Clinical application of a multimodal electrophysiological test battery to predict optimal behavioral levels in cochlear implantees

Abstract

Objectives: Indications for cochlear implantation have expanded to include very young children and those with syndromes or multiple handicaps. In such cases programming the implant based on behavioral responses may be tedious, wherein matching effective and appropriate measurable auditory percepts (maps) and becomes the key issue in the rehabilitation program. In 'difficult to map' scenarios, objective measures become paramount for predicting 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 formulas for deriving optimal comfort levels (if unknown), using linear and multiple regression analysis. Materials and methods: This prospective study included 10 profoundly hearing impaired children aged 2 to 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 the 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 analysed using paired t-tests. Using Pearsons correlation method, electrode-wise correlations were derived for NRI thresholds versus 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. Results: 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 close to the actual behavioral M-levels recorded among this cohort and proved to be statistically reliable. Conclusions: The study has explored the trends and correlations between electrophysiological tests and behavioral responses, recorded over time among a cohort of cochlear implantees. It 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, the best outcomes will come from following a protocol of sequential behavioral programming in conjunction with electrophysiological correlates.