Predictive models for cochlear implant outcomes: Performance, generalizability, and the impact of cohort size

• Published in: Trends in hearing Vol. 25; p. 23312165211066174
• Main Authors: Shafieibavani, Elaheh, Goudey, Benjamin, Kiral, Isabell, Zhong, Peter, Jimeno-Yepes, Antonio, Swan, Annalisa, Gambhir, Manoj, Buechner, Andreas, Kludt, Eugen, Eikelboom, Robert H., Sucher, Cathy, Gifford, Rene H., Rottier, Riaan, Plant, Kerrie, Anjomshoa, Hamideh
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.01.2021; Sage Publications Ltd
• Subjects: Adult; Cochlear Implantation - methods; Cochlear Implants; Deafness - diagnosis; Hearing Aids; Humans; Machine learning; Original; Retrospective Studies; Speech Perception; Transplants & implants; Treatment Outcome; cochlear implant; machine learning; predictive model
• ISSN: 2331-2165; 2331-2165
• DOI: 10.1177/23312165211066174

While cochlear implants have helped hundreds of thousands of individuals, it remains difficult to predict the extent to which an individual’s hearing will benefit from implantation. Several publications indicate that machine learning may improve predictive accuracy of cochlear implant outcomes compared to classical statistical methods. However, existing studies are limited in terms of model validation and evaluating factors like sample size on predictive performance. We conduct a thorough examination of machine learning approaches to predict word recognition scores (WRS) measured approximately 12 months after implantation in adults with post-lingual hearing loss. This is the largest retrospective study of cochlear implant outcomes to date, evaluating 2,489 cochlear implant recipients from three clinics. We demonstrate that while machine learning models significantly outperform linear models in prediction of WRS, their overall accuracy remains limited (mean absolute error: 17.9-21.8). The models are robust across clinical cohorts, with predictive error increasing by at most 16% when evaluated on a clinic excluded from the training set. We show that predictive improvement is unlikely to be improved by increasing sample size alone, with doubling of sample size estimated to only increasing performance by 3% on the combined dataset. Finally, we demonstrate how the current models could support clinical decision making, highlighting that subsets of individuals can be identified that have a 94% chance of improving WRS by at least 10% points after implantation, which is likely to be clinically meaningful. We discuss several implications of this analysis, focusing on the need to improve and standardize data collection.