ArticleThe journal of international advanced otology2023
Enlarged Vestibular Aqueduct and Associated Inner Ear Malformations: Hearing Loss Prognostic Factors and Data Modeling from an International Cohort.
Article in The journal of international advanced otology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Etiology-Driven Personalized Cochlear Implantation: Implications for Electrode Choice, Timing, and Outcomes.Journal of personalized medicine · 2026Review
- Prediction of Auditory Performance in Cochlear Implants Using Machine Learning Methods: A Systematic Review.Audiology research · 2025Review
- Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
ackgroundThere is a need to operationalize existing clinical data to support precision medicine in progressive hearing loss (HL). By utilizing enlarged vestibular aqueduct (EVA) and its associated inner ear abnormalities as an exemplar, we model data from a large international cohort, confirm prognostic factors for HL, and explore the potential to generate a prediction model to optimize current management paradigms.
methodsAn international retrospective cohort study. Regression analyses were utilized to model frequency-specific HL and identify prognostic factors for baseline average HL severity and progression. Elastic-net regression and machine learning (ML) techniques were utilized to predict future average HL progression based upon routinely measurable clinical, genetic, and radiological data.
resultsHigher frequencies of hearing were lost more severely. Prognostic factors for HL were the presence of incomplete partition type 2 (coefficient 12.95 dB, P=.011, 95% CI 3.0-22 dB) and presence of sac signal heterogeneity (P=.009, 95% CI 0.062-0.429) on magnetic resonance imaging. Elastic-net regression outperformed the ML algorithms (R2 0.32, mean absolute error 11.05 dB) with coefficients for baseline average hearing level and the presence of sac heterogeneity contributing the most to prediction outcomes.
conclusionIncomplete partition type 2 and endolymphatic sac signal heterogeneity phenotypes should be monitored closely for hearing deterioration and need for early audiological rehabilitation/cochlear implant. Preliminary prediction models have been generated using routinely collected health data in EVA. This study showcases how international collaborative research can use exemplar techniques to improve precision medicine in relatively rare disease entities.
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