title: 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.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
authors.affiliation: a Department of Communication Sciences and Disorders , The University of Iowa , Iowa City , USA .
authors.forename: Yu-Hsiang
authors.initials: YH
authors.lastname: Wu
authors.affiliation: b Department of Otolaryngology , Buddhist Dalin Tzu-Chi General Hospital , Chiayi , Taiwan .|c School of Medicine, Tzu-Chi University , Hualien , Taiwan , and.
authors.forename: Hsu-Chueh
authors.initials: HC
authors.lastname: Ho
authors.affiliation: b Department of Otolaryngology , Buddhist Dalin Tzu-Chi General Hospital , Chiayi , Taiwan .|c School of Medicine, Tzu-Chi University , Hualien , Taiwan , and.
authors.forename: Shih-Hsuan
authors.initials: SH
authors.lastname: Hsiao
authors.affiliation: d Department of Computer Science , The University of Iowa , Iowa City , USA.
authors.forename: Ryan B
authors.initials: RB
authors.lastname: Brummet
authors.affiliation: d Department of Computer Science , The University of Iowa , Iowa City , USA.
authors.forename: Octav
authors.initials: O
authors.lastname: Chipara
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001309:Auditory Threshold; D012048:Correction of Hearing Impairment / Q000295:instrumentation*; D005260:Female; D006310:Hearing Aids / Q000706:statistics & numerical data*; D034381:Hearing Loss / Q000503:physiopathology* / Q000534:rehabilitation; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009622:Noise; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011237:Predictive Value of Tests; D017755:Prosthesis Fitting; D011795:Surveys and Questionnaires; D013997:Time Factors; D016896:Treatment Outcome
keywords: Acceptable noise level; hearing aid; international outcome inventory for hearing aids (IOI-HA); machine learning; outcome