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Predicting three-month and 12-month post-fitting real-world hearing-aid outcome using pre-fitting acceptable noise level (ANL)
Journal article   Open access   Peer reviewed

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

Yu-Hsiang Wu, Hsu-Chueh Ho, Shih-Hsuan Hsiao, Ryan B Brummet and Octav Chipara
International journal of audiology, Vol.55(5), pp.285-294
05/03/2016
DOI: 10.3109/14992027.2015.1120892
PMCID: PMC4823154
PMID: 26878163
url
https://www.ncbi.nlm.nih.gov/pmc/articles/4823154View
Open Access

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 (similar to 18 dB) than first-time hearing-aid users (similar to 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.
Audiology & Speech-Language Pathology Life Sciences & Biomedicine Otorhinolaryngology Science & Technology

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