Predicting three-month and 12-month post-fitting real-world hearing-aid outcome using pre-fitting acceptable noise level (ANL)

Abstract

Objective: Determine the extent to which pre-fitting acceptable noise level (ANL), with or without other predictors such as hearing-aid experience, can predict real-world hearing-aid outcomes at three and 12 months post-fitting. Design: ANLs were measured before hearing-aid fitting. Post-fitting outcome was assessed using the international outcome inventory for hearing aids (IOI-HA) and a hearing-aid use questionnaire. Models that predicted outcomes (successful vs. unsuccessful) were built using logistic regression and several machine learning algorithms, and were evaluated using the cross-validation technique. Study sample: A total of 132 adults with hearing impairment. Results: The prediction accuracy of the models ranged from 61% to 68% (IOI-HA) and from 55% to 61% (hearing-aid use questionnaire). The models performed more poorly in predicting 12-month than three-month outcomes. The ANL cutoff between successful and unsuccessful users was higher for experienced (∼18 dB) than first-time hearing-aid users (∼10 dB), indicating that most experienced users will be predicted as successful users regardless of their ANLs. Conclusions: Pre-fitting ANL is more useful in predicting short-term (three months) hearing-aid outcomes for first-time users, as measured by the IOI-HA. The prediction accuracy was lower than the accuracy reported by some previous research that used a cross-sectional design.