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

Awal, Md. Abdul, Lai, Melissa M., Azemi, Ghasem, Boashash, Boualem and Colditz, Paul B. (2015) EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: a structured review. Clinical Neurophysiology, 127 1: 285-296. doi:10.1016/j.clinph.2015.05.018


Author Awal, Md. Abdul
Lai, Melissa M.
Azemi, Ghasem
Boashash, Boualem
Colditz, Paul B.
Title EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: a structured review
Journal name Clinical Neurophysiology   Check publisher's open access policy
ISSN 1872-8952
1388-2457
Publication date 2015-05-31
Sub-type Article (original research)
DOI 10.1016/j.clinph.2015.05.018
Open Access Status Not Open Access
Volume 127
Issue 1
Start page 285
End page 296
Total pages 12
Place of publication Shannon, Clare, Ireland
Publisher Elsevier Ireland
Collection year 2016
Language eng
Formatted 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.
Keyword EEG
Background features
Newborn
HIE
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article in press corrected proof.

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
Official 2016 Collection
School of Medicine Publications
 
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