Article

EEG for Predicting Early Neurodevelopment in Preterm Infants: An Observational Cohort Study

Department of Pediatrics, Washington University in St Louis, 660 South Euclid Ave, St Louis, MO 63110. .
PEDIATRICS (Impact Factor: 5.3). 09/2012; 130(4):e891-7. DOI: 10.1542/peds.2012-1115
Source: PubMed

ABSTRACT To clarify the prognostic value of conventional EEG for the identification of preterm infants at risk for subsequent adverse neurodevelopment in the current perinatal care and medicine setting.
We studied 780 EEG records of 333 preterm infants born <34 weeks' gestation between 2002 and 2008. Serial EEG recordings were conducted during 3 time periods; at least once each within days 6 (first period), during days 7 to 19 (second period), and days 20 to 36 (third period). The presence and the grade of EEG background abnormalities were assessed according to an established classification system. Neurodevelopmental outcomes were assessed at a corrected age of 12 to 18 months.
Of the 333 infants, 33 (10%) had developmental delay and 34 (10%) had cerebral palsy. The presence of EEG abnormalities was significantly predictive of developmental delay and cerebral palsy at all 3 time periods: the first period (n = 265; odds ratio [OR], 4.5; 95% confidence interval [CI], 2.2-9.4), the second period (n = 278; OR, 7.6; 95% CI, 3.6-16), and the third period (n = 237; OR, 5.9; 95% CI, 2.8-13). The grade of EEG abnormalities correlated with the incidence of developmental delay or cerebral palsy in all periods (P < .001). After controlling for other clinical variables, including severe brain injury, EEG abnormality in the second period was an independent predictor of developmental delay (OR, 3.2; 95% CI, 1.1-9.7) and cerebral palsy (OR, 6.8; 95% CI 2.0-23).
EEG abnormalities within the first month of life significantly predict adverse neurodevelopment at a corrected age of 12 to 18 months in the current preterm survivor.

0 Followers
 · 
105 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Advances in obstetric and neonatal medical care and assisted reproductive technology have increased the rates of preterm birth, decreased preterm mortality rates, and lowered the limit of viability. However, morbidity in survivors, including neurodevelopmental disabilities, has increased, especially in extremely preterm infants born at ≤25 weeks' gestation. A better understanding of the prevalence and patterns of adverse neurodevelopmental outcomes in extremely preterm infants is important for patient care, counseling of families, and research.
    Pediatric Neurology 11/2014; 52(2). DOI:10.1016/j.pediatrneurol.2014.10.027 · 1.50 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Objective EEG is a valuable tool for evaluation of brain maturation in preterm babies. Preterm EEG constitutes of high voltage burst activities and more suppressed episodes, called interburst intervals (IBIs). Evolution of background characteristics provides information on brain maturation and helps in prediction of neurological outcome. The aim is to develop a method for automated burst detection. Methods Thirteen polysomnography recordings were used, collected at preterm postmenstrual age of 31.4 (26.1-34.4) weeks. We developed a burst detection algorithm based on the feature line length and compared it with manual scorings of clinical experts and other published methods. Results The line length–based algorithm is robust (84.27% accuracy, 84.00% sensitivity, 85.70% specificity). It is not critically dependent on the number of measurement channels, because two channels still provide 82% accuracy. Furthermore, it approximates well clinically relevant features, such as median IBI duration 5.45 (4.00-7.11) seconds, maximum IBI duration 14.02 (8.73-18.80) seconds and burst percentage 48.89 (35.45-60.12)%, with a median deviation of respectively 0.65s, 1.96s and 6.55%. Conclusion Automated assessment of long-term preterm EEG is possible and its use will optimize EEG interpretation in the NICU. Significance This study takes a first step towards fully automatic analysis of the preterm brain.
    Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 10/2014; DOI:10.1016/j.clinph.2014.02.015 · 2.98 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A key feature of normal neonatal EEG at term age is interhemispheric synchrony (IHS), which refers to the temporal co-incidence of bursting across hemispheres during trace alternant EEG activity. The assessment of IHS in both clinical and scientific work relies on visual, qualitative EEG assessment without clearly quantifiable definitions. A quantitative measure, activation synchrony index (ASI), was recently shown to perform well as compared to visual assessments. The present study was set out to test whether IHS is stable enough for clinical use, and whether it could be an objective feature of EEG normality. We analyzed 31 neonatal EEG recordings that had been clinically classified as normal (n = 14) or abnormal (n = 17) using holistic, conventional visual criteria including amplitude, focal differences, qualitative synchrony, and focal abnormalities. We selected 20-min epochs of discontinuous background pattern. ASI values were computed separately for different channel pair combinations and window lengths to define them for the optimal ASI intraindividual stability. Finally, ROC curves were computed to find trade-offs related to compromised data lengths, a common challenge in neonatal EEG studies. Using the average of four consecutive 2.5-min epochs in the centro-occipital bipolar derivations gave ASI estimates that very accurately distinguished babies clinically classified as normal vs. abnormal. It was even possible to draw a cut-off limit (ASI~3.6) which correctly classified the EEGs in 97% of all cases. Finally, we showed that compromising the length of EEG segments from 20 to 5 min leads to increased variability in ASI-based classification. Our findings support the prior literature that IHS is an important feature of normal neonatal brain function. We show that ASI may provide diagnostic value even at individual level, which strongly supports its use in prospective clinical studies on neonatal EEG as well as in the feature set of upcoming EEG classifiers.
    Frontiers in Human Neuroscience 01/2014; 8:1030. DOI:10.3389/fnhum.2014.01030 · 2.90 Impact Factor

Similar Publications