Effects of Preoperative Factors on the Learning Curves of Postlingual Cochlear Implant Recipients.

Abstract

Objectives: The substantial variability in speech perception outcomes after cochlear implantation complicates efforts to develop valid predictive models of these outcomes. Existing predictive regression models are too unreliable for clinical application, possibly because speech intelligibility (SI) after cochlear implant (CI) rehabilitation is often based on a limited number of assessments. The development of SI after CI has rarely been detailed, although knowing the shape of the learning curve can potentially improve predictive modeling. Knowing the learning curve after CI could also aid in setting expectations about SI immediately after implantation, and the duration of rehabilitation. The current objectives were to construct learning curves to estimate baseline SI at 1 week (B), maximal SI after rehabilitation (M), and rehabilitation time (time to reach 80% of the learning effect; t[M - B]80%), and to subsequently deploy these outcomes for multiple-regression modeling to predict CI outcomes.

Design: To assess rehabilitation after cochlear implantation, we retrospectively fitted learning curves using clinically available SI assessments from 533 postlingually deaf, unilaterally implanted adults. SI was assessed with consonant-vowel-consonant words (CVC) in quiet, with phoneme score as the outcome measure. Participants were followed for up to 4 years, with SI measurements collected at fixed intervals. SI was commonly assessed 1, 2, 4, and 8 weeks after device activation. B, M, and t(M - B)80% were determined from the fitted learning curves. Predictive multiple-regression analyses were performed on these three outcome measures based on eight previously identified preoperative demographic and audiometric predictor variables: age at implantation, duration of severe-to-profound hearing loss, best-aided CVC phoneme score (in the free field), unaided ipsilateral and contralateral residual hearing and CVC phoneme scores (measured with headphones), and education type (regular or special education).

Results: At 1 week after CI activation, raw phoneme scores had increased from 40% preoperatively (best-aided condition) to 51%, with further improvement to approximately 78% at 4 years. SI increased significantly until 1 year after activation and then plateaued. Fitted learning curves supported better estimates of these parameters, showing that average baseline SI at 1 week after CI activation was 51%, increasing to 85% after rehabilitation. The asymptotic score exceeded the raw average after 4 years because many cases had not yet plateaued. The median t(M - B)80% was 1.5 months. Predictive modeling identified duration of hearing loss, age at implantation, best-aided CVC phoneme score, and education type as the most robust predictors for postoperative SI. Despite the statistically significant correlations, however, the combined predictive value was ~19% for B, 10% for M, and 2% for t(M - B)80%.

Conclusions: This study is among the few to generate detailed learning curves after cochlear implantation. By including clinical SI measures in the earliest rehabilitation period, we report a median rehabilitation time with CI of 1.5 months. This implied rapid learning effect emphasizes the value of monitoring SI in the first few weeks after rehabilitation. According to multiple-regression analyses, the most commonly used preoperative variables correlated significantly with postoperative outcomes, but with limited predictive value for the clinic. By fitting learning curves through data reported in the literature, we show that the increase in SI during rehabilitation is an important predictor for t(M - B)80%.