Risk factors associated with late-onset hearing loss in children with congenital cytomegalovirus: a systematic review

María Fernández-Rueda1, Christian Calvo-Henriquez2, Rafael Fernández-Liesa3

  • 1Department of Otorhinolaryngology-Head and Neck Surgery, Hospital Universitario 12 Octubre, Avenida Córdoba S/N, 28041, Madrid, Spain. mfrueda29@gmail.com.

Insights

This review identified six significant risk factors for late-onset hearing loss in infants with congenital cytomegalovirus (cCMV). However, current evidence is limited, highlighting the need for more research on predicting cCMV-related hearing loss.

Area of Science:

  • Neonatal Medicine
  • Virology
  • Audiology

Background:

  • Congenital cytomegalovirus (cCMV) is a leading cause of non-genetic sensorineural hearing loss in infants.
  • Late-onset hearing loss (LOHL) can manifest after the neonatal period, posing diagnostic challenges.
  • Identifying prognostic factors for LOHL in cCMV is crucial for timely intervention.

Purpose of the Study:

  • To systematically review existing evidence on prognostic factors associated with LOHL development in infants with cCMV.
  • To synthesize findings on risk factors from published literature.

Main Methods:

  • A PRISMA systematic review was conducted.
  • Searches were performed in PubMed, Embase, and Web of Science databases up to December 2023.
  • Nine studies involving 292 children with LOHL were included.

Main Results:

  • Twelve risk factors for LOHL in cCMV were identified, with six found to be statistically significant.
  • Symptomatic cCMV, elevated DNAemia, and salivary viral load in asymptomatic cases were associated with LOHL.
  • First-trimester seroconversion, gestational age < 37 weeks, and low birth weight correlated with LOHL.

Conclusions:

  • While significant risk factors were identified, the evidence is limited and inconsistent.
  • Reliable prediction of LOHL in cCMV patients using neonatal and maternal parameters is currently not feasible.
  • Further high-quality studies with consistent designs are needed to improve prediction models.
Abstract