Published online December 1, 2005
PEDIATRICS Vol. 116 No. 6 December 2005, pp. 1367-1373 (doi:10.1542/peds.2004-2099)
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Prediction of Death for Extremely Low Birth Weight Neonates

Namasivayam Ambalavanan, MD*, Waldemar A. Carlo, MD*, Georgiy Bobashev, PhD{ddagger}, Erin Mathias, BS{ddagger}, Bing Liu, MS{ddagger}, Kenneth Poole, PhD{ddagger}, Avroy A. Fanaroff, MB, BCh§, Barbara J. Stoll, MD||, Richard Ehrenkranz, MD, Linda L. Wright, MD# for the National Institute of Child Health and Human Development Neonatal Research Network

* Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama
{ddagger} Research Triangle Institute, Research Triangle Park, North Carolina
§ Department of Pediatrics, Case Western Reserve University, Cleveland, Ohio
|| Department of Pediatrics, Emory University, Atlanta, Georgia
Department of Pediatrics, Yale University, New Haven, Connecticut
# National Institute of Child Health and Human Development Neonatal Research Network, Bethesda, Maryland

Objective. To compare multiple logistic regression and neural network models in predicting death for extremely low birth weight neonates at 5 time points with cumulative data sets, as follows: scenario A, limited prenatal data; scenario B, scenario A plus additional prenatal data; scenario C, scenario B plus data from the first 5 minutes after birth; scenario D, scenario C plus data from the first 24 hours after birth; scenario E, scenario D plus data from the first 1 week after birth.

Methods. Data for all infants with birth weights of 401 to 1000 g who were born between January 1998 and April 2003 in 19 National Institute of Child Health and Human Development Neonatal Research Network centers were used (n = 8608). Twenty-eight variables were selected for analysis (3 for scenario A, 15 for scenario B, 20 for scenario C, 25 for scenario D, and 28 for scenario E) from those collected routinely. Data sets censored for prior death or missing data were created for each scenario and divided randomly into training (70%) and test (30%) data sets. Logistic regression and neural network models for predicting subsequent death were created with training data sets and evaluated with test data sets. The predictive abilities of the models were evaluated with the area under the curve of the receiver operating characteristic curves.

Results. The data sets for scenarios A, B, and C were similar, and prediction was best with scenario C (area under the curve: 0.85 for regression; 0.84 for neural networks), compared with scenarios A and B. The logistic regression and neural network models performed similarly well for scenarios A, B, D, and E, but the regression model was superior for scenario C.

Conclusions. Prediction of death is limited even with sophisticated statistical methods such as logistic regression and nonlinear modeling techniques such as neural networks. The difficulty of predicting death should be acknowledged in discussions with families and caregivers about decisions regarding initiation or continuation of care.

Key Words: logistic models • neural networks (computer) • predictive value • receiver operating characteristic curve

Abbreviations: ELBW, extremely low birth weight • ROC, receiver operating characteristic • AUC, area under the curve • SNAP, Score for Neonatal Acute Physiology • PPV, positive predictive value • NPV, negative predictive value

Accepted Feb 28, 2005.


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