title: Comparison of new modeling methods for postnatal weight in ELBW infants using prenatal and postnatal data.
abstract: OBJECTIVE
  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.
authors.affiliation: *Department of Pediatrics, Wake Forest University, Winston-Salem, NC †Department of Biomedical Informatics, Vanderbilt University, Nashville, TN.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Porcelli
authors.forename: S Trent
authors.initials: ST
authors.lastname: Rosenbloom
mesh_terms: D000465:Algorithms; D001724:Birth Weight; D001835:Body Weight; D057286:Electronic Health Records; D004750:Enteral Nutrition; D005260:Female; D005440:Fluid Therapy; D005544:Forecasting / Q000379:methods; D005865:Gestational Age; D057238:Growth Charts; D006801:Humans; D052577:Infant, Extremely Low Birth Weight / Q000254:growth & development*; D062071:Infant, Extremely Premature / Q000254:growth & development*; D007231:Infant, Newborn; D016014:Linear Models; D008297:Male; D016571:Neural Networks, Computer; D009752:Nutritional Status; D010288:Parenteral Nutrition