INTRODUCTION: Only a small percentage (6-10%) of patients who are candidates receive cochlear implants (CIs). One potential reason contributing to low usage rates may be confusion regarding which patients to refer for CI evaluation. The extent to which information provided by standard clinical audiologic assessments is sufficient for selecting appropriate CI evaluation referrals is uncertain. The objective of this study is to evaluate the capacity of standard clinical audiologic measures to differentiate CI candidates from noncandidates. METHOD: The study design is a retrospective review of a prospectively maintained CI database from a university-based tertiary medical center of 518 patients undergoing CI evaluations from 2012 to 2020. Each ear of each patient was treated as an independent value. Receiver operating characteristic (ROCs) curves were constructed using aided AzBio sentence recognition scores in quiet and aided AzBio +10 dB signal-to-noise ratio scores <60% as binary classifiers for CI candidacy. For each ROC, we examined the capacity of multiple pure-tone thresholds, pure-tone average (PTA), and CNC word recognition scores (WRSs) measured under earphones to determine CI candidacy. Area under the curve ROC (AUC-ROC) values were calculated to demonstrate the capacity of each model to differentiate CI candidates from noncandidates. RESULTS: Variables with the greatest capacity to accurately differentiate CI candidates from noncandidates using aided AzBio in quiet scores were earphone CNC WRS, earphone pure-tone threshold at 1,000 Hz, and earphone PTA (AUC-ROC values = 0.86-0.88). Using aided AzBio +10 scores as the measure for candidacy, only CNC word recognition had a fair capacity to identify candidates (AUC-ROC value = 0.73). Based on the ROCs, a 1,000 Hz pure-tone threshold >50 dB HL, PTA >57 dB HL, and a monosyllabic WRS <60% can each serve as individual indicators for referral for CI evaluations. CONCLUSION: The current study provides initial indicators for referral and a first step at developing evidence-based criteria for CI evaluation referral using standard audiologic assessments.
INTRODUCTION: Only a small percentage (6-10%) of patients who are candidates receive cochlear implants (CIs). One potential reason contributing to low usage rates may be confusion regarding which patients to refer for CI evaluation. The extent to which information provided by standard clinical audiologic assessments is sufficient for selecting appropriate CI evaluation referrals is uncertain. The objective of this study is to evaluate the capacity of standard clinical audiologic measures to differentiate CI candidates from noncandidates. METHOD: The study design is a retrospective review of a prospectively maintained CI database from a university-based tertiary medical center of 518 patients undergoing CI evaluations from 2012 to 2020. Each ear of each patient was treated as an independent value. Receiver operating characteristic (ROCs) curves were constructed using aided AzBio sentence recognition scores in quiet and aided AzBio +10 dB signal-to-noise ratio scores <60% as binary classifiers for CI candidacy. For each ROC, we examined the capacity of multiple pure-tone thresholds, pure-tone average (PTA), and CNC word recognition scores (WRSs) measured under earphones to determine CI candidacy. Area under the curve ROC (AUC-ROC) values were calculated to demonstrate the capacity of each model to differentiate CI candidates from noncandidates. RESULTS: Variables with the greatest capacity to accurately differentiate CI candidates from noncandidates using aided AzBio in quiet scores were earphone CNC WRS, earphone pure-tone threshold at 1,000 Hz, and earphone PTA (AUC-ROC values = 0.86-0.88). Using aided AzBio +10 scores as the measure for candidacy, only CNC word recognition had a fair capacity to identify candidates (AUC-ROC value = 0.73). Based on the ROCs, a 1,000 Hz pure-tone threshold >50 dB HL, PTA >57 dB HL, and a monosyllabic WRS <60% can each serve as individual indicators for referral for CI evaluations. CONCLUSION: The current study provides initial indicators for referral and a first step at developing evidence-based criteria for CI evaluation referral using standard audiologic assessments.
Authors: Theodore R McRackan; Michael Bauschard; Jonathan L Hatch; Emily Franko-Tobin; Harris Richard Droghini; Craig A Velozo; Shaun A Nguyen; Judy R Dubno Journal: Otol Neurotol Date: 2018-01 Impact factor: 2.311
Authors: James M Gaylor; Gowri Raman; Mei Chung; Jounghee Lee; Madhumathi Rao; Joseph Lau; Dennis S Poe Journal: JAMA Otolaryngol Head Neck Surg Date: 2013-03 Impact factor: 6.223
Authors: Ji Eun Choi; Sung Hwa Hong; Jong Ho Won; Hee-Sung Park; Young Sang Cho; Won-Ho Chung; Yang-Sun Cho; Il Joon Moon Journal: Sci Rep Date: 2016-10-12 Impact factor: 4.379