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1
Department of Radiology, University of Washington-Harborview Medical Center,
Box 359728, 325 Ninth Ave., Seattle, WA 98104-2499
2
Department of Radiology, University of North Carolina-Chapel Hill, School of
Medicine, CB 7510, Chapel Hill, NC 27599-7510
3
Nortwest Asthma and Allergy Clinic, 4500 Sand Point Way N.E., Ste. 222,
Seattle, WA 98105
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.
MATERIALS AND 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.
CONCLUSION. 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.
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