title: Evaluation of an artificial neural network to predict urea nitrogen appearance for critically ill multiple-trauma patients.
abstract: BACKGROUND
  Computer-based simulated biologic neural network models have made significant strides in clinical medicine.
  
  
  METHODS
  To determine the predictive performance of a conventional regression model and an artificial neural network for estimating urea nitrogen appearance (UNA) during critical illness, 125 adult patients admitted to the trauma intensive care unit who required specialized nutrition support were studied. The first 100 consecutive patients were used to develop the 2 models. The first model used stepwise multivariate regression analysis. The second model entailed the use of a feeding-forward, back-propagation, supervised neural network. Bias and precision of both methods were evaluated in 25 separate patients.
  
  
  RESULTS
  Multivariate regression analysis revealed a significant highly correlative relationship (r(2) = .918, p < or = .01): Predicted UNA (g/d) = (0.29 x WT) + (1.20 x WBC) + (0.44 x SUN) with WT as current body weight in kg, WBC as white blood cell count in cells/mm(3), and SUN as serum urea nitrogen concentration (mg/dL). The regression method was biased toward overestimating measured UNA, whereas the neural network was unbiased. Precision (95% confidence interval) of the neural network was significantly better than the regression (3.3-7.2 g vs 7.3-11.6 g, respectively, p < .01). Regression analysis successfully predicted UNA within 3 g of measured UNA in 16% (4 of 25) of patients, whereas the neural network successfully predicted UNA in 44% (11 out of 25) of patients (p < .06).
  
  
  CONCLUSIONS
  These preliminary data indicate that use of an artificial neural network may be superior to conventional regression modeling techniques for estimating UNA in critically ill adult multiple-trauma patients receiving specialized nutrition support.
authors.affiliation: Department of Pharmacy, University of Tennessee Health Science Center, Memphis, 38163, USA. rdickerson@utmem.edu
authors.forename: Roland N
authors.initials: RN
authors.lastname: Dickerson
authors.forename: Darius L
authors.initials: DL
authors.lastname: Mason
authors.forename: Martin A
authors.initials: MA
authors.lastname: Croce
authors.forename: Gayle
authors.initials: G
authors.lastname: Minard
authors.forename: Rex O
authors.initials: RO
authors.lastname: Brown
mesh_terms: D000328:Adult; D015982:Bias; D016638:Critical Illness; D005260:Female; D006801:Humans; D008297:Male; D009104:Multiple Trauma / Q000628:therapy / Q000652:urine*; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000032:analysis / Q000652:urine*; D018529:Nutritional Support; D011237:Predictive Value of Tests; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014508:Urea / Q000032:analysis / Q000652:urine*
chemical_list: D014508:Urea; D009584:Nitrogen