aEmergency Department bOperational Planning Office, Fire Brigade of Paris, Paris cAP-HP, EMS (Samu92), University Poincare Hospital, Garches dCHU H Mondor, AP-HP, Créteil, France.
Eur J Emerg Med. 2017 Oct;24(5):377-381. doi: 10.1097/MEJ.0000000000000366.
Whenever a mass casualty incident (MCI) occurs, it is essential to anticipate the final number of victims to dispatch the adequate number of ambulances. In France, the custom is to multiply the initial number of prehospital victims by 2-4 to predict the final number. However, no one has yet validated this multiplying factor (MF) as a predictive tool. We aimed to build a statistical model to predict the final number of victims from their initial count.
We observed retrospectively over 30 years of MCIs triggered in a large urban area. We considered three types of events: explosions, fires, and road traffic accidents. We collected the initial and final numbers of victims, with distinction between deaths, critical victims (T1), and delayed or minimal victims (T2-T3). The MF was calculated for each category of victims according to each type of event. Using a Poisson multivariate regression, we calculated the incidence risk ratio (IRR) of the final number of T1 as a function of the initial deaths and the initial T2-T3 counts, while controlling for potential confounding variables.
Sixty-eight MCIs were included. The final number of T1 increased with the initial incidence of deaths [IRR: 1.8 (1.4-2.2)], the initial number of T2-T3 being greater than 12 [IRR: 1.6 (1.3-2.1)], and the presence of one or more explosion [IRR: 1.4 (1.1-1.8)].
The MF seems to be an appealing decision-making tool to anticipate the need for ambulance resources. In explosive MCIs, we recommend multiplying T1 by 1.4 to estimate final count and the need for supplementary advanced life support teams.
每当发生大规模伤亡事件（MCI）时，预测最终受害者人数以派遣足够数量的救护车至关重要。在法国，习惯上通过将最初的院前受害者人数乘以 2-4 来预测最终人数。然而，目前还没有人验证这种乘法因子（MF）作为预测工具的准确性。我们旨在建立一个统计模型，根据初始人数预测最终受害者人数。
我们回顾性观察了在一个大城市地区发生的 30 多年的 MCI。我们考虑了三种类型的事件：爆炸、火灾和道路交通事故。我们收集了初始和最终受害者人数，区分了死亡人数、危急受害者（T1）和延迟或轻微受害者（T2-T3）。根据每种类型的事件，我们为每个受害者类别计算了 MF。使用泊松多变量回归，我们计算了最终 T1 数量的发病率风险比（IRR）作为初始死亡人数和初始 T2-T3 计数的函数，同时控制潜在的混杂变量。
共纳入 68 次 MCI。最终 T1 的数量随着初始死亡人数的增加而增加[IRR：1.8（1.4-2.2）]，T2-T3 的初始数量大于 12[IRR：1.6（1.3-2.1）]，以及存在一次或多次爆炸[IRR：1.4（1.1-1.8）]。
MF 似乎是一种有吸引力的决策工具，可以预测救护车资源的需求。在爆炸 MCI 中，我们建议将 T1 乘以 1.4 来估计最终人数和补充高级生命支持团队的需求。
