Brain injury severity due to direct head contact from near-side motor vehicle collisions

The objective of this study was to generate functional forms of brain injury risk curves using the National Automotive Sample System Crashworthiness Data System’s (NASS-CDS) database for the years of 2001–2015. The population of interest was near-side occupants who experienced a direct head impact with an injury source located lateral to a typical seated position. Brain injuries were restricted to Abbreviated Injury Scale (AIS) 2005 Update 2008 defined concussions and internal organ injuries of the head. Near-side occupants comprised two major groups, both of which were required to have evidence of head contact (i.e., a head injury with DIRINJ = 1 and SOUCON = 1 or 2): brain injured occupants (MAIS1, MAIS2, MAIS3+) and non-brain injured occupants with some other direct contact head injury (MAIS0). Analyzed cases were required to have an indication of a reasonable crash reconstruction. Injury sources allowed within the final sample consisted of A-pillars, B-pillars, roof/roof rails, impacting vehicles/exterior objects, other components of the vehicle’s side interior, and other occupants or otherwise unspecified interior objects. Risk curves for occupants with brain injury severities of MAIS0, MAIS1+, MAIS2+, and MAIS3+ were generated using multivariate stepwise logistic regressions. Investigated predictors involved vehicle change in velocity, seat belt use, principal direction of force (PDOF), and injury source type (B-pillar and side window). Multivariate stepwise logistic regressions identified significant predictors of lateral change in velocity (<i>dvlat</i>) for all injury severity categories, and side window injury source (INJSOU = 56, 57, 58, 106, and 107) for MAIS0 and MAIS1+ risk curves. Although model sensitivity decreased for more severe injury predictions, risk curves dependent on only <i>dvlat</i> yielded accuracies of 70% for all presented models. Real world crashes are often complex and lack the benefit of real time monitoring; however, NASS-CDS post-crash investigations provide data useful for injury risk prediction. Further analysis is needed to determine the effect of data confidence, injury source, and accident sequence restrictions on NASS-CDS sampling biases. The presented models likely favor a more conservative risk prediction due to the limitations of NASS-CDS data collection, AIS code conversion, and unweighted sample analysis.

本研究旨在基于2001至2015年的美国国家汽车抽样系统碰撞安全性数据系统（National Automotive Sample System Crashworthiness Data System，NASS-CDS）数据库，构建脑损伤风险曲线的函数形式。本研究关注的研究对象为侧方碰撞场景中头部直接受撞击、撞击源位于标准坐姿侧向位置的近侧乘员。脑损伤限定为符合《简明损伤定级标准2005更新版2008》（Abbreviated Injury Scale 2005 Update 2008，AIS）定义的脑震荡及头部内部器官损伤。近侧乘员分为两大组别，两组均需具备头部接触的相关证据（即头部损伤的DIRINJ=1且SOUCON=1或2）：脑损伤乘员（MAIS1、MAIS2、MAIS3+）以及存在其他直接头部接触损伤的非脑损伤乘员（MAIS0）。纳入分析的案例需具备合理的碰撞重建依据。最终样本允许的损伤来源包括A柱、B柱、车顶/车顶纵梁、碰撞车辆/外部物体、车辆侧内饰的其他部件，以及其他乘员或未明确说明的内饰物体。针对脑损伤严重程度分别为MAIS0、MAIS1+、MAIS2+及MAIS3+的乘员，研究采用多元逐步逻辑回归构建相应的风险曲线。考察的预测变量包括车辆速度变化量、安全带使用情况、主要受力方向（Principal Direction of Force，PDOF）以及损伤源类型（B柱与侧车窗）。多元逐步逻辑回归分析结果显示，对于所有损伤严重程度类别，侧向速度变化量（dvlat）均为显著预测因子；而针对MAIS0与MAIS1+的风险曲线，侧车窗损伤源（INJSOU=56、57、58、106及107）同样为显著预测因子。尽管针对更严重损伤的预测模型灵敏度有所下降，但仅基于dvlat构建的风险曲线在所有展示模型中均达到70%的准确率。现实世界中的碰撞通常较为复杂，且缺乏实时监测的条件，但NASS-CDS的事后调查数据可为损伤风险预测提供有效支撑。未来仍需进一步开展分析，以明确数据置信度、损伤源及事故序列限制对NASS-CDS抽样偏差的影响。受限于NASS-CDS的数据收集流程、AIS编码转换规则以及未加权样本分析方法，本研究展示的模型可能倾向于给出更为保守的风险预测结果。