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Background EEG features and prediction of cognitive outcomes in very preterm infants: A systematic review

Annice H T Kong1, Melissa M Lai1, Simon Finnigan1

  • 1The University of Queensland, UQ Centre for Clinical Research, Brisbane, Australia; The University of Queensland, Perinatal Research Centre, Faculty of Medicine, Brisbane, Australia.

Early Human Development
|October 20, 2018
PubMed

Insights

Electroencephalography (EEG) shows promise in predicting cognitive outcomes for very preterm infants. However, findings are too varied to pinpoint the most effective EEG features for early identification of at-risk infants.

Area of Science:

  • Neonatal neurology
  • Developmental neuroscience
  • Pediatric neurophysiology

Background:

  • Very preterm infants face significant risks of cognitive impairment.
  • Current methods for identifying at-risk infants are suboptimal.
  • Electroencephalography (EEG) is a tool for assessing brain development.

Purpose of the Study:

  • To review the relationship between EEG and cognitive outcomes in very preterm infants.
  • To evaluate the predictive capacity of EEG for cognitive impairment in this population.

Main Methods:

  • A systematic literature search was conducted across multiple databases (PubMed, CINAHL, PsycINFO, Cochrane, Embase, Web of Science).
  • Studies included very preterm infants (≤34 weeks gestational age) with EEG assessments (≤43 weeks postmenstrual age) and cognitive outcomes (≥3 months).
  • Meta-analysis was performed on studies with comparable EEG features to determine pooled sensitivity and specificity.

Main Results:

  • 31 studies involving 4712 infants met the inclusion criteria, revealing heterogeneity in EEG methods and follow-up.
  • Meta-analysis was feasible for only one category: dysmature/disorganized EEG patterns.
  • Pooled sensitivity was 0.63 (95% CI: 0.53-0.72) and specificity was 0.83 (95% CI: 0.74-0.89) for predicting cognitive outcomes using these patterns.

Conclusions:

  • Background EEG features show preliminary potential for predicting cognitive outcomes in very preterm infants.
  • The heterogeneity of findings prevents definitive conclusions on the optimal EEG predictors.
  • Further research is needed to standardize EEG analysis and identify the most reliable predictive features.
Abstract

Keywords:
Cognitive outcomeEEGMeta-analysisPreterm infantsSystematic review

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