EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: A structured review

Md Abdul Awal; Melissa M Lai; Ghasem Azemi; Boualem Boashash; Paul B Colditz

doi:10.1016/j.clinph.2015.05.018

EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: A structured review

Clin Neurophysiol. 2016 Jan;127(1):285-296. doi: 10.1016/j.clinph.2015.05.018. Epub 2015 May 31.

Authors

Md Abdul Awal¹, Melissa M Lai², Ghasem Azemi³, Boualem Boashash⁴, Paul B Colditz²

Affiliations

¹ The University of Queensland, UQ Centre for Clinical Research, Brisbane, Australia; The University of Queensland, Perinatal Research Centre, School of Medicine, Brisbane, Australia. Electronic address: m.awal@uq.edu.au.
² The University of Queensland, UQ Centre for Clinical Research, Brisbane, Australia; The University of Queensland, Perinatal Research Centre, School of Medicine, Brisbane, Australia.
³ Department of Electrical Engineering, Razi University, Kermanshah, Iran.
⁴ College of Engineering, Qatar University, Qatar; The University of Queensland, Perinatal Research Centre, School of Medicine, Brisbane, Australia; The University of Queensland, UQ Centre for Clinical Research, Brisbane, Australia.

PMID: 26105684
DOI: 10.1016/j.clinph.2015.05.018

Abstract

Objectives: Hypoxic ischaemic encephalopathy is a significant cause of mortality and morbidity in the term infant. Electroencephalography (EEG) is a useful tool in the assessment of newborns with HIE. This systematic review of published literature identifies those background features of EEG in term neonates with HIE that best predict neurodevelopmental outcome.

Methods: A literature search was conducted using the PubMed, EMBASE and CINAHL databases from January 1960 to April 2014. Studies included in the review described recorded EEG background features, neurodevelopmental outcomes at a minimum age of 12 months and were published in English. Pooled sensitivities and specificities of EEG background features were calculated and meta-analyses were performed for each background feature.

Results: Of the 860 articles generated by the initial search strategy, 52 studies were identified as potentially relevant. Twenty-one studies were excluded as they did not distinguish between different abnormal background features, leaving 31 studies from which data were extracted for the meta-analysis. The most promising neonatal EEG features are: burst suppression (sensitivity 0.87 [95% CI (0.78-0.92)]; specificity 0.82 [95% CI (0.72-0.88)]), low voltage (sensitivity 0.92 [95% CI (0.72-0.97)]; specificity 0.99 [95% CI (0.88-1.0)]), and flat trace (sensitivity 0.78 [95% CI (0.58-0.91)]; specificity 0.99 [95% CI (0.88-1.0)]).

Conclusion: Burst suppression, low voltage and flat trace in the EEG of term neonates with HIE most accurately predict long term neurodevelopmental outcome.

Significance: This structured review and meta-analysis provides quality evidence of the background EEG features that best predict neurodevelopmental outcome.

Keywords: Background features; EEG; HIE; Hypothermia; Meta-analysis; Neurodevelopmental outcome; Newborn; Structured review.

Publication types

Meta-Analysis
Research Support, Non-U.S. Gov't
Review
Systematic Review

MeSH terms

Electroencephalography / methods*
Humans
Hypoxia-Ischemia, Brain / diagnosis*
Hypoxia-Ischemia, Brain / physiopathology*
Hypoxia-Ischemia, Brain / therapy
Infant
Infant, Newborn
Predictive Value of Tests
Term Birth / physiology*
Treatment Outcome