Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy
Abstract Objective Assess the capacity of brain state of the newborn (BSN) to predict neurodevelopment outcomes in neonatal encephalopathy. Methods Trends of BSN, a deep learning‐based measure translating EEG background to a continuous trend, were studied from a three‐channel montage long‐term EEG m...
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| Format: | Article |
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Wiley
2024-12-01
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| Series: | Annals of Clinical and Translational Neurology |
| Online Access: | https://doi.org/10.1002/acn3.52233 |
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| author | Micheline Lagacé Saeed Montazeri Daphne Kamino Eva Mamak Linh G. Ly Cecil D. Hahn Vann Chau Sampsa Vanhatalo Emily W. Y. Tam |
| author_facet | Micheline Lagacé Saeed Montazeri Daphne Kamino Eva Mamak Linh G. Ly Cecil D. Hahn Vann Chau Sampsa Vanhatalo Emily W. Y. Tam |
| author_sort | Micheline Lagacé |
| collection | DOAJ |
| description | Abstract Objective Assess the capacity of brain state of the newborn (BSN) to predict neurodevelopment outcomes in neonatal encephalopathy. Methods Trends of BSN, a deep learning‐based measure translating EEG background to a continuous trend, were studied from a three‐channel montage long‐term EEG monitoring from a prospective cohort of 92 infants with neonatal encephalopathy and neurodevelopmental outcomes assessed by Bayley Scales of Infant Development, 3rd edition (Bayley‐III) at 18 months. Outcome prediction used categories “Severe impairment” (Bayley‐III composite score ≤70 or death) or “Any impairment” (score ≤85 or death). Results “Severe impairment” was predicted best for motor outcomes (24 h area under the curve (AUC) = 0.97), followed by cognitive (36 h AUC = 0.90), overall (24 h AUC = 0.84), and language (24 h AUC = 0.82). “Any impairment” was best predicted for motor outcomes (12 h AUC = 0.95), followed by cognitive (24 h AUC = 0.85), overall (12 h AUC = 0.75), and language (12 and 24 h AUC = 0.68). Optimal BSN cutoffs for outcome predictions evolved with the postnatal age. Low BSN scores reached a 100% positive prediction of poor outcomes at 24 h of age. Interpretation BSN is an excellent predictor of adverse neurodevelopmental outcomes in survivors of neonatal encephalopathy after therapeutic hypothermia, even at 24 h of life. The trend provides a fully automated, objective, quantified, and reliable interpretation of EEG background. The high temporal resolution supports continuous bedside brain assessment and early prognostication during the initial dynamic recovery phase. |
| format | Article |
| id | doaj-art-2d794b31ef0c4c699ad9c39ebbbb98e3 |
| institution | Kabale University |
| issn | 2328-9503 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Annals of Clinical and Translational Neurology |
| spelling | doaj-art-2d794b31ef0c4c699ad9c39ebbbb98e32024-12-17T16:12:21ZengWileyAnnals of Clinical and Translational Neurology2328-95032024-12-0111123267327910.1002/acn3.52233Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathyMicheline Lagacé0Saeed Montazeri1Daphne Kamino2Eva Mamak3Linh G. Ly4Cecil D. Hahn5Vann Chau6Sampsa Vanhatalo7Emily W. Y. Tam8Faculty of Medicine, Clinician Investigator Program University of British Columbia Vancouver British Columbia CanadaBABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic Center University of Helsinki and Helsinki University Hospital Helsinki FinlandProgram in Neurosciences and Mental Health SickKids Research Institute Toronto Ontario CanadaDepartment of Psychology The Hospital for Sick Children Toronto Ontario CanadaProgram in Neurosciences and Mental Health SickKids Research Institute Toronto Ontario CanadaDivision of Neurology, Department of Paediatrics, The Hospital for Sick Children University of Toronto Toronto Ontario CanadaDivision of Neurology, Department of Paediatrics, The Hospital for Sick Children University of Toronto Toronto Ontario CanadaBABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic Center University of Helsinki and Helsinki University Hospital Helsinki FinlandDivision of Neurology, Department of Paediatrics, The Hospital for Sick Children University of Toronto Toronto Ontario CanadaAbstract Objective Assess the capacity of brain state of the newborn (BSN) to predict neurodevelopment outcomes in neonatal encephalopathy. Methods Trends of BSN, a deep learning‐based measure translating EEG background to a continuous trend, were studied from a three‐channel montage long‐term EEG monitoring from a prospective cohort of 92 infants with neonatal encephalopathy and neurodevelopmental outcomes assessed by Bayley Scales of Infant Development, 3rd edition (Bayley‐III) at 18 months. Outcome prediction used categories “Severe impairment” (Bayley‐III composite score ≤70 or death) or “Any impairment” (score ≤85 or death). Results “Severe impairment” was predicted best for motor outcomes (24 h area under the curve (AUC) = 0.97), followed by cognitive (36 h AUC = 0.90), overall (24 h AUC = 0.84), and language (24 h AUC = 0.82). “Any impairment” was best predicted for motor outcomes (12 h AUC = 0.95), followed by cognitive (24 h AUC = 0.85), overall (12 h AUC = 0.75), and language (12 and 24 h AUC = 0.68). Optimal BSN cutoffs for outcome predictions evolved with the postnatal age. Low BSN scores reached a 100% positive prediction of poor outcomes at 24 h of age. Interpretation BSN is an excellent predictor of adverse neurodevelopmental outcomes in survivors of neonatal encephalopathy after therapeutic hypothermia, even at 24 h of life. The trend provides a fully automated, objective, quantified, and reliable interpretation of EEG background. The high temporal resolution supports continuous bedside brain assessment and early prognostication during the initial dynamic recovery phase.https://doi.org/10.1002/acn3.52233 |
| spellingShingle | Micheline Lagacé Saeed Montazeri Daphne Kamino Eva Mamak Linh G. Ly Cecil D. Hahn Vann Chau Sampsa Vanhatalo Emily W. Y. Tam Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy Annals of Clinical and Translational Neurology |
| title | Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy |
| title_full | Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy |
| title_fullStr | Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy |
| title_full_unstemmed | Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy |
| title_short | Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy |
| title_sort | automated assessment of eeg background for neurodevelopmental prediction in neonatal encephalopathy |
| url | https://doi.org/10.1002/acn3.52233 |
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