Abstract
OBJECTIVE: (1) Identify major determinants of adverse neurodevelopmental outcome in extremely low birth weight (ELBW) infants. (2) Compare neural networks and regression analysis in the prediction of major handicaps and Bayley scores (MDI and PDI) in individual ELBW neonates followed to 18 months.
DESIGN: Retrospective cohort study of regional tertiary care NICU database. A database with 21 selected variables was divided into training (n=144) and test sets (n=74). The training set was used to train a neural network and develop regression equations to predict outcomes in the test set.
RESULTS: Determinants (descending order of contribution to variance): Major handicap: intraventricular hemorrhage (IVH) grade, necrotizing enterocolitis≥stage II, black race, and no chorioamnionitis; low MDI: IVH grade, plurality, bronchopulmonary dysplasia (BPD), lower maternal grade, and no chorioamnionitis; low PDI: IVH grade, BPD, periventricular leukomalacia, lower maternal grade, and no chorioamnionitis. Regression techniques and neural networks were comparable and had relatively low sensitivity and correlation with adverse outcomes.
CONCLUSIONS: Much of the variance in ELBW neurologic outcome cannot be explained by either regression analysis or neural network approaches.
Similar content being viewed by others
Author information
Authors and Affiliations
Additional information
Presented in part at the Society of Pediatric Research, New Orleans, LA, May 1998.
Rights and permissions
About this article
Cite this article
Ambalavanan, N., Nelson, K., Alexander, G. et al. Prediction of Neurologic Morbidity in Extremely Low Birth Weight Infants. J Perinatol 20, 496–503 (2000). https://doi.org/10.1038/sj.jp.7200419
Published:
Issue Date:
DOI: https://doi.org/10.1038/sj.jp.7200419
- Springer Nature America, Inc.
This article is cited by
-
Developing a practical neurodevelopmental prediction model for targeting high-risk very preterm infants during visit after NICU: a retrospective national longitudinal cohort study
BMC Medicine (2024)
-
The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review
npj Digital Medicine (2023)
-
Predicting the outcomes of preterm neonates beyond the neonatal intensive care unit: What are we missing?
Pediatric Research (2021)
-
Optimizing Growth and Neurocognitive Development While Minimalizing Metabolic Risk in Preterm Infants
Current Pediatrics Reports (2014)
-
Outcome of extremely low birth weight survivors at school age: the influence of perinatal parameters on neurodevelopment
European Journal of Pediatrics (2007)