Using Machine Learning and Multifaceted Preoperative Measures to Predict Adult Cochlear Implant Outcomes: A Prospective Pilot Study

Abstract

Objectives: To use machine learning and a battery of measures for preoperative prediction of speech recognition and quality of life (QOL) outcomes after cochlear implant (CI) surgery. Design: Demographic, audiologic, cognitive-linguistic, and QOL predictors were collected from 30 postlingually deaf adults before CI surgery. K-means clustering separated patients into groups. Reliable change index scores were computed for speech recognition and QOL from pre-CI to 6 months post-CI, and group differences were determined. Results: Clustering yielded three groups with differences in reliable change index for sentence recognition. One group demonstrated low baseline sentence recognition and only small improvements post-CI, suggesting a group “at risk” for limited benefits. This group showed lower pre-CI scores on verbal learning and memory and lack of musical training. Conclusions: Preoperative assessments can prognosticate CI recipients’ postoperative performance and identify individuals at risk for experiencing poor sentence recognition outcomes, which may help guide counseling and rehabilitation.