接收医院对交通事故造成的大规模伤亡事件中受伤病人的管理和结局的影响。

IF 1
Neslihan Suzer, Gulbin Aydoğdu Umaç, Sarper Yilmaz
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引用次数: 0

摘要

背景:大规模伤亡事件(MCIs)的医疗管理需要从院前阶段到患者出院阶段策略性地应用分诊方法,以确保同时有效地治疗多名受伤人员。本研究旨在探讨交通事故导致MCIs的创伤患者前往三级医院的转运过程,并评估这些过程对患者预后的影响。方法:本回顾性研究调查了在一个省份的交通事故中受伤的创伤患者的院前、院间转院和住院过程。从多个角度进行综合分析。采用监督人工神经网络模型来预测患者死亡率,因为它能够识别高维临床数据中的复杂非线性模式。结果:共纳入606例患者。其中,212人(35.0%)在交通事故导致mci后转院至三级医院,394人(65.0%)在交通事故导致mci后直接入院。二次转院组院前时间较长(106.0 vs. 74.7分钟)。结论:在交通事故致MCIs后的创伤患者的处理中,将患者转院到缺乏足够创伤护理的医院会增加死亡率。继发性转运对血流动力学稳定性有负面影响,导致更需要输血、血管加压剂支持、大量输血和机械通气。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The impact of receiving hospitals on the management and outcomes of injured patients in traffic accidents causing mass casualty incidents.

The impact of receiving hospitals on the management and outcomes of injured patients in traffic accidents causing mass casualty incidents.

Background: The medical management of mass casualty incidents (MCIs) requires the strategic application of triage methods from the prehospital phase to patient discharge, ensuring the simultaneous and effective treatment of multiple injured individuals. This study aims to examine the transport processes of trauma patients to tertiary hospitals following traffic accidents that result in MCIs, and to evaluate the impact of these processes on patient outcomes.

Methods: This retrospective study investigates the prehospital, inter-hospital transfer, and in-hospital processes of trauma patients injured in traffic accidents causing MCIs over a five-year period within a single province. A comprehensive analysis was conducted from multiple perspectives. A supervised artificial neural network model was employed to predict patient mortality, selected for its ability to identify complex, non-linear patterns in high-dimensional clinical data.

Results: A total of 606 patients were included in the study. Of these, 212 (35.0%) underwent secondary transfer to a tertiary hospital, while 394 (65.0%) were directly admitted to a tertiary hospital following traffic accidents causing MCIs. The secondary transfer group experienced longer prehospital times (106.0 vs. 74.7 minutes, p<0.001) and received fewer correct triage decisions (75.0% vs. 92.4%, p<0.001). They also had higher rates of blood transfusion (60.8% vs. 38.8%, p<0.001), vasopressor use (43.9% vs. 22.1%, p<0.001), massive transfusion (36.8% vs. 19.0%, p<0.001), and mechanical ventilation (62.3% vs. 39.8%, p<0.001). In-hospital mortality was higher in the secondary transfer group (20.3%) compared to the direct admission group (8.1%), with an unadjusted odds ratio (OR) of 0.348 (95% confidence interval [CI]: 0.205-0.585, p<0.001). The trained neural network model demonstrated excellent predictive performance for mortality (Training area under the curve [AUC]: 0.947; 95% CI: 0.928-0.966, Testing AUC: 0.841; 95% CI: 0.782-0.899). A stratified analysis examining the impact of correct vs. incorrect triage decisions on mortality revealed that among correctly triaged patients, mortality was significantly higher in the secondary transfer group (22.6%) compared to direct tertiary admission (8.0%), with an OR of 3.38 (95% CI: 1.99-5.78, p<0.001). Overall, patients who underwent secondary transfer had a higher mortality risk compared to direct admissions (OR: 2.35; 95% CI: 1.12-5.10, p=0.0265). A direct comparison between all correctly and incorrectly triaged patients showed that correct triage significantly reduced mortality risk (OR: 4.19; 95% CI: 2.15-8.48, p<0.001).

Conclusion: In the management of trauma patients following traffic accidents causing MCIs, transferring patients to hospitals that lack adequate trauma care increases mortality. Secondary transport negatively affects hemodynamic stability and leads to a greater need for blood transfusion, vasopressor support, massive transfusion, and mechanical ventilation.

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