Siegmund G P, Brault J R, Wheeler J B
MacInnis Engineering Associates, Richmond, BC, Canada.
Accid Anal Prev. 2000 Mar;32(2):207-17. doi: 10.1016/s0001-4575(99)00109-8.
Recent experiments have produced a linked data set of clinical and kinematic responses for human subjects exposed to controlled low-speed rear-end collisions. The purpose of this paper was to examine this paired data set and determine whether the presence or absence of clinical symptoms could be predicted from the peak linear and angular kinematic response of the head and neck. The data were generated using 42 male and female human subjects seated normally in the front passenger seat of a stationary vehicle struck from behind to produce vehicle speed changes of 4 and 8 km/h. Pre- and post-test clinical examinations documented the presence, severity and duration of whiplash-associated disorders (WAD). Logistic regression and backward elimination of independent variables were used to develop the prediction model. The analysis yielded a 16 parameter model that was significantly related (odds ratio = 21.2; P = 0.0069) to the presence or absence of transient whiplash symptoms. The model correctly predicted symptom presence in 13 of 23 tests (sensitivity 57%) and symptom absence in 49 of 52 tests (specificity 94%) in a population of 75 with a symptom prevalence of 31%. The model's positive predictive value was 81% and its negative predictive value was 83%. Despite statistical significance, the model did not discriminate between the presence and absence of symptoms in all tests, and indicated that factors other than the selected peak kinematic responses influenced symptom production.
最近的实验生成了一组关联数据集,该数据集涉及暴露于可控低速追尾碰撞中的人类受试者的临床和运动学反应。本文的目的是检查这一配对数据集,并确定能否根据头部和颈部的峰值线性和角向运动学反应来预测临床症状的有无。数据是通过让42名男性和女性受试者正常坐在静止车辆的前排乘客座位上,从后方撞击车辆以产生4公里/小时和8公里/小时的车速变化而生成的。测试前后的临床检查记录了挥鞭样损伤相关疾病(WAD)的存在、严重程度和持续时间。使用逻辑回归和自变量的向后消除法来建立预测模型。分析得出了一个16参数模型,该模型与短暂性挥鞭样症状的有无显著相关(优势比 = 21.2;P = 0.0069)。在症状患病率为31%的75名受试者群体中,该模型在23次测试中的13次正确预测了症状的存在(敏感性57%),在52次测试中的49次正确预测了症状的不存在(特异性94%)。该模型的阳性预测值为81%,阴性预测值为83%。尽管具有统计学意义,但该模型在所有测试中并未区分症状的有无,这表明除了所选的峰值运动学反应之外,还有其他因素影响症状的产生。