Abstract

Given our aging society and the prevalence of age-related hearing loss that often develops during adulthood, hearing loss is a common public health issue affecting almost all older adults. Moderate-to-moderately severe hearing loss can usually be corrected with hearing aids; however, severe-to-profound hearing loss often requires a cochlear implant (CI). However, post-operative CI results vary, and the performance of the previous prediction models is limited, indicating that a new approach is needed. For postlingually deaf adults (n de120) who received CI with full insertion, we predicted CI outcomes using a Random-Forest Regression (RFR) model and investigated the effect of preoperative factors on CI outcomes. Postoperative word recognition scores (WRS) served as the dependent variable to predict. Predictors included duration of deafness (DoD), age at CI operation (ageCI), duration of hearing-aid use (DoHA), preoperative hearing threshold and sentence recognition score. Prediction accuracy was evaluated using mean absolute error (MAE) and Pearson’s correlation coefficient r between the true WRS and predicted WRS. The fitting using a linear model resulted in prediction of WRS with r = 0.7 and MAE = 15.6 ± 9. RFR outperformed the linear model (r = 0.96, MAE = 6.1 ± 4.7, p < 0.00001). Cross-hospital data validation showed reliable performance using RFR (r = 0.91, MAE = 9.6 ± 5.2). The contribution of DoD to prediction was the highest (MAE increase when omitted: 14.8), followed by ageCI (8.9) and DoHA (7.5). After CI, patients with DoD < 10 years presented better WRSs and smaller variations (p < 0.01) than those with longer DoD. Better WRS was also explained by younger age at CI and longer-term DoHA. Machine learning demonstrated a robust prediction performance for CI outcomes in postlingually deaf adults across different institutes, providing a reference value for counseling patients considering CI. Health care providers should be aware that the patients with severe-to-profound hearing loss who cannot have benefit from hearing aids need to proceed with CI as soon as possible and should continue using hearing aids until after CI operation.

Highlights

  • Given our aging society and the prevalence of age-related hearing loss that often develops during adulthood, hearing loss is a common public health issue affecting almost all older adults

  • Health care providers should be aware that the patients with severeto-profound hearing loss who cannot have benefit from hearing aids need to proceed with cochlear implant (CI) as soon as possible and should continue using hearing aids until after CI operation

  • Some studies reported a negative relationship between duration of deafness (DoD) and postoperative speech and its greater role when combined with residual hearing[5,6,8,9,10], whereas others reported that DoD might have no or even a positive relationship with speech recognition[4,7]

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Summary

Introduction

Given our aging society and the prevalence of age-related hearing loss that often develops during adulthood, hearing loss is a common public health issue affecting almost all older adults. Predictors included duration of deafness (DoD), age at CI operation (ageCI), duration of hearing-aid use (DoHA), preoperative hearing threshold and sentence recognition score. Many factors including age at CI operation, duration of hearing loss, the presence of residual hearing, previous hearing aid use, and the presence of cochlear anomaly are considered to be related to the outcomes. We proposed to build predictive models of postCI outcomes by combining common preoperative variables with multivariate regression modeling using a nonlinear machine learning approach as well as a general linear model. Such machine-learning approaches have been successfully used to improve disease diagnosis or predictions across various conditions[18,19,20]. We validated the fitted model using data from two other independent hospitals and investigated the effect of preoperative factors on CI outcomes

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