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解毒剂用量、给药时机及院前策略在神经毒剂大规模伤亡事件中的影响:一项模拟研究

Impact of antidote quantity, timing and prehospital strategies in nerve agent mass casualty events: a simulation study.

作者信息

De Rouck Ruben, Benhassine Mehdi, Debacker Michel, Van Utterbeeck Filip, Vaes Jan, Meskens Isabell, Hubloue Ives

机构信息

Research Group on Emergency and Disaster Medicine, Vrije Universiteit Brussel, Brussels, Belgium.

Simulation, Modelling, and Analysis of Complex Systems, Department of Mathematics, Royal Military Academy, Brussels, Belgium.

出版信息

Front Public Health. 2025 Aug 26;13:1640554. doi: 10.3389/fpubh.2025.1640554. eCollection 2025.

Abstract

INTRODUCTION

Mass casualty incidents (MCIs) involving nerve agents pose major challenges for emergency medical response due to rapid symptom onset, hazardous environments, and operational uncertainties. Several gaps remain in the knowledge about the prehospital response to nerve agent MCI treatment strategies and logistical decision-making. To address these gaps, this study uses Discrete Event Simulation to evaluate the impact of advanced medical stabilization (AMS) team arrival time, antidote availability, and evacuation policy on patient survival during an urban chemical-traumatic MCI with a subway sarin release scenario.

METHODS

A validated simulation model (SIMEDIS) was adapted to represent the full prehospital response chain, including triage, antidote administration, AMS, dry decontamination, further on-site stabilization in the forward medical post and transport to categorized hospitals. Two transport policies were modeled: Scoop&Run (rapid transport of victims to hospitals) and Stay&Play (on-site stabilization before transport). We simulated various AMS team arrival times and antidote availability scenarios to assess their impact on survival. Locations of deaths were analyzed to identify critical points of failure in the medical response chain.

RESULTS

AMS team arrival time, antidote availability, and evacuation policy significantly influenced mortality among the 25 salvageable victims. The number of deaths ranged from 8.0 (32%) in the most favorable case to 23.8 (95.2%) in the least favorable. Earlier AMS team arrival and greater antidote availability were associated with fewer deaths, particularly under the Scoop&Run policy. Stay&Play resulted in more deaths unless medical and transport capacity were significantly enhanced. Location-of-death analysis revealed preventable bottlenecks, especially during decontamination and hospital transport under the Stay&Play model.

DISCUSSION

The results highlight the importance of rapid hospital transport, swift antidote availability and administration during urban chemical MCIs. AMS team arrival time emerged as the strongest predictor of preventable mortality, showing a sigmoid-shaped curve where delays beyond 11 min led to sharp increases in death. Antidote supply showed a dose-dependent effect, but the impact diminishes with delayed administration, underscoring the need for timely delivery over sheer volume. To reduce preventable deaths in chemical MCIs, policy makers should focus on streamlining AMS team deployment, prioritizing rapid evacuation, and addressing logistical bottlenecks in decontamination and transport.

摘要

引言

涉及神经毒剂的大规模伤亡事件(MCI)给紧急医疗应对带来了重大挑战,原因包括症状迅速发作、环境危险以及操作的不确定性。在关于神经毒剂MCI的院前应对、治疗策略和后勤决策方面,仍存在一些知识空白。为填补这些空白,本研究使用离散事件模拟来评估在地铁沙林泄漏场景下的城市化学创伤性MCI中,高级医疗稳定(AMS)团队到达时间、解毒剂可用性和疏散政策对患者生存的影响。

方法

采用经过验证的模拟模型(SIMEDIS)来代表完整的院前应对链,包括分诊、解毒剂给药、AMS、干式去污、在前哨医疗站进行进一步现场稳定以及转运至分类医院。模拟了两种转运政策:抢先转运(将受害者快速转运至医院)和原地救治(转运前在现场进行稳定)。我们模拟了不同的AMS团队到达时间和解毒剂可用性场景,以评估它们对生存的影响。分析死亡地点以确定医疗应对链中的关键失败点。

结果

AMS团队到达时间、解毒剂可用性和疏散政策对25名可救治受害者的死亡率有显著影响。死亡人数从最有利情况下的8.0(32%)到最不利情况下的23.8(95.2%)不等。AMS团队更早到达和解毒剂可用性更高与死亡人数减少相关,尤其是在抢先转运政策下。原地救治导致更多死亡,除非医疗和转运能力得到显著增强。死亡地点分析揭示了可预防的瓶颈,特别是在原地救治模式下的去污和医院转运过程中。

讨论

结果凸显了在城市化学MCI中快速医院转运、迅速提供和使用解毒剂的重要性。AMS团队到达时间成为可预防死亡率的最强预测因素,呈现出S形曲线,超过11分钟的延迟会导致死亡人数急剧增加。解毒剂供应显示出剂量依赖性效应,但随着给药延迟影响会减弱,这突出了及时交付而非单纯数量的必要性。为减少化学MCI中的可预防死亡,政策制定者应专注于优化AMS团队部署、优先进行快速疏散以及解决去污和转运中的后勤瓶颈。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/479d/12417396/0702208c0dd3/fpubh-13-1640554-g001.jpg

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