PURPOSE: To develop a method to use clinically apparent factors to determine cervical spine fracture risk to guide selection of optimal imaging strategies. MATERIALS AND METHODS: Records from 472 patients with trauma (168 with fractures, 304 control patients) who visited the emergency department in 1994 and 1995 were reviewed for 20 potential predictors of cervical spine fracture in this retrospective case-control study. Simple logistic regression was used to determine predictors of cervical spine fracture. Prediction rules were formulated by using multiple logistic regression and recursive partitioning with bootstrap validation. Posttest fracture probabilities were calculated from base prevalence and likelihood ratios derived for predictors by using Bayes theorem. RESULTS: Predictors of cervical spine fracture included severe head injury (adjusted odds ratio [OR] = 8.5, 95% CI: 4.0, 17.0), high-energy cause (OR = 11.6, 95% CI: 5.4, 25.0), and focal neurologic deficit (OR = 58, 95% CI: 12, 283). The prediction rule was used to stratify patients into groups with fracture probabilities of 0.04%-19.70%. After adjusting for overfitting, the area under the receiver operating characteristic curve was 0.87. CONCLUSION: Clinically apparent factors, including cause of injury, associated injuries, and age, can be used to determine the probability of cervical spine fracture. Development of evidence-based imaging guidelines should incorporate knowledge of fracture probability.
PURPOSE: To develop a method to use clinically apparent factors to determine cervical spine fracture risk to guide selection of optimal imaging strategies. MATERIALS AND METHODS: Records from 472 patients with trauma (168 with fractures, 304 control patients) who visited the emergency department in 1994 and 1995 were reviewed for 20 potential predictors of cervical spine fracture in this retrospective case-control study. Simple logistic regression was used to determine predictors of cervical spine fracture. Prediction rules were formulated by using multiple logistic regression and recursive partitioning with bootstrap validation. Posttest fracture probabilities were calculated from base prevalence and likelihood ratios derived for predictors by using Bayes theorem. RESULTS: Predictors of cervical spine fracture included severe head injury (adjusted odds ratio [OR] = 8.5, 95% CI: 4.0, 17.0), high-energy cause (OR = 11.6, 95% CI: 5.4, 25.0), and focal neurologic deficit (OR = 58, 95% CI: 12, 283). The prediction rule was used to stratify patients into groups with fracture probabilities of 0.04%-19.70%. After adjusting for overfitting, the area under the receiver operating characteristic curve was 0.87. CONCLUSION: Clinically apparent factors, including cause of injury, associated injuries, and age, can be used to determine the probability of cervical spine fracture. Development of evidence-based imaging guidelines should incorporate knowledge of fracture probability.