evaluation. Methods Speech perception was assessed using AzBio sentences in both quiet and noisy conditions (multi-talker babble) at 5 and 10 dB signal-to-noise ratio (SNR) through one loudspeaker. Sentence recognition scores were measured for 10 (5-bilateral, 5-unilateral) hearing-impaired patients using three hearing aid programs: calm situation, speech in noise, and spheric speech in loud noise (DNN-based noise reduction). Speech perception results were compared to bench analyses comprising the phase inversion technique, employed to predict SNR improvement, and the Hearing-Aid Speech Perception Index (HASPI v2), utilized to predict speech intelligibility. Results The spheric speech in loud noise program improved speech perception by 20 to 32% points as compared to the calm situation program. This improvement might make it difficult for some patients to achieve the