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SUMMARY:Early qualitative and quantitative amplitude-integrated electroenc
 ephalogram and raw electroencephalogram for predicting long-term neurodeve
 lopmental outcomes in extremely preterm infants in the Netherlands: a 10-y
 ear cohort study - Professor Maria Luisa Tataranno (University Medical Cen
 ter Utrecht\, The Netherlands)
DTSTART;VALUE=DATE-TIME:20240514T150000
DTEND;VALUE=DATE-TIME:20240514T160000
UID:https://talks.ox.ac.uk/talks/id/802422ef-3abc-4722-9de0-8ec9fabf405d/
DESCRIPTION:Extremely preterm infants born before 28 weeks gestation are a
 t high risk for neurodevelopmental impairments. Amplitude-integrated EEG (
 aEEG) accompanied by raw EEG traces (aEEG-EEG) during the first days after
  birth could help predict outcomes in these infants. This study aimed to d
 etermine if specific qualitative and quantitative aEEG-EEG features predic
 t cognitive\, motor\, and behavioral outcomes at ages 2-3 and 5-7 years in
  extremely preterm infants.\n\nThis retrospective cohort study analyzed aE
 EG-EEG recordings from the first 3 days after birth for extremely preterm 
 infants born before 28 weeks gestation at Wilhelmina Children’s Hospital
 \, Netherlands between 2008-2018. Infants with genetic/metabolic diseases 
 or major malformations were excluded. Qualitative features were extracted\
 , including background pattern\, sleep-wake cycling\, and seizures. Quanti
 tative metrics were also extracted\, grouped into spectral content\, ampli
 tude\, connectivity\, and discontinuity. Machine learning models evaluated
  if these early aEEG-EEG features predicted outcomes at follow-up\, contro
 lling for potential confounders like illness severity and medications.\n\n
 Key findings showed background pattern was the strongest predictor. Infant
 s with discontinuous background patterns were more likely to have cognitiv
 e\, motor\, and behavioral problems at follow-up. Quantitative features al
 so had predictive value - increased discontinuity and decreased lower-freq
 uency activity predicted worse outcomes. Sleep-wake cycling and seizures o
 ccurred too infrequently to assess predictive utility.\n\nThis study found
  early aEEG-EEG background patterns and quantitative metrics in extremely 
 preterm infants provided valuable prognostic information about neurodevelo
 pmental impairments at ages 2-7 years. Discontinuous background and increa
 sed discontinuity specifically were associated with cognitive\, motor\, an
 d behavioral problems. These findings highlight the potential for automate
 d\, interpretable analysis of early aEEG-EEG features to aid risk stratifi
 cation\, decision-making\, and intervention planning for this high-risk po
 pulation. Future research should explore integrating these predictive EEG 
 biomarkers into an automated prognostic tool to enable individualized pred
 ictions and support precision care for extremely preterm infants.\nSpeaker
 s:\nProfessor Maria Luisa Tataranno (University Medical Center Utrecht\, T
 he Netherlands)
LOCATION:https://zoom.us/j/91092603798?pwd=NUxQVGZ0SzY4OUR1TzRDOW9SdGQ2dz0
 9
TZID:Europe/London
URL:https://talks.ox.ac.uk/talks/id/802422ef-3abc-4722-9de0-8ec9fabf405d/
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DESCRIPTION:Talk:Early qualitative and quantitative amplitude-integrated e
 lectroencephalogram and raw electroencephalogram for predicting long-term 
 neurodevelopmental outcomes in extremely preterm infants in the Netherland
 s: a 10-year cohort study - Professor Maria Luisa Tataranno (University Me
 dical Center Utrecht\, The Netherlands)
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