Albert Devon L, Hardy Warren N, Kemper Andrew R
Center for Injury Biomechanics, Virginia Tech, Blacksburg, Virginia.
Traffic Inj Prev. 2024;25(sup1):S33-S42. doi: 10.1080/15389588.2024.2405643. Epub 2024 Nov 1.
The first objective was to evaluate the effect of using less censored (i.e., exact and interval-censored) data on thoracic injury risk curves and the resulting injury probabilities. The second objective was to generate new injury risk curves to predict Abbreviated Injury Scale (AIS) 3+ rib fractures based on chest deflection.
Two data sets consisting of postmortem human surrogate (PMHS) tests with multipoint chest deflection measurements were compiled: A less censored data set consisting of exact and interval-censored data and a doubly censored data set consisting of left- and right-censored data. Chest deflection data from both data sets were processed in a consistent manner to calculate the maximum deflections at different locations across the chest. Survival analysis methods were used to generate nonparametric and parametric injury risk curves for serious skeletal injury. The total sample sizes and proportions of less censored data used to generate the risk curves were varied for each curve to evaluate the effects of sample size and less censored data on risk curve shape and predicted injury thresholds.
Increasing the proportion of less censored data resulted in steeper injury risk curves and a higher predicted risk for a given amount of deflection. Differences in injury risk were more pronounced in the upper half of the injury risk curves. Introducing less censored data also produced narrower confidence intervals. At a total sample size of 79, increasing the percentage of less censored data from 0 to 30 had minimal effect on the shape of the risk curve.
Doubly censored chest deflection data have historically been used to generate thoracic injury risk curves for frontal motor vehicle crash events. This study found that incorporating less censored data into thoracic injury risk curves meaningfully affected the shape of the injury risk curves and their resulting injury risk predictions. All of the injury risk curves generated in the study predicted a lower threshold for serious rib fracture injury compared to previously developed injury risks curves that are currently in use in the field. Based on the results of this study, adding less censored data to injury risk curves should be strongly considered to improve thoracic injury risk curve prediction and confidence, especially for smaller sample sizes.
首先，评估使用较少受删失数据（即精确和区间删失数据）对胸伤风险曲线和由此产生的损伤概率的影响。第二个目的是生成新的损伤风险曲线，以基于胸部挠度预测 Abbreviated Injury Scale（AIS）3+肋骨骨折。
编译了包含多点胸部挠度测量的死后人体替代物（PMHS）测试的两个数据集：一个包含精确和区间删失数据的较少删失数据集，以及一个由左右删失数据组成的双重删失数据集。使用生存分析方法为严重骨骼损伤生成非参数和参数损伤风险曲线。为每条曲线生成风险曲线的总样本量和较少删失数据的比例有所不同，以评估样本量和较少删失数据对风险曲线形状和预测损伤阈值的影响。
增加较少删失数据的比例导致损伤风险曲线更加陡峭，并且在给定挠度下预测的风险更高。在上半部分的损伤风险曲线上，损伤风险的差异更为明显。引入较少删失数据还产生了更窄的置信区间。在总样本量为 79 的情况下，将较少删失数据的百分比从 0 增加到 30 对风险曲线的形状几乎没有影响。
胸部挠度的双重删失数据历来被用于生成正面汽车碰撞事件的胸部损伤风险曲线。本研究发现，将较少删失数据纳入到胸伤风险曲线中，会显著影响损伤风险曲线的形状及其由此产生的损伤风险预测。与目前在该领域使用的先前开发的损伤风险曲线相比，研究中生成的所有损伤风险曲线都预测出了较低的严重肋骨骨折损伤阈值。基于本研究的结果，应强烈考虑将较少删失数据添加到损伤风险曲线中，以提高胸伤风险曲线预测的准确性和置信度，尤其是在较小的样本量情况下。
