AI Article Synopsis

  • Amplitude-integrated electroencephalogram (aEEG) is a tool for continuous monitoring of brain function in preterm infants and may help predict their neurodevelopmental outcomes.
  • A systematic review of 19 studies involving 2074 preterm neonates showed that specific aEEG patterns, such as burst suppression and low voltage backgrounds, are linked to poorer outcomes, while normal backgrounds predict better development.
  • Early monitoring of aEEG, particularly within the first week of life, is crucial for accurately assessing the neurodevelopmental prognosis of preterm infants.

Article Abstract

Objective: Amplitude-integrated electroencephalogram (aEEG) enables continuous and simplified bedside monitoring of brain function. This review aims to investigate aEEG's value as a predictor of neurodevelopment outcome in preterm infants.

Methods: PubMed, Embase and Web of Science were systematically searched according to the PRISMA in April 2023 and updated in October 2023. The protocol was registered on PROSPERO platform and the methodological quality of studies was analyzed using the Newcastle-Ottawa Scale (NOS).

Results: Nineteen articles (18 cohort and 1 case-control study) were included, reporting the recording of aEEG in 2074 preterm neonates and its association to neurodevelopment outcome. In most studies, aEEG recording started within 72 h of life. The mean NOS score for prospective and retrospective cohort studies were 6 (4-7, median 6) and 6.6 (6-7, median 7), respectively. The case-control study received 7 stars. Burst suppression and low voltage background were associated with poor neurodevelopmental outcome, while normal background and established cyclicity was correlated with a favorable outcome, especially when they occurred in the first week of life.

Conclusion: The background patterns and cyclicity of aEEG seems to be reliable patterns to help predict neurodevelopmental outcome in premature infants, especially when monitorization started early.

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Source
http://dx.doi.org/10.1016/j.braindev.2024.104315DOI Listing

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