Cochlear Implant Candidacy Support Tool Using Conjunctive Consolidation

Importance Despite the proven benefits of a cochlear implant, utilization rates remain low. Current screening tools have improved awareness but rely on binary classification (candidate vs noncandidate), limiting individualized counseling and shared decision-making. Objective To develop a risk stratification system for cochlear implant candidacy based on routine audiometric data, enabling individualized estimates of cochlear implant candidacy likelihood, supporting improved shared decision-making Design, Setting, and Participants This retrospective cohort study including adults with hearing loss was conducted at a single tertiary academic center. Methods Consonant-nucleus-consonant (CNC) scores of 50% or lower were used as candidacy criteria. A conjunctive consolidation approach was used to classify patients into 4 audiometric severity stages, combining pure tone average (PTA) and word recognition score (WRS) cutoffs. Groups were informed by clinical judgment and statistical isometry. Discriminative power was assessed using the C statistic. A secondary stratification system was developed using AzBio sentences (≤60% in quiet or +10 dB on signal-to-noise ratio examination) to define candidacy. Results Among 1312 patients with complete data and PTA below 100 dB, 782 (59.6%) met cochlear implant candidacy criteria based on CNC scores of 50% or lower. The 4-stage classification system showed a clear gradient of candidacy likelihood, ranging from 2.8% in stage 0 to 88.5% in stage 3, with strong discriminative power (C = 0.83; 95% CI, 0.81-0.85). Similar trends were observed when candidacy was defined by AzBio scores, with strong model discrimination (C = 0.80; 95% CI, 0.77-0.83). Demographic factors such as age and duration of hearing loss did not enhance model performance and were excluded. Conclusion This cohort study found that patients with hearing loss can be effectively stratified by likelihood of cochlear implant candidacy using routine audiometric data. This 4-level classification system offers a simple, clinically intuitive method to estimate candidacy probability, moving beyond binary screening and supporting personalized, data-driven decision-making between clinicians and patients.