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Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
Published on: August 25, 2014
Prediction of neurologic morbidity in extremely low birth weight infants
N Ambalavanan1, K G Nelson, G Alexander
1Division of Neonatology, Department of Pediatrics, University of Alabama at Birmingham, 525 New Hillman Building, Birmingham, AL 35233-7335, USA.
Insights
Predicting neurodevelopmental outcomes in extremely low birth weight (ELBW) infants remains challenging. Key determinants like intraventricular hemorrhage and bronchopulmonary dysplasia were identified, but predictive models showed limited accuracy.
Area of Science:
- Neonatal neurology
- Developmental pediatrics
- Biostatistics
Background:
- Extremely low birth weight (ELBW) infants face significant risks for adverse neurodevelopmental outcomes.
- Accurate prediction of these outcomes is crucial for timely intervention and improved long-term prognosis.
Purpose of the Study:
- To identify key determinants of major handicaps and cognitive/motor impairments in ELBW infants.
- To compare the predictive performance of neural networks and regression analysis for these outcomes.
Main Methods:
- Retrospective cohort study utilizing a regional tertiary care NICU database.
- A dataset of 21 variables was divided into training (n=144) and testing (n=74) sets.
- Neural networks and regression models were trained to predict outcomes in the test set.
Main Results:
- Major determinants for adverse outcomes included intraventricular hemorrhage (IVH) grade, necrotizing enterocolitis, race, bronchopulmonary dysplasia (BPD), and periventricular leukomalacia.
- Both neural networks and regression analysis demonstrated comparable, yet limited, sensitivity and correlation with adverse outcomes.
- Significant variance in neurodevelopmental outcomes remained unexplained by the models.
Conclusions:
- Current predictive models, including neural networks and regression, explain only a portion of the variance in neurodevelopmental outcomes for ELBW infants.
- Further research is needed to identify additional contributing factors and improve predictive accuracy.
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.
Study 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 > or = 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.
Conclusion:
Much of the variance in ELBW neurologic outcome cannot be explained by either regression analysis or neural network approaches.
