Elizabeth K Sewell1,2, Gilbert Vezina3,4, Taeun Chang5,6, Tammy Tsuchida5,6, Kari Harris5, Michelande Ridore1, Penny Glass2,7, An N Massaro1,2. 1. Division of Neonatology, Children's National Health Systems, Washington, District of Columbia. 2. Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia. 3. Division of Diagnostic Imaging and Radiology, Children's National Health Systems, Washington, District of Columbia. 4. Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia. 5. Division of Neurophysiology, Epilepsy and Critical Care, Children's National Health Systems, Washington, District of Columbia. 6. Department of Neurology, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia. 7. Division of Psychology and Behavioral Health, Children's National Health System, Washington, District of Columbia.
Abstract
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. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.
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. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.
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