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: Roland N Dickerson, Darius L Mason, Martin A Croce, Gayle Minard, Rex O Brown
• Journal: JPEN. Journal of parenteral and enteral nutrition (JPEN J Parenter Enteral Nutr) 2005 Nov-Dec Vol. 29 Issue 6 Pg. 429-35 ISSN: 0148-6071 [Print] United States
• PMID: 16224036 (Publication Type: Evaluation Study, Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Urea
• Nitrogen

Topics

• Adult
• Bias
• Critical Illness
• Female
• Humans
• Male
• Multiple Trauma (therapy, urine)
• Multivariate Analysis
• Neural Networks, Computer
• Nitrogen (analysis, urine)
• Nutritional Support
• Predictive Value of Tests
• Regression Analysis
• Reproducibility of Results
• Sensitivity and Specificity
• Urea (analysis, urine)