The Journal of International
Advanced Otology
Original Article

Enlarged Vestibular Aqueduct and Associated Inner Ear Malformations: Hearing Loss Prognostic Factors and Data Modeling from an International Cohort


Royal Manchester Children’s Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK


Division of Informatics, Imaging and Data Sciences, University of Manchester, University of Manchester, School of Biological Sciences, Manchester, UK


Department of Otorhinolaryngology, Head and Neck Surgery, and Audiology, Copenhagen Hearing and Balance Center (CHBC), Copenhagen University Hospital, Copenhagen, Denmark


University of Copenhagen, Faculty of Health and Medical Sciences, Copenhagen, Denmark


Department of Paediatric Otolaryngology, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK


The Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS Foundation Trust, London, UK


University College London Ear Institute, London, UK


Department of Paediatric Radiology, Royal Manchester Children’s Hospital, Manchester, UK


Manchester Centre for Genomic Medicine, Manchester Academic Health Sciences Centre, Manchester University NHS Foundation Trust, Manchester, UK


Department of Infection, Immunity and Respiratory Medicine, University of Manchester, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester, UK

J Int Adv Otol 2023; 19: 454-460
DOI: 10.5152/iao.2023.231044
Read: 1025 Downloads: 370 Published: 30 November 2023

BACKGROUND: There 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.

METHODS: An 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.

RESULTS: Higher 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.

CONCLUSION: Incomplete 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.

Cite this article as: Saeed HS, Fergie M, Mey K, et al. Enlarged vestibular aqueduct and associated inner ear malformations: Hearing loss prognostic factors and data modeling from an international cohort. J Int Adv Otol. 2023;19(6):454-460.

EISSN 2148-3817