Traumatic optic neuropathy prediction after blunt facial trauma: derivation of a risk score based on facial CT findings at admission.

PURPOSE

To determine the specific facial computed tomographic (CT) findings that can be used to predict traumatic optic neuropathy (TON) in patients with blunt craniofacial trauma and propose a scoring system to identify patients at highest risk of TON.

MATERIALS AND METHODS

This study was compliant with HIPAA, and permission was obtained from the institutional review board. Facial CT examination findings in 637 consecutive patients with a history of blunt facial trauma were evaluated retrospectively. The following CT variables were evaluated: midfacial fractures, extraconal hematoma, intraconal hematoma, hematoma along the optic nerve, hematoma along the posterior globe, optic canal fracture, nerve impingement by optic canal fracture fragment, extraconal emphysema, and intraconal emphysema. A prediction model was derived by using regression analysis, followed by receiver operating characteristic analysis to assess the diagnostic performance. To examine the degree of overfitting of the prediction model, a k-fold cross-validation procedure (k = 5) was performed. The ability of the cross-validated model to allow prediction of TON was examined by comparing the mean area under the receiver operating characteristic curve (AUC) from cross-validations with that obtained from the observations used to create the model.

RESULTS

The five CT variables with significance as predictors were intraconal hematoma (odds ratio, 12.73; 95% confidence interval [CI]: 5.16, 31.42; P < .001), intraconal emphysema (odds ratio, 5.21; 95% CI: 2.03, 13.36; P = .001), optic canal fracture (odds ratio, 4.45; 95% CI: 1.91, 10.35; P = .001), hematoma along the posterior globe (odds ratio, 0.326; 95% CI: 0.111, 0.958; P = .041), and extraconal hematoma (odds ratio, 2.36; 95% CI: 1.03, 5.41; P = .042). The AUC was 0.818 (95% CI: 0.734, 0.902) for the proposed model based on the observations used to create the model and 0.812 (95% CI: 0.723, 0.9) after cross-validation, excluding substantial overfitting of the model.

CONCLUSION

The risk model developed may help radiologists suggest the possibility of TON and prioritize ophthalmology consults. However, future external validation of this prediction model is necessary.