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Comparison of New Modeling Methods for Postnatal Weight in ELBW Infants Using Prenatal and Postnatal Data

Porcelli, Peter J.*; Rosenbloom, S. Trent

Journal of Pediatric Gastroenterology & Nutrition: July 2014 - Volume 59 - Issue 1 - p e2–e8
doi: 10.1097/MPG.0000000000000342
Original Articles: Hepatology and Nutrition

Objectives: Postnatal infant weight curves are used to assess fluid management and evaluate postnatal nutrition and growth. Traditionally, postnatal weight curves are based on birth weight and do not incorporate postnatal clinical information. The aim of the present study was to compare the accuracy of birth weight–based weight curves with weight curves created from individual patient records, including electronic records, using 2 predictive modeling methods, linear regression (LR) and an artificial neural network (NN), which apply mathematical relations between predictor and outcome variables.

Methods: Perinatal demographic and postnatal nutrition data were collected for extremely-low-birth-weight (ELBW; birth weight <1000 g) infants. Static weight curves were generated using published algorithms. The postnatal predictive models were created using the demographic and nutrition dataset.

Results: Birth weight (861 ± 83 g, mean ± 1 standard deviation [SD]), gestational age (26.2 ± 1.4 weeks), and the first month of nutrition data were collected from individual health records for 92 ELBW infants. The absolute residual (|measured − predicted|) for weight was 84.8 ± 74.4 g for the static weight curves, 60.9 ± 49.1 g for the LR model, and 12.9 ± 9.2 g for the NN model, analysis of variance: both LR and NN P < 0.01 versus static curve. NPO (nothing by mouth) infants had greater weight curve discrepancies.

Conclusions: Compared with birth weight–based and logistic regression–generated weight curves, NN-generated weight curves more closely approximated ELBW infant weight curves, and, using the present electronic health record systems, may produce weight curves better reflective of the patient's status.

*Department of Pediatrics, Wake Forest University, Winston-Salem, NC

Department of Biomedical Informatics, Vanderbilt University, Nashville, TN.

Address correspondence and reprint requests to Peter J. Porcelli, Department of Pediatrics, Watlington Hall, Wake Forest University, Winston-Salem, NC 27157 (e-mail:

Received 12 May, 2010

Accepted 12 February, 2014

Information contained in the present work was presented at the 77th annual meeting of the Society for Pediatric Research.

The project was supported by the Allen Foundation (Midland, MI) and the US National Library of Medicine (K22 LM008576-02).

The authors report no conflicts of interest.

© 2014 by European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and North American Society for Pediatric Gastroenterology,