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Evaluation of an Artificial Neural Network to Predict Urea Nitrogen Appearance for Critically Ill Multiple‐Trauma Patients
Author(s) -
Dickerson Roland N.,
Mason Darius L.,
Croce Martin A.,
Minard Gayle,
Brown Rex O.
Publication year - 2005
Publication title -
journal of parenteral and enteral nutrition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.935
H-Index - 98
eISSN - 1941-2444
pISSN - 0148-6071
DOI - 10.1177/0148607105029006429
Subject(s) - artificial neural network , multivariate statistics , regression analysis , stepwise regression , confidence interval , regression , serum urea , blood urea nitrogen , multivariate analysis , linear regression , intensive care unit , medicine , statistics , artificial intelligence , computer science , mathematics , creatinine
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 ≤ .01): Predicted UNA (g/d) = (0.29 × WT) + (1.20 × WBC) + (0.44 × 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.