Electrophysiological Correlates of Behavioral Comfort Levels in Cochlear Implantees: A Prospective Study

Abstract

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.

Keywords: Clinical Unit (CU); Cochlear Implant (CI); Electrically Evoked Auditory Brainstem Response (EABR); Electrically Evoked Stapedial Response Telemetry (ESRT); Evoked Compound Action Potential (ECAP); Impedance Telemetry (IT); Measurable Auditory Percept (MAP); Most Comfortable Level (M-Level); Neural Response Imaging (NRI).

Fig. 1

Trends in electrophysiological thresholds and M-levels over time

Fig. 2

Correlations of electrophysiological tests with M-levels over time

Fig. 3

Comparison of behavioral versus predicted M-levels in subject A at 1 month

Fig. 4

Comparison of behavioral versus predicted M-levels in subject A at 12 months