a International Center for Automotive Medicine , University of Michigan , Ann Arbor , Michigan.
Traffic Inj Prev. 2014;15(6):619-26. doi: 10.1080/15389588.2013.852665.
Abdominal injuries resulting from vehicle crashes can be significant, in particular when undetected. In this study, abdominal injuries for occupants involved in frontal impacts were assessed using crash and medical data.
Injury rates and patterns were first assessed with respect to thoracic injuries. A statistical analysis was then conducted to predict abdominal injury outcome using 18 covariate variables, including 4 vehicle, 4 demographic, and 10 morphomic, derived from computed tomography (CT) scans. More than 260,000 logistic regression models were fitted using all possible variable combinations. The models were ranked using the Akaike information criterion (AIC) and combined through the model-averaging approach to produce the optimal predictive model. The performance of the models was then assessed using the area under the curve (AUC).
The rate of serious thoracic injury was 2.49 times higher than the rate of abdominal injury. The associated odds ratio was 2.31 (P <.01). These results suggest a strong association between serious abdominal and thoracic injuries. The optimal model AUC was 0.646 when using solely vehicle data, 0.696 when combining vehicle and demographic data, 0.866 when combining vehicle and morphomic data, and 0.879 when combining vehicle, demographic, and morphomic data. These results suggest that morphomic variables better predict abdominal injury outcomes than demographic variables. The most important morphomics variables included visceral fat area, trabecular bone density, and spine angulation.
This study is the first to combine vehicle, demographic, and anatomical data to predict abdominal injury rates in frontal crashes.
车辆碰撞导致的腹部损伤可能很严重，尤其是在未被发现的情况下。本研究使用事故和医疗数据评估了正面碰撞中乘员的腹部损伤。
首先根据胸部损伤评估损伤率和损伤模式。然后，使用来自 CT 扫描的 18 个协变量，包括 4 个车辆、4 个人口统计学和 10 个形态学变量，进行了统计分析，以预测腹部损伤结果。使用所有可能的变量组合拟合了超过 260,000 个逻辑回归模型。使用 Akaike 信息准则 (AIC) 对模型进行排名，并通过模型平均方法进行组合，以生成最佳预测模型。然后使用曲线下面积 (AUC) 评估模型的性能。
严重胸部损伤的发生率是腹部损伤的 2.49 倍。相关的优势比为 2.31（P <.01）。这些结果表明严重腹部和胸部损伤之间存在很强的关联。仅使用车辆数据时，最佳模型 AUC 为 0.646，结合车辆和人口统计学数据时为 0.696，结合车辆和形态学数据时为 0.866，结合车辆、人口统计学和形态学数据时为 0.879。这些结果表明形态学变量比人口统计学变量更好地预测腹部损伤结果。最重要的形态学变量包括内脏脂肪面积、小梁骨密度和脊柱角度。
这是第一项结合车辆、人口统计学和解剖学数据来预测正面碰撞中腹部损伤率的研究。
