Term-equivalent functional brain maturational measures predict neurodevelopmental outcomes in premature infants

Ters, Nathalie M. El; Vesoulis, Zachary A.; Liao, Steve M.; Smyser, Christopher D.; Mathur, Amit M.

doi:10.1016/j.earlhumdev.2018.02.010

Cited by 18 publications

(12 citation statements)

References 28 publications

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“…Afterward, the percentile score was obtained by the following formula: Subtest score = 100 x the number of items/the number of items to succeed with respect to 90%). [11][12][13] The 90%-reliability rate among the implementers and more than 85%-rate of yielding similar result for the repetitive measurements have been reported by Yalaz et al 10…”

Section: Denver Developmental Screening Test II (Ddst Ii)supporting

confidence: 85%

Developmental evaluation in children experiencing febrile convulsions

et al. 2021

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Background. The objective of this study was to determine the effect of febrile convulsion (FC) on neuromotor development.Methods. Data of 325 patients, who were followed up at our outpatient clinic and diagnosed with FC between January 2012 and December 2018, were retrospectively evaluated. Of these patients, 203 underwent the Denver Developmental Screening Test II (DDST II) and were included in the study as the patient group and 100 healthy children as the control group.Results. Of the study group, 84 (41.4%) were girls and 119 (58.6%) were boys (B/G: 1.4). Of all patients, 163 (80.3%) were diagnosed with simple FC, 22 (10.8%) with complicated FC, and 18 (8.9%) with FC+. There was no significant relationship found between FC subtypes and gender, family history of FC, family history of epilepsy, iron (Fe) deficiency, and Fe deficiency anemia. DDST II subtest points were significantly lower in all developmental areas in the patient group when compared to the controls (p<0.001), while suspected and abnormal test results were higher in all developmental areas in the patient group compared to the controls (p=0.01). It was also determined that the language points were lower as the age of first seizure increased (r=-0.319, p<0.01). Conclusions.Although FC is known to usually having a good prognosis, the low DDST II test results measured in this study indicated that the FC may pose a developmental risk and patients with FC should be followed up in terms of developmental features. Because of the retrospective nature of the study, there was no "preconvulsion" developmental evaluation. This is a major limitation of our study.

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Section: Denver Developmental Screening Test II (Ddst Ii)supporting

confidence: 85%

Developmental evaluation in children experiencing febrile convulsions

et al. 2021

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“…For example, for preterm infants <32 weeks of gestation, qEEG has been shown to mirror maturation (12–26) and has been used to identify and quantify a temporal evolution of the EEG after birth (27–30). Quantitative EEG has also been used to detect bursts within the EEG (23, 31–39), detect early brain injury (7), and to predict neurodevelopmental outcomes (29, 40–42). However, the real power of qEEG may lie in coupling the feature set with machine learning methods to develop specific classification and regression algorithms.…”

Section: Quantitative Eeg Analysismentioning

confidence: 99%

Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques

O’Toole

Boylan

2019

Front. Pediatr.

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Hemodynamic changes during neonatal transition increase the vulnerability of the preterm brain to injury. Real-time monitoring of brain function during this period would help identify the immediate impact of these changes on the brain. Neonatal EEG provides detailed real-time information about newborn brain function but can be difficult to interpret for non-experts; preterm neonatal EEG poses even greater challenges. An objective quantitative measure of preterm brain health would be invaluable during neonatal transition to help guide supportive care and ultimately protect the brain. Appropriate quantitative measures of preterm EEG must be calculated and care needs to be taken when applying the many techniques available for this task in the era of modern data science. This review provides valuable information about the factors that influence quantitative EEG analysis and describes the common pitfalls. Careful feature selection is required and attention must be paid to behavioral state given the variations encountered in newborn EEG during different states. Finally, the detrimental influence of artifacts on quantitative EEG analysis is illustrated.

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“…The complexity of outcome prediction might be even more pronounced for school-age compared to early infancy outcome and could explain the differing results of our study and previous reports. In that sense the repetition of aEEG recording at term equivalent age might be more informative in terms of long term prognosis of neurodevelopmental outcome as recently demonstrated by El Ters et al (34).…”

Section: Discussionmentioning

confidence: 85%

Association between development of early cortical activity on amplitude-integrated EEG and 5-years neurodevelopmental outcome in the preterm infant

Feldmann

Rousson

Nguyen

et al. 2019

Preprint

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Aim:To analyse the association between early aEEG and cognitive outcome at early schoolage in very preterm infants.Methods: Prospective cohort study including infants with gestational age (GA) <32.0 weeks, undergoing continuous aEEG recording during first 4 days of life. Semiquantitative and quantitative (maximum/minimum amplitude) measures were averaged over the recording period. Cognitive outcome was assessed with the Kaufman-Assessment Battery for Children at 5 years of age. Uni-and multivariate logistic regressions were calculated between aEEG parameters and normal cognitive outcome (IQ≥85). Results: Among 118 monitored preterm children, 89 were assessed at median(IQR) corrected age of 68.6 months (65.5-71.2) [48% female, median(IQR) GA 29.9(28.2,30.9) weeks, mean(SD) birth weight 1235(363) grams]. Mean(SD) IQ was 97.8(12.7). IQ<85 occurred in 21.3 %, cerebral palsy was found in 2.2%. Despite univariate associations of total maturity scores, cycling subscores, background pattern and minimum aEEG amplitude with normal cognitive outcome none of the associations remained significant after adjustment for confounders. Socioeconomic status was identified as independent predictor of neurodevelopmental outcome. Conclusion:In this cohort of very preterm infants, early short-term aEEG was not predictive of later cognitive outcome. Further research is needed to explore how aEEG could help to inform long-term prognosis in this population group. Key notes• Preterm born infants are at high risk for neurodevelopmental impairment.• Early amplitude integrated electroencephalography characteristics are univariately associated with cognitive outcome at 5 years of age in preterm born children.• However, socioeconomic factors and neonatal morbidity were stronger predictors of long-term neurodevelopmental outcome than early aEEG measures.

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Term-equivalent functional brain maturational measures predict neurodevelopmental outcomes in premature infants

Abstract: aEEG may be used at TEA as a new tool for risk stratification of infants at higher risk of poor neurodevelopmental outcomes. Therefore, a larger study is needed to validate these results in premature infants at low and high risk of brain injury.

Cited by 18 publications

References 28 publications

Developmental evaluation in children experiencing febrile convulsions

Developmental evaluation in children experiencing febrile convulsions

Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques

Association between development of early cortical activity on amplitude-integrated EEG and 5-years neurodevelopmental outcome in the preterm infant

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