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Updated: May 21, 2026
Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
Published on: August 25, 2014
Outcome trajectories in extremely preterm infants
Namasivayam Ambalavanan1, Waldemar A Carlo, Jon E Tyson
1Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama 35249, USA. ambal@uab.edu
Insights
Predicting outcomes for extremely premature infants can be improved by updating prognostic factors throughout their NICU stay. This dynamic approach refines predictions of death or impairment over time.
Area of Science:
- Neonatalogy and Perinatal Medicine
- Clinical Prediction Modeling
- Biostatistics
Background:
- Accurate prognosis for extremely premature neonates is crucial for clinical decision-making.
- Initial predictions at birth use factors like gestational age and birth weight.
- Improved predictive models are needed that incorporate evolving clinical data.
Purpose of the Study:
- To develop and validate serial prediction models for infant outcomes.
- To assess the utility of information gathered later in the NICU course.
- To improve predictions of death or neurodevelopmental impairment.
Main Methods:
- Multivariable regression models were developed using data from extremely premature infants (birth weight ≤ 1.0 kg).
- Models were created at multiple time points during NICU hospitalization (delivery room, 7, 28 days, 36-week postmenstrual age).
- Predictions of death or death/neurodevelopmental impairment at 18-22 months were the primary outcomes.
Main Results:
- Prediction accuracy improved with later-available clinical information.
- The influence of birth weight decreased, while respiratory illness severity became more important postnatally.
- Validation models showed good performance with c-statistics ranging from 0.74 to 0.80.
Conclusions:
- Dynamic, serial outcome prediction models enhance prognostic accuracy in preterm infants.
- These models allow for individualized "outcome trajectories" based on evolving clinical data.
- The impact of potential morbidities on outcomes can be evaluated dynamically.
Objective:
Methods are required to predict prognosis with changes in clinical course. Death or neurodevelopmental impairment in extremely premature neonates can be predicted at birth/admission to the ICU by considering gender, antenatal steroids, multiple birth, birth weight, and gestational age. Predictions may be improved by using additional information available later during the clinical course. Our objective was to develop serial predictions of outcome by using prognostic factors available over the course of NICU hospitalization.
Methods:
Data on infants with birth weight ≤ 1.0 kg admitted to 18 large academic tertiary NICUs during 1998-2005 were used to develop multivariable regression models following stepwise variable selection. Models were developed by using all survivors at specific times during hospitalization (in delivery room [n = 8713], 7-day [n = 6996], 28-day [n = 6241], and 36-week postmenstrual age [n = 5118]) to predict death or death/neurodevelopmental impairment at 18 to 22 months.
Results:
Prediction of death or neurodevelopmental impairment in extremely premature infants is improved by using information available later during the clinical course. The importance of birth weight declines, whereas the importance of respiratory illness severity increases with advancing postnatal age. The c-statistic in validation models ranged from 0.74 to 0.80 with misclassification rates ranging from 0.28 to 0.30.
Conclusions:
Dynamic models of the changing probability of individual outcome can improve outcome predictions in preterm infants. Various current and future scenarios can be modeled by input of different clinical possibilities to develop individual "outcome trajectories" and evaluate impact of possible morbidities on outcome.
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