title: Determining risk of traumatic aortic injury: how to optimize imaging strategy.
abstract: OBJECTIVE
  Our objective was to develop and validate a clinical prediction rule that determines patient probability of traumatic aortic injury to guide selection of optimal screening imaging strategy.
  
  
  METHODS
  A 2-year, single-institution retrospective case-control study was conducted of 31 cases of traumatic aortic injury and 171 random major trauma control subjects. The presence of potential injury predictors was determined from chart review. Logistic regression was used to determine injury predictors, and clinically similar predictors were combined into composite predictors. The composite predictors were used to develop a seven-point injury index clinical prediction rule using multivariate logistic regression. Injury probabilities were determined through Bayes' theorem. Bootstrap validation was performed.
  
  
  RESULTS
  Predictors of aortic injury included head injury (odds ratio, 18.3; 95% confidence interval [CI], 7.3-46), pelvic fracture (odds ratio, 27.3; 95% CI, 8.8-85), pneumothorax (odds ratio, 27.3; 95% CI, 8.8-85), and lack of seat belt use (odds ratio, 6.8; 95% CI, 2.6-17). The seven composite predictors of age, unrestrained vehicle occupant, hypotension, thoracic injury, abdominopelvic injury, extremity fracture, and head injury, were combined into the seven-point injury index. In the injury index, each composite predictor had an adjusted odds ratio of 7.1 (95% CI, 3.7-13.5), and the odds ratios were additive. The injury index prediction rule had an area under the receiver operating characteristic curve of 0.97. All injured patients had at least one composite predictor.
  
  
  CONCLUSIONS
  The probability of traumatic aortic injury can be estimated from the injury index prediction rule. Because cost-effectiveness of various imaging strategies depends on probability of injury, the prediction rule can guide imaging selection.
authors.affiliation: Department of Radiology, University of Washington-Harborview Medical Center, Seattle 98104-2499, USA.
authors.forename: C C
authors.initials: CC
authors.lastname: Blackmore
authors.forename: A
authors.initials: A
authors.lastname: Zweibel
authors.forename: F A
authors.initials: FA
authors.lastname: Mann
mesh_terms: D001011:Aorta / Q000293:injuries*; D016022:Case-Control Studies; D006801:Humans; D015601:Injury Severity Score; D016015:Logistic Models; D008875:Middle Aged; D012189:Retrospective Studies; D018570:Risk Assessment; D014949:Wounds, Nonpenetrating / Q000453:epidemiology*