Predicting significant torso trauma

Ram Nirula, Daniel Talmor, Karen Brasel

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

Background: Identification of motor vehicle crash (MVC) characteristics associated with thoracoabdominal injury would advance the development of automatic crash notification systems (ACNS) by improving triage and response times. Our objective was to determine the relationships between MVC characteristics and thoracoabdorninal trauma to develop a torso injury probability model. Methods: Drivers involved in crashes from 1993 to 2001 within the National Automotive Sampling System were reviewed. Relationships between torso injury and MVC characteristics were assessed using multivariate logistic regression. Receiver operating characteristic curves were used to compare the model to current ACNS models. Results: There were a total of 56,466 drivers. Age, ejection, braking, avoidance, velocity, restraints, passenger-side impact, rollover, and vehicle weight and type were associated with injury (p < 0.05). The area under the receiver operating characteristic curve (83.9) was significantly greater than current ACNS models. Conclusion: We have developed a thoracoabdominal injury probability model that may improve patient triage when used with ACNS.

Original languageEnglish (US)
Pages (from-to)132-135
Number of pages4
JournalJournal of Trauma - Injury, Infection and Critical Care
Volume59
Issue number1
DOIs
StatePublished - Jul 1 2005
Externally publishedYes

Keywords

  • Automatic
  • Crash
  • Injury
  • Notification
  • Prediction
  • Thoracoabdominal
  • Torso

ASJC Scopus subject areas

  • Surgery
  • Critical Care and Intensive Care Medicine

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