Development of a model for prediction of survival in pediatric trauma patients: comparison of artificial neural networks and logistic regression.
OBJECTIVE
There 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).


METHODS
Patients 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).


RESULTS
There 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).


CONCLUSIONS
The 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.
DiRusso
Stephen M
SM
Department of Surgery, New York Medical College and Westchester Medical Center, Valhalla, NY 10595, USA.
Chahine
A Alfred
AA
Sullivan
Thomas
T
Risucci
Donald
D
Nealon
Peter
P
Cuff
Sara
S
Savino
John
J
Slim
Michel
M
D002138:Calibration; D002648:Child; D005260:Female; D006801:Humans; D015601:Injury Severity Score; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012372:ROC Curve; D012044:Regression Analysis; D016019:Survival Analysis; D015996:Survival Rate; D014947:Wounds and Injuries / Q000145:classification / Q000401:mortality*