Development of a Novel Screening Tool for Predicting Cochlear Implant Candidacy

Objectives: 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.

Methods: 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.

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

Conclusion: 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.

Level Of Evidence: 4.