Genotype-Phenotype Correlation for Predicting Cochlear Implant Outcome: Current Challenges and Opportunities
- PMID: 32765579
- PMCID: PMC7381205
- DOI: 10.3389/fgene.2020.00678
Genotype-Phenotype Correlation for Predicting Cochlear Implant Outcome: Current Challenges and Opportunities
Abstract
The use and utility of cochlear implantation has rapidly increased in recent years as technological advances in the field have expanded both the efficacy and eligible patient population for implantation. This review aims to serve as a general overview of the most common hearing disorders that have favorable auditory outcomes with cochlear implants (CI). Hearing loss in children caused by congenital cytomegalovirus infection, syndromic conditions including Pendred Syndrome, and non-syndromic genetic conditions such as hearing impairment associated with GJB2 mutations have shown to be successfully managed by CI. Furthermore, cochlear implantation provides the auditory rehabilitation for the most common etiology of hearing loss in adults and age-related hearing loss (ARHL) or presbycusis. However, in some cases, cochlear implantation have been associated with some challenges. Regarding implantation in children, studies have shown that sometimes parents seem to have unrealistic expectations regarding the ability of CI to provide auditory rehabilitation and speech improvement. Given the evidence revealing the beneficial effects of early intervention via CI in individuals with hearing disorders especially hearing loss due to genetic etiology, early auditory and genetic screening efforts may yield better clinical outcomes. There is a need to better understand genotype-phenotype correlations and CI outcome, so that effective genetic counseling and successful treatment strategies can be developed at the appropriate time for hearing impaired individuals.
Keywords: age-related hearing loss; cochlear implant; genetic etiology; genotye–phenotype correlation; hearing loss.
Copyright © 2020 Eshraghi, Polineni, Davies, Shahal, Mittal, Al-Zaghal, Sinha, Jindal and Mittal.
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