Bullet trajectory predicts the need for damage control: an artificial neural network model

J Trauma. 2002 May;52(5):852-8. doi: 10.1097/00005373-200205000-00006.


Background: Effective use of damage control in trauma hinges on an early decision to use it. Bullet trajectory has never been studied as a marker for damage control. We hypothesize that this decision can be predicted by an artificial neural network (ANN) model based on the bullet trajectory and the patient's blood pressure.

Methods: A multilayer perceptron ANN predictive model was developed from a data set of 312 patients with single abdominal gunshot injuries. Input variables were the bullet path, trajectory patterns, and admission systolic pressure. The output variable was either a damage control laparotomy or intraoperative death. The best performing ANN was implemented on prospectively collected data from 34 patients.

Results: The model achieved a correct classification rate of 0.96 and area under the receiver operating characteristic curve of 0.94. External validation showed the model to have a sensitivity of 88% and specificity of 96%. Model implementation on the prospectively collected data had a correct classification rate of 0.91. Sensitivity analysis showed that systolic pressure, bullet path across the midline, and trajectory involving the right upper quadrant were the three most important input variables.

Conclusion: Bullet trajectory is an important, hitherto unrecognized, factor that should be incorporated into the decision to use damage control.

MeSH terms

  • Abdominal Injuries / mortality
  • Abdominal Injuries / physiopathology*
  • Abdominal Injuries / surgery*
  • Adult
  • Blood Pressure / physiology*
  • Humans
  • Laparotomy
  • Neural Networks, Computer*
  • Outcome Assessment, Health Care
  • Predictive Value of Tests
  • ROC Curve
  • Reproducibility of Results
  • Wounds, Gunshot / mortality
  • Wounds, Gunshot / physiopathology*
  • Wounds, Gunshot / surgery*