Automated assessment of EEG background for neurodevelopmental prediction in neonatal encephalopathy

Micheline Lagacé, Saeed Montazeri, Daphne Kamino, Eva Mamak, Linh G. Ly, Cecil D. Hahn, Vann Chau, Sampsa Vanhatalo, Emily W.Y. Tam

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

Abstrakti

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.

Alkuperäiskielienglanti
LehtiAnnals of Clinical and Translational Neurology
Vuosikerta11
Numero12
Sivut3267-3279
Sivumäärä13
ISSN2328-9503
DOI - pysyväislinkit
TilaJulkaistu - jouluk. 2024
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

Lisätietoja

Publisher Copyright:
© 2024 The Author(s). Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.

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