EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: A structured review
Introduction
Hypoxic ischaemic encephalopathy (HIE) occurs following a peripartum hypoxic–ischaemic insult to the brain. A secondary energy failure follows, with build-up of reactive metabolites and injury ensues (Volpe, 2008). In the term newborn, HIE occurs in 1 to 3 per 1000 live full-term births (Lai and Yang, 2011) and causes 23% of the 3.6 million neonatal deaths that occur globally (Lawn et al., 2010). The spectrum of long-term morbidity in survivors ranges from mild motor and cognitive deficits to cerebral palsy and severe cognitive deficits (Perlman and Shah, 2011). The outcomes of HIE or “birth asphyxia” can be devastating and permanent, making it a major burden for the patient, the family and society (Lai and Yang, 2011). The ability to accurately predict outcomes in this population is important in identifying those most appropriate to enrol in studies of neuroprotective or neurorestorative therapies.
Electroencephalography (EEG) is a useful method of assessment in asphyxiated infants. Conventional and amplitude integrated EEG (aEEG) both perform well in predicting outcome (van Laerhoven et al., 2013) and in the early diagnosis and classification of HIE severity (Walsh et al., 2011). The benefits of EEG are that it can easily be implemented at the cot-side soon after birth and it provides a real-time measure of cerebral function (Walsh et al., 2011). Detecting seizures (Navakatikyan et al., 2006, Bjorkman et al., 2010, Boashash et al., 2013) and assessing response to anticonvulsants (Hamelin et al., 2011) are other useful functions of EEG recording in the clinical setting, particularly because many seizures in the newborn have no detectable clinical manifestations. A normal EEG is highly predictive of a normal outcome, whereas various abnormal EEG features have been consistently associated with neurological abnormalities or death (Hamelin et al., 2011). We conducted a systematic review and meta-analysis of the literature to determine which specific background features of the EEG, in term neonates with HIE, best predict outcome.
Section snippets
Search strategy and inclusion and exclusion criteria
A literature search was conducted of the PubMed, EMBASE and CINAHL databases for studies published between January 1960 and April 2014. The search terms HIE or asphyxia, EEG, term newborn infants, prognosis or outcomes; and their derivatives were used to locate relevant studies. For PubMed, the specific search was: (((“Asphyxia Neonatorum” [Mesh] OR asphyx∗[tiab]) OR Hypoxic Ischemic Encephalopathy OR sarnat[tiab])) AND (((Term∗ OR Neonate OR newborn OR neonat∗) AND (Electroencephalo∗ OR
Results
Of the 860 articles generated by the initial search strategy, 566 studies remained when duplicates were removed. In the first stage, only 48 studies remained after hand-searching for the presence of reporting of EEG background features along with neurodevelopmental outcome in term neonates with HIE. A further 4 more were identified through hand-searching the references contained in the 48 studies. A total of 52 studies were finally identified. A further 21 studies were excluded because they did
Discussion
We have performed a meta-analysis to establish the prognostic value of different EEG background patterns in term neonates with HIE. To our knowledge this is the first review to analyse systematically different EEG techniques (CFM, aEEG, cEEG, international 10–20 EEG) and the background activity as a predictor of neurodevelopmental outcome. The meta-analysis clearly indicates that some features of EEG predict outcome accurately in term neonates with HIE.
One channel (CFM) or two channel (aEEG)
Conclusion
Background features of EEG accurately predict long term neurodevelopmental outcome in term neonates with HIE. These results have been generated in studies of term neonates and further studies are needed to determine the usefulness of EEG background patterns for prognostication of outcome in preterm infants.
The studies reviewed here are from different decades covering the change from analogue to digital EEG equipment. However, we are interested in the ability of EEG background patterns in term
Acknowledgements
Lars Eriksson and Nicola Foxlee, librarians at The University of Queensland Health Science Library provided assistance in defining the search strategy. Dr. Robert Ware, senior biostatistical consultant, School of Population Health, The University of Queensland provided statistical assistance. This work was supported in part by a Grant from the Qatar National Research Fund under its National Priorities Research Program award number NPRP 6-885-2-364.
Conflict of interest: None
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