Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, 405 30, Gothenburg, Sweden.
Gothenburg Emergency Medicine Research Group (GEMREG), Sahlgrenska Academy, 413 45, Gothenburg, Sweden.
Scand J Trauma Resusc Emerg Med. 2023 Nov 28;31(1):88. doi: 10.1186/s13049-023-01128-3.
Mass casualty incidents (MCI) pose significant challenges to existing resources, entailing multiagency collaboration. Triage is a critical component in the management of MCIs, but the lack of a universally accepted triage system can hinder collaboration and lead to preventable loss of life. This multinational study uses validated patient cards (cases) based on real MCIs to evaluate the feasibility and effectiveness of a novel Translational Triage Tool (TTT) in primary triage assessment of mass casualty victims.
Using established triage systems versus TTT, 163 participants (1575 times) triaged five patient cases. The outcomes were statistically compared.
TTT demonstrated similar sensitivity to the Sieve primary triage method and higher sensitivity than the START primary triage system. However, the TTT algorithm had a lower specificity compared to Sieve and higher over-triage rates. Nevertheless, the TTT algorithm demonstrated several advantages due to its straightforward design, such as rapid assessment, without the need for additional instrumental interventions, enabling the engagement of non-medical personnel.
The TTT algorithm is a promising and feasible primary triage tool for MCIs. The high number of over-triages potentially impacts resource allocation, but the absence of under-triages eliminates preventable deaths and enables the use of other personal resources. Further research involving larger participant samples, time efficiency assessments, and real-world scenarios is needed to fully assess the TTT algorithm's practicality and effectiveness in diverse multiagency and multinational contexts.
大规模伤亡事件(MCI)对现有资源构成重大挑战,需要多机构合作。分类是 MCI 管理的关键组成部分,但缺乏普遍接受的分类系统会阻碍合作,并导致可预防的生命损失。这项多国家研究使用基于真实 MCI 的经过验证的患者卡片(病例),评估新型转化分类工具(TTT)在大规模伤亡患者初步分类评估中的可行性和有效性。
使用既定的分类系统与 TTT,163 名参与者(1575 次)对 5 个患者病例进行分类。对结果进行了统计学比较。
TTT 与 Sieve 初步分类方法的敏感性相似,且对 START 初步分类系统的敏感性更高。然而,与 Sieve 相比,TTT 算法的特异性较低,过度分类率较高。尽管如此,由于其设计简单,TTT 算法具有快速评估等优势,无需额外的仪器干预,能够让非医疗人员参与。
TTT 算法是一种有前途且可行的 MCI 初步分类工具。高比例的过度分类可能会影响资源分配,但不存在漏分类可消除可预防的死亡,并能够利用其他人员资源。需要进一步研究,涉及更大的参与者样本、时间效率评估和真实场景,以充分评估 TTT 算法在不同多机构和多国家环境中的实用性和有效性。