Cochlear Implant Fitting Methods for Hearing Loss
(CALOS4 Trial)
Recruiting in Palo Alto (17 mi)
+1 other location
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Cochlear
Disqualifiers: Auditory neuropathy, Visual impairment, others
No Placebo Group
Trial Summary
What is the purpose of this trial?This study aims to collect data in newly implanted cochlear implant-recipients to inform future development of fitting methods to optimally and efficiently program a cochlear implant.
Will I have to stop taking my current medications?
The trial information does not specify whether you need to stop taking your current medications.
What data supports the effectiveness of the treatment methods used in the Cochlear Implant Fitting Methods for Hearing Loss trial?
Research shows that using personalized and optimized fitting methods for cochlear implants, which include objective data and machine-learning algorithms, can improve speech recognition and hearing outcomes. Additionally, studies indicate that combining behavioral and electrophysiological measures can help predict optimal comfort levels for cochlear implant users, leading to better hearing experiences.
12345How does the Categorical Loudness Scaling (CLS) Based Fitting treatment for cochlear implants differ from other treatments?
The Categorical Loudness Scaling (CLS) Based Fitting treatment for cochlear implants is unique because it uses a method to scale loudness perception across different sound levels, allowing for more personalized and accurate adjustments of the implant's settings. This approach focuses on achieving normal loudness perception by defining specific loudness categories, which can be adjusted for each electrode, providing a tailored hearing experience for the user.
13678Eligibility Criteria
This trial is for adults over 18 who became deaf after age 2 and now have a CI600 or CI500 series cochlear implant. Participants must understand the language used in speech tests and agree to follow the study rules.Inclusion Criteria
Unilaterally implanted with the CI600 Series (CI612, CI622, CI632) or CI500 series (CI512, CI522, CI532) cochlear implant
Willingness to participate in and comply with all requirements of the protocol
Willing and able to provide written informed consent
+3 more
Exclusion Criteria
Subject who will be programmed with an acoustic component in the implanted ear
Pure tone average (average of unaided thresholds at 0.5, 1, 2 and 4 kHz) less than or equal to 30 dB HL and aided word score of more than 80% in the contralateral ear
Score below 3 on the screening subset of questions from the Mobile Device Proficiency Questionnaire
+6 more
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
Treatment
Participants receive both Categorical Loudness Scaling Based Fitting and Behavioural Fitting with 4 weeks experience of both MAPS
4 weeks
Multiple visits for fitting and testing
Follow-up
Participants are monitored for performance outcomes and further refinement of fitting methods
4 weeks
Follow-up visits for outcome measures
Participant Groups
The study compares two ways of setting up cochlear implants: one uses behavioral fitting with Custom Sound Suite software, and the other uses loudness scaling with Nexus System. It's to find out which method works best post-activation.
1Treatment groups
Experimental Treatment
Group I: Participants receiving both Categorical Loudness Scaling Based Fitting and behavioural fitting.Experimental Treatment2 Interventions
Participants will receive a categorical loudness scaling based fitting (Interventional) and behavioural fitting with 4 weeks experience of both MAPS
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Cochlear AmericasLone Tree, CO
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Who Is Running the Clinical Trial?
CochlearLead Sponsor
QbD ClinicalIndustry Sponsor
QbD ClinicalCollaborator
AvaniaIndustry Sponsor
References
Development of Custom Sound® Pro software utilising big data and its clinical evaluation. [2023]Label="OBJECTIVES" NlmCategory="UNASSIGNED">To inform and optimise a cochlear implant (CI) fitting software design through an analysis of big data to define array-specific comfort (C) level profiles, frequently-used MAP parameters, and the minimum number of Neural Response Telemetry thresholds (tNRT) needed to create an accurate profile. To evaluate the software's ease of use and completion time for AutoNRT®s.
Towards personalized and optimized fitting of cochlear implants. [2023]A cochlear implant (CI) is a neurotechnological device that restores total sensorineural hearing loss. It contains a sophisticated speech processor that analyzes and transforms the acoustic input. It distributes its time-enveloped spectral content to the auditory nerve as electrical pulsed stimulation trains of selected frequency channels on a multi-contact electrode that is surgically inserted in the cochlear duct. This remarkable brain interface enables the deaf to regain hearing and understand speech. However, tuning of the large (>50) number of parameters of the speech processor, so-called "device fitting," is a tedious and complex process, which is mainly carried out in the clinic through 'one-size-fits-all' procedures. Current fitting typically relies on limited and often subjective data that must be collected in limited time. Despite the success of the CI as a hearing-restoration device, variability in speech-recognition scores among users is still very large, and mostly unexplained. The major factors that underly this variability incorporate three levels: (i) variability in auditory-system malfunction of CI-users, (ii) variability in the selectivity of electrode-to-auditory nerve (EL-AN) activation, and (iii) lack of objective perceptual measures to optimize the fitting. We argue that variability in speech recognition can only be alleviated by using objective patient-specific data for an individualized fitting procedure, which incorporates knowledge from all three levels. In this paper, we propose a series of experiments, aimed at collecting a large amount of objective (i.e., quantitative, reproducible, and reliable) data that characterize the three processing levels of the user's auditory system. Machine-learning algorithms that process these data will eventually enable the clinician to derive reliable and personalized characteristics of the user's auditory system, the quality of EL-AN signal transfer, and predictions of the perceptual effects of changes in the current fitting.
[Cochlear implant fitting strategies]. [2019]Cochlear implant (CI) fitting is the technical adjustment of a CI processor to the individual needs of a subject. Fitting comprises the choice of stimulation strategy and determination of the lower and upper stimulation levels for the individual. This electrical dynamic range defines the stimulation range for the real-time functioning of the CI system. All of the CI manufacturers provide a large set of parameters which have to be optimized for proper hearing and speech comprehension. As a consequence of the widening of indication criteria for CIs and lowering the age of implantation for children, the fitting process has changed dramatically over the years. This includes replacement of behavioral responses by other data from objective electrophysiologic measures and from expert knowledge. Recent developments aim to structure and automatize the CI fitting process. This reduces the time requirement for CI fitting and leads to fast settings which can be tested in the real-time situation. This review provides an overview of state-of-the-art fitting procedures and recent developments for fitting strategies. These will be discussed with respect to practicability and quality assurance.
Electrophysiological Correlates of Behavioral Comfort Levels in Cochlear Implantees: A Prospective Study. [2020]Indications for cochlear implantation have expanded today to include very young children and those with syndromes/multiple handicaps. Programming the implant based on behavioral responses may be tedious for audiologists in such cases, wherein matching an effective MAP and appropriate MAP becomes the key issue in the habilitation program. In 'Difficult to MAP' scenarios, objective measures become paramount to predict optimal current levels to be set in the MAP. We aimed, (a) to study the trends in multi-modal electrophysiological tests and behavioral responses sequentially over the first year of implant use, (b) to generate normative data from the above, (c) to correlate the multi-modal electrophysiological thresholds levels with behavioral comfort levels, and (d) to create predictive formulae for deriving optimal comfort levels (if unknown), using linear and multiple regression analysis. This prospective study included ten profoundly hearing impaired children aged between 2 and 7 years with normal inner ear anatomy and no additional handicaps. They received the Advanced Bionics HiRes 90K Implant with Harmony Speech processor and used HiRes-P with Fidelity 120 strategy. They underwent, Impedance Telemetry, Neural Response Imaging, Electrically Evoked Stapedial Response Telemetry and Electrically Evoked Auditory Brainstem Response tests at 1, 4, 8 and 12 months of implant use, in conjunction with behavioral Mapping. Trends in electrophysiological and behavioral responses were analyzed using paired t test. By Karl Pearson's correlation method, electrode-wise correlations were derived for NRI thresholds versus Most Comfortable Levels (M-Levels) and offset based (apical, mid-array and basal array) correlations for EABR and ESRT thresholds versus M-Levels were calculated over time. These were used to derive predictive formulae by linear and multiple regression analysis. Such statistically predicted M-Levels were compared with the behaviorally recorded M-Levels among the cohort, using Cronbach's Alpha Reliability test method for confirming the efficacy of this method. NRI, ESRT and EABR thresholds showed statistically significant positive correlations with behavioral M-Levels, which improved with implant use over time. These correlations were used to derive predicted M-Levels using regression analysis. Such predicted M-Levels were found to be in proximity to the actual behavioral M-Levels recorded among this cohort and proved to be statistically reliable. When clinically applied, this method was found to be successful among subjects of our study group. Although there existed disparities of a few clinical units, between the actual and predicted comfort levels among the subjects, this statistical method was able to provide a working MAP, close to the behavioral MAP used by these children. The results help to infer that behavioral measurements are mandatory to program cochlear implantees, but in cases where they are difficult to obtain, this study method may be used as reference for obtaining additional inputs, in order to set an optimal MAP. The study explores the trends and correlations between electrophysiological tests and behavioral responses, recorded over time among a cohort of cochlear implantees and provides a statistical method which may be used as a guideline to predict optimal behavioral levels in difficult situations among future implantees. In 'Difficult to MAP' scenarios, following a protocol of sequential behavioral programming, in conjunction with electrophysiological correlates will provide the best outcomes.
A clinical study of electrophysiological correlates of behavioural comfort levels in cochlear implantees. [2014]Indications for cochlear implantation have expanded today to include very young children and those with syndromes/multiple handicaps. Programming the implant based on behavioural responses may be tedious for audiologists in such cases, wherein matching an effective Measurable Auditory Percept (MAP) and appropriate MAP becomes the key issue in the habilitation program. In 'Difficult to MAP' scenarios, objective measures become paramount to predict optimal current levels to be set in the MAP. We aimed to (a) study the trends in multi-modal electrophysiological tests and behavioural responses sequentially over the first year of implant use; (b) generate normative data from the above; (c) correlate the multi-modal electrophysiological thresholds levels with behavioural comfort levels; and (d) create predictive formulae for deriving optimal comfort levels (if unknown), using linear and multiple regression analysis.
Indication for the need of flexible and frequency specific mapping functions in cochlear implant speech processors. [2018]Categorical loudness scaling of electric and acoustic stimuli was performed in cochlear implant (CI) recipients equipped with Nucleus systems in order to achieve a normal loudness perception in the whole dynamic range of acoustic input. For each electrode, the lower and upper limits of electric stimulus were defined by the values corresponding to "very soft" and "too loud". Within this dynamic range, the stimulus strength intervals associated to the verbal categories "soft", "medium", "loud" and "very loud" were determined. The same loudness categories were used for the scaling of acoustic stimuli. From both scaling experiments, the transduction of the CI system can be assessed and the parameters of the individual mapping function yielding a normal loudness growth can be derived. Deviations from optimum mapping can be corrected at least partially by manipulating the parameters of the mapping function. In many cases, however, one mapping function is not sufficient for all channels. The results argue in favour of the development of flexible and channel-specific mapping function parameters in future CI systems.
A different approach to using neural response telemetry for automated cochlear implant processor programming. [2019]This study explores the theoretical relation between the psychophysically measured current levels required for sound processor fitting in cochlear implants and the objectively measured compound action potential threshold (as measured by Neural Response Telemetry, NRT). The objective was to gain understanding of the variability across implantees in this relation and determine possible ways (using objective measures) of improving the predictability of NRT thresholds for behavioral levels needed for mapping.
Loudness growth in cochlear implants: effect of stimulation rate and electrode configuration. [2007]In cochlear implant speech processor design, acoustic amplitudes are mapped to electric currents with the intention of preserving loudness relationships across electrodes. Many parameters may affect the growth of loudness with electrical stimulation. The present study measured the effects of stimulation rate and electrode configuration on loudness growth in six Nucleus-22 cochlear implant users. Loudness balance functions were measured for stimuli that differed in terms of stimulation rate, electrode configuration and electrode location; a 2-alternative, forced-choice adaptive procedure (double-staircase) was used. First, subjects adaptively adjusted the amplitude of a 100-pulse-per-second (pps) pulse train to match the loudness of a 1000-pps standard pulse train. For a range of reference stimulation levels, the loudness of the 100-pps stimulus was matched to that of the 1000-pps standard stimulus; loudness balancing was performed for three electrode pairs [(20,22), (1,3), (1,22)]. The results showed that the loudness balance functions between the 100- and 1000-pps stimulation rates were highly subject-dependent. Some subjects' loudness balance functions were logarithmic, while others' were nearly linear. Loudness balance functions were also measured across electrode locations [(20,22) vs. (1,3)] for two stimulation rates (100, 1000 pps). Results showed that the loudness balance functions between the apical and basal electrode pairs highly depended on the stimulation rate. For all subjects, at the 1000-pps rate, the loudness balance functions between the two electrode locations were nearly linear; however, at the 100-pps rate, the loudness balance function was highly nonlinear in two out of six subjects. These results suggest that, for some cochlear implant patients, low-frequency stimulation may be processed differently at different electrode locations; for these patients, acoustic-to-electric amplitude mapping may need to be sensitive to this place-dependent processing when relatively low stimulation rates are used.