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Updated: May 21, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

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

|June 13, 2012

Summary

This summary is machine-generated.

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.

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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.