Objectives: This study aims to evaluate the ability of (1) a novel amplitude-integrated electroencephalogram (aEEG) background evolution classification system; and (2) specific hour of life (HOL) cut points when observation of aEEG normalization and development of cycling can predict adverse neurological outcomes in infants with hypoxic-ischemic encephalopathy (HIE).
Study design: Continuous aEEG data of term neonates with HIE were reviewed for background pattern and aEEG cycling from start of monitoring through rewarming. Infants were classified by overall background evolution pattern. Adverse outcomes were defined as death or severe magnetic resonance imaging injury, as well as developmental outcomes in a subset of patients. aEEG characteristics were compared between outcome groups by multivariate regression models, likelihood ratios (LR), and receiver operating characteristic (ROC) curve analyses.
Results: Overall, 80 infants receiving therapeutic hypothermia met the inclusion criteria. Background evolution pattern seemed to distinguish outcome groups more reliably than background pattern at discrete intervals in time (LR: 43.9, p value < 0.001). Infants who did not reach discontinuous background by 15.5 HOL, cycling by 45.5 HOL, and normalization by 78 HOL were most likely to have adverse outcomes.
Conclusion: Evolution of aEEG in term neonates with HIE may be more useful for predicting outcome than evaluating aEEG at discrete intervals in time.
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