Predicting Dural Tear in Compound Depressed Skull Fractures: A Prospective Multicenter Correlational Study

Background: Compound depressed skull fracture (DSF) is a neurosurgical emergency. Preoperative knowledge of dural status is indispensable for treatment decision making. This study aimed to determine predictors of dural tear from clinical and imaging characteristics in patients with compound DSF. Methods: This prospective, multicenter correlational study in neurosurgical hospitals in Addis Ababa, Ethiopia, included 128 patients operated on from January 1, 2016, to October 31, 2016. Clinical, imaging, and intraoperative findings were evaluated. Univariate and multivariate analyses were used to establish predictors of dural tear. A logistic regression model was developed to predict probability of dural tear. Model validation was done using the receiver operating characteristic curve. Results: Dural tear was seen in 55.5% of 128 patients. Demographics, injury mechanism, clinical presentation, and site of DSF had no significant correlation with dural tear. In univariate and multivariate analyses, depth of fracture depression (odds ratio 1.3, P < 0.001), pneumocephalus (odds ratio 2.8, P = 0.005), and brain contusions/intracerebral hematoma (odds ratio 5.5, P < 0.001) were significantly correlated with dural tear. We developed a logistic regression model (diagnostic test) to calculate probability of dural tear. Using the receiver operating characteristic curve, we determined the cutoff value for a positive test giving the highest accuracy to be 30% with a corresponding sensitivity of 93.0% and specificity of 43.9%. Conclusions: Dural tear in compound DSF can be predicted with 93.0% sensitivity using preoperative findings and may guide treatment decision making in resource-limited settings where risk of extensive cranial surgery outweighs the benefit.