Machine Learning Technique Reveals Prognostic Factors of Vibrant Soundbridge for Conductive or Mixed Hearing Loss Patients.

OBJECTIVES

Vibrant Soundbridge (VSB) was developed for treatment of hearing loss, but clinical outcomes vary and prognostic factors predicting the success of the treatment remain unknown. We examined clinical outcomes of VSB for conductive or mixed hearing loss, prognostic factors by analyzing prediction models, and cut-off values to predict the outcomes.

STUDY

DESIGN

Retrospective chart review.

SETTING

Tertiary care hospital.

PATIENTS: Thirty patients who underwent VSB surgery from January 2017 to December 2019 at our hospital.

INTERVENTION: Audiological tests were performed prior to and 3 months after surgery; patients completed questionnaires 3 months after surgery.

MAIN OUTCOME MEASURES: We used a multiregression and the random forest algorithm for predictions. Mean absolute errors and coefficient of determinations were calculated to estimate prediction accuracies. Coefficient values in the multiregression model and the importance of features in the random forest model were calculated to clarify prognostic factors. Receiver operation characteristic curves were plotted.

RESULTS

All audiological outcomes improved after surgery. The random forest model (mean absolute error: 0.06) recorded more accuracy than the multiregression model (mean absolute error: 0.12). Speech discrimination score in a silent context in patients with hearing aids was the most influential factor (coefficient value: 0.51, featured value: 0.71). The candidate cut-off value was 36% (sensitivity: 89%, specificity: 75%).

CONCLUSIONS

VSB is an effective treatment for conductive or mixed hearing loss. Machine learning demonstrated more precise predictions, and speech discrimination scores in a silent context in patients with hearing aids were the most important factor in predicting clinical outcomes.