• Stephen M DiRusso, A Alfred Chahine, Thomas Sullivan, Donald Risucci, Peter Nealon, Sara Cuff, John Savino, and Michel Slim.
• Department of Surgery, New York Medical College and Westchester Medical Center, Valhalla, NY 10595, USA.
• J. Pediatr. Surg. 2002 Jul 1;37(7):1098-104; discussion 1098-104.

Background/PurposeThere is a paucity of outcome prediction models for injured children. Using the National Pediatric Trauma Registry (NPTR), the authors developed an artificial neural network (ANN) to predict pediatric trauma death and compared it with logistic regression (LR).MethodsPatients in the NPTR from 1996 through 1999 were included. Models were generated using LR and ANN. A data search engine was used to generate the ANN with the best fit for the data. Input variables included anatomic and physiologic characteristics. There was a single output variable: probability of death. Assessment of the models was for both discrimination (ROC area under the curve) and calibration (Lemeshow-Hosmer C-Statistic).ResultsThere were 35,385 patients. The average age was 8.1 +/- 5.1 years, and there were 1,047 deaths (3.0%). Both modeling systems gave excellent discrimination (ROC A(z): LR = 0.964, ANN = 0.961). However, LR had only fair calibration, whereas the ANN model had excellent calibration (L/H C stat: LR = 36, ANN = 10.5).ConclusionsThe authors were able to develop an ANN model for the prediction of pediatric trauma death, which yielded excellent discrimination and calibration exceeding that of logistic regression. This model can be used by trauma centers to benchmark their performance in treating the pediatric trauma population.Copyright 2002, Elsevier Science (USA). All rights reserved.

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