Longitudinal Analysis of Amplitude-Integrated Electroencephalography for Outcome Prediction in Hypoxic-Ischemic Encephalopathy

• Main Authors: Andorka, C. (Author), Balogh, C.D (Author), Brandt, F.A (Author), Cseko, A.J (Author), Dobi, M. (Author), Jermendy, A. (Author), Kovacs, K. (Author), Meder, U. (Author), Szabo, A.J (Author), Szabo, M. (Author), Szakacs, L. (Author), Szakmar, E. (Author)
• Format: Article
• Language: English
• Published: Elsevier Inc. 2022
• Subjects: amplitude-integrated electroencephalography; asphyxia; brain function; newborn; score; seizure; sleep-wake cycling; therapeutic hypothermia
• Online Access: View Fulltext in Publisher

 Objective: To investigate the prognostic accuracy of longitudinal analysis of amplitude-integrated electroencephalography (aEEG) background activity to predict long-term neurodevelopmental outcome in neonates with hypoxic-ischemic encephalopathy (HIE) receiving therapeutic hypothermia. Study design: This single-center observational study included 149 neonates for derivation and 55 neonates for validation with moderate-severe HIE and of gestational age ≥35 weeks at a tertiary neonatal intensive care unit. Single-channel aEEG background pattern, sleep-wake cycling, and seizure activity were monitored over 84 hours during therapeutic hypothermia and rewarming, then scored for each 6-hour interval. Neurodevelopmental outcome was assessed using the Bayley Scales of Infant Development, Second Edition. Favorable outcome was defined as having both a Mental Development Index (MDI) score and Psychomotor Development Index (PDI) score ≥70, and adverse outcome was defined as either an MDI or a PDI <70 or death. Regression modeling for longitudinal analysis of repeatedly measured data was applied, and area under the receiver operating characteristic curve (AUC) was calculated. Results: Longitudinal aEEG background analysis combined with sleep-wake cycling score had excellent predictive value (AUC, 0.90; 95% CI, 0.85-0.95), better than single aEEG scores at any individual time point. The model performed well in the independent validation cohort (AUC, 0.87; 95% CI, 0.62-1.00). The reclassification rate of this model compared with the conventional analysis of aEEG background at 48 hours was 18% (24 patients); 14% (18 patients) were reclassified correctly. Our results were used to develop a user-friendly online outcome prediction tool. Conclusions: Longitudinal analysis of aEEG background activity and sleep-wake cycling is a valuable and accurate prognostic tool. © 2022 The Author(s)