Development of a novel screening tool for predicting Cochlear implant candidacy

Cochlear implantation (CI) is a well-established treatment for sensorineural hearing loss. Due in part to a lack of referral guidelines, CI technology remains underutilized, and many patients who could benefit from CI may not be referred for evaluation. This study aimed to develop a model for predicting CI candidacy using routine audiometric measures, with the goal of providing guidance to clinicians regarding when to refer a patient for CI evaluation.

Unaided three-frequency pure tone average (PTA), unaided speech discrimination score (SDS), and best-aided sentence recognition testing with AZBio sentence lists were collected from 252 subjects undergoing CIE. Candidacy was defined by meeting traditional (AZBio score ≤ 60%), or Medicare criteria (≤40%). A logistic regression model was developed to predict candidacy. Confusion matrices were plotted to determine the sensitivity and specificity at various probability thresholds.

Logistic regression models were capable of predicting probability of candidacy for traditional criteria (P < .001) and Medicare criteria (P < .001). PTA and SDS were significant predictors (P < .001). Using a probability cutoff of .5, the models yielded a sensitivity rate of 91% and 78% for traditional and Medicare criteria, respectively.

Probability of CI candidacy may be determined using a novel screening tool for referral. This tool supports individualized counseling, serves as a proof of concept for candidacy prediction, and could be modified based on an institution's philosophy regarding an acceptable false positive rate of referral.

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