Accident and Emergency Department, Hospital of Navarre, Navarre, Spain; Department of Health, Public University of Navarre, Navarre, Spain.
Am J Emerg Med. 2013 Sep;31(9):1382-8. doi: 10.1016/j.ajem.2013.06.026. Epub 2013 Jul 26.
To determine which factors predict death among trauma patients who are alive on arrival at hospital.
Design prospective cohort study method. Data were collected on 378 trauma patients who were initially delivered by the emergency medical services of Navarre (Spain) with multiple injuries with a new injury severity score of 15 or more in 2011-2012. These data related to age, gender, presence of premorbid conditions, abbreviated injury score, injury severity score, new injury severity score (NISS), revised trauma score (RTS), and prehospital and hospital response times. Bivariate analysis was used to show the association between each variable and time until death. Mortality prediction was modeled using logistic regression analysis.
The variables related to the end result were the age of the patient, associated comorbidity, NISS, and hospital RTS. Two models were formulated: in one, the variables used were quantitative, while in the other model these variables were converted into dichotomous qualitative variables. The predictive capability of the two models was compared with the trauma and injury severity score using the area under the curve. The predictive capacities of the three models had areas under the curve of 0.93, 0.88, and 0.87. The response times of the Navarre emergency services system, measured as the sum of the time taken to reach the hospital (median time of 65 min), formulate computed tomography (46 min), and perform crucial surgery (115 min), when required, were not taken into account.
Age, premorbid conditions, hospital RTS, and NISS are significant predictors of death after trauma. The time intervals between the accident and arrival at the hospital, arrival at the hospital and the first computed tomography scan or the first crucial emergency intervention, do not appear to affect the risk of death.
确定在抵达医院时存活的创伤患者中哪些因素与死亡相关。
采用前瞻性队列研究方法。2011-2012 年，共收集了 378 名因多处创伤且新损伤严重程度评分（NISS）≥15 分而被纳瓦拉（西班牙）急诊医疗服务机构首次运送的创伤患者的资料。这些数据与年龄、性别、合并症、简明损伤评分（AIS）、损伤严重程度评分（ISS）、新损伤严重程度评分（NISS）、修订创伤评分（RTS）、院前和医院反应时间有关。采用单变量分析显示各变量与死亡时间之间的相关性。采用逻辑回归分析对死亡率预测模型进行建模。
与结局相关的变量是患者年龄、合并症、NISS 和医院 RTS。制定了两个模型：一个模型中使用的变量是定量的，而另一个模型中则将这些变量转换为二分类定性变量。通过曲线下面积比较了两个模型的预测能力与创伤和损伤严重程度评分。三个模型的预测能力的曲线下面积分别为 0.93、0.88 和 0.87。纳瓦拉紧急救援系统的反应时间，即到达医院的总时间（中位数为 65 分钟）、进行计算机断层扫描（46 分钟）和进行关键手术（需要时 115 分钟）的时间没有被考虑在内。
年龄、合并症、医院 RTS 和 NISS 是创伤后死亡的重要预测因素。事故发生与到达医院、到达医院与首次计算机断层扫描或首次关键紧急干预之间的时间间隔似乎并不影响死亡风险。
