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A Better START for Low-acuity Victims: Data-driven Refinement of Mass Casualty Triage

机译:低敏受害者的一个更好的起点:大规模伤亡分类的数据驱动优化

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Objective. Methods currently used to triage patients from mass casualty events have a sparse evidence basis. The objective of this project was to assess gaps of the widely used Simple Triage and Rapid Transport (START) algorithm using a large database when it is used to triage low-acuity patients. Subsequently, we developed and tested evidenced-based improvements to START. Methods. Using the National Trauma Database (NTDB), a large set of trauma victims were assigned START triage levels, which were then compared to recorded patient mortality outcomes using area under the receiver-operator curve (AUC). Subjects assigned to the "Minor/Green" level who nevertheless died prior to hospital discharge were considered mistriaged. Recursive partitioning identified factors associated with of these mistriaged patients. These factors were then used to develop candidate START models of improved triage, whose overall performance was then re-evaluated using data from the NTDB. This process of evaluating performance, identifying errors, and further adjusting candidate models was repeated iteratively. Results. The study included 322,162 subjects assigned to "Minor/Green" of which 2,046 died before hospital discharge. Age was the primary predictor of under-triage by START. Candidate models which re-assigned patients from the "Minor/Green" triage level to the "Delayed/Yellow" triage level based on age (either for patients >60 or >75), reduced mortality in the "Minor/Green" group from 0.6% to 0.1% and 0.3%, respectively. These candidate START models also showed net improvement in the AUC for predicting mortality overall and in select subgroups. Conclusion. In this research model using trauma registry data, most START under-triage errors occurred in elderly patients. Overall START accuracy was improved by placing elderly but otherwise minimally injured-mass casualty victims into a higher risk triage level. Alternatively, such patients would be candidates for closer monitoring at the scene or expedited transport ahead of other, younger "Minor/Green" victims.
机译:目的。当前用于从大规模人员伤亡事件中对患者进行分类的方法具有稀疏的证据基础。该项目的目的是使用大型数据库评估低眼力患者时,评估广泛使用的简单分类和快速运输(START)算法的差距。随后,我们开发并测试了基于证据的START改进。方法。使用国家创伤数据库(NTDB),为大量创伤受害者分配了START分诊等级,然后使用接收者-操作者曲线下的面积(AUC)将其与记录的患者死亡率结果进行比较。分配为“轻微/绿色”级别但在出院前死亡的受试者被认为是流产。递归划分确定了与这些流产患者相关的因素。然后,这些因素将用于开发改进分类的候选START模型,然后使用来自NTDB的数据重新评估其总体性能。反复评估,评估错误,识别错误以及进一步调整候选模型的过程。结果。该研究包括322,162名被分配为“次要/绿色”受试者,其中2,046人在出院前死亡。年龄是START不足分流的主要预测指标。根据年龄(大于60岁或大于75岁的患者)将患者从“次要/绿色”分类分类重新分配为“延迟/黄色”分类的候选模型,降低了“次要/绿色”分类中的死亡率分别为0.6%至0.1%和0.3%。这些候选的START模型还显示了AUC的净改善,可以预测总体死亡率和部分亚组的死亡率。结论。在使用创伤登记数据的研究模型中,大多数START-triage错误均发生在老年患者中。通过将年长但受伤人数最少的伤亡受害者置于较高的风险分流级别,可以提高整体START准确性。或者,这些患者将成为在现场进行更密切监视或在其他年轻的“未成年人/绿色”受害者面前加快运输的候选人。

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