A predictive model of cochlear implant performance in postlingually deafened adults.

OBJECTIVE

To develop a predictive model of cochlear implant (CI) performance in postlingually deafened adults that includes contemporary speech perception testing and the hearing history of both ears.

STUDY DESIGN

Retrospective clinical study. Multivariate predictors of speech perception after CI surgery included duration of any degree of hearing loss (HL), duration of severe-to-profound HL, age at implantation, and preoperative Hearing in Noise Test (HINT) sentences in quiet and HINT sentences in noise scores. Consonant-nucleus-consonant (CNC) scores served as the dependent variable. To develop the model, we performed a stepwise multiple regression analysis.

SETTING

Tertiary referral center.

PATIENTS

Adult patients with postlingual severe-to-profound HL who received a multichannel CI. Mean follow-up was 28 months. Fifty-five patients were included in the initial bivariate analysis.

INTERVENTION(S): Multichannel cochlear implantation.

MAIN OUTCOME MEASURES(S): Predicted and measured postoperative CNC scores.

RESULTS

The regression analysis resulted in a model that accounted for 60% of the variance in postoperative CNC scores. The formula is (pred)CNC score = 76.05 + (-0.08 x DurHL(CI ear)) + (0.38 x pre-HINT sentences in quiet) + (0.04 x long sev-prof HL(either ear)). Duration of HL was in months. The mean difference between predicted and measured postoperative CNC scores was 1.7 percentage points (SD, 16.3).

CONCLUSION

The University of Massachusetts CI formula uses HINT sentence scores and the hearing history of both ears to predict the variance in postoperative monosyllabic word scores. This model compares favorably with previous studies that relied on Central Institute for the Deaf sentence scores and uses patient data collected by most centers in the United States.