EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: A structured review. Issue 1 (January 2016)

• Record Type: Journal Article
• Authors: Awal, Md. Abdul; Lai, Melissa M.; Azemi, Ghasem; Boashash, Boualem; Colditz, Paul B.
• Is Part Of: Clinical neurophysiology. Volume 127:Issue 1(2016:Jan.)
• Page Start: 285
• Page End: 296
• Publication Date: 2016-01
• Subjects: EEG -- Background features -- Newborn -- HIE -- Hypothermia -- Meta-analysis -- Structured review -- Neurodevelopmental outcome; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2015.05.018
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Classification of HIE is important as a selection criterion for hypothermia treatment. Background features of EEG predict neurodevelopmental outcome. Definitions of EEG background features needs agreement. 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.