Salia Shemsedin Musefa, Mersha Hagos Biluts, Aklilu Abenezer Tirsit, Baleh Abat Sahlu, Lund-Johansen Morten
Neurosurgery Unit, Department of Surgery, Hawassa University Comprehensive Specialized Hospital, Tabor Sub-city, Hawassa, Ethiopia.
Neurosurgery Unit, Department of Surgery, Addis Ababa University Tikur Anbessa Specialized Hospital, Lideta Sub-city, Addis Ababa, Ethiopia.
World Neurosurg. 2018 Jun;114:e833-e839. doi: 10.1016/j.wneu.2018.03.095. Epub 2018 Mar 26.
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.
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.
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%.
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.
复合性凹陷性颅骨骨折（DSF）是一种神经外科急症。术前了解硬脑膜状态对于治疗决策至关重要。本研究旨在根据复合性DSF患者的临床和影像学特征确定硬脑膜撕裂的预测因素。
这项在埃塞俄比亚亚的斯亚贝巴神经外科医院进行的前瞻性、多中心相关性研究纳入了2016年1月1日至2016年10月31日期间接受手术的128例患者。对临床、影像学和术中发现进行了评估。采用单因素和多因素分析来确定硬脑膜撕裂的预测因素。建立了逻辑回归模型来预测硬脑膜撕裂的概率。使用受试者工作特征曲线进行模型验证。
128例患者中有55.5%出现硬脑膜撕裂。人口统计学、损伤机制、临床表现和DSF部位与硬脑膜撕裂无显著相关性。在单因素和多因素分析中，骨折凹陷深度（比值比1.3，P<0.001）、气颅（比值比2.8，P=0.005）和脑挫伤/脑内血肿（比值比5.5，P<0.001）与硬脑膜撕裂显著相关。我们开发了一个逻辑回归模型（诊断测试）来计算硬脑膜撕裂的概率。使用受试者工作特征曲线，我们确定阳性测试的截止值为30%，此时准确性最高，相应的敏感性为93.0%，特异性为43.9%。
使用术前检查结果可预测复合性DSF中硬脑膜撕裂的敏感性为93.0%，这可能有助于在资源有限的环境中指导治疗决策，在这种环境中，广泛开颅手术的风险超过了益处。
