Vichiensanth Piraya, Leepayakhun Kantawat, Yuksen Chaiyaporn, Jenpanitpong Chetsadakon, Seesuklom Suteenun
Division of Emergency Medicine, Department of Emergency Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand.
Division of Paramedicine, Department of Emergency Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand.
Arch Acad Emerg Med. 2025 Mar 10;13(1):e37. doi: 10.22037/aaemj.v13i1.2581. eCollection 2025.
Delays in accessing an appropriate level of care can lead to significant morbidity or even mortality of trauma patients. This study aimed to develop a simplified prehospital predictive model to determine the need for tertiary care trauma centers (TTC), enabling timely and appropriate transport decisions by emergency medical service (EMS) teams.
This is a retrospective cohort study conducted at the emergency department (ED) of Ramathibodi Hospital between January 2020 and April 2024. Prehospital trauma patients aged ≥15 years who were transported by EMS were included in the study. Patients were divided into two groups with and without the need for TTC, and the independent predictive factors of the need for TTC were explored using multivariable regression analysis.
The study included 440 trauma patients, with 31.1% requiring TTC. The predictors of the need for TTC included age (coefficient (Coef.) -0.003; 95% confidence interval (CI): -0.018 to 0.012; P=0.693), traffic mechanism (Coef. 0.848; 95%CI: 0.150 to 1.546; P=0.017), respiratory rate (Coef. 0.044; 95%CI: -0.037 to 1.124; P=0.285), heart rate (Coef. -0.004; 95%CI: -0.020 to 0.012; P=0.610), and Glasgow Coma Scale (Coef. -0.312; 95%CI: -0.451 to -0.173; P<0.001). The predictive model categorized patients into low, moderate, and high-risk groups. Patients who were categorized in the high-risk group showed a positive likelihood ratio (LHR+) of 14.88 for requiring TTC. The model achieved an area under the receiver operating characteristic curve (AuROC) of 73%, indicating the good discriminative ability of this prediction model.
The predictive model classifies trauma patients into three risk groups based on five prognostic variables, which are able to predict the likelihood of requiring TTC. Internal validation has verified its high level of accuracy in trauma triage.
获得适当医疗护理水平的延迟可能导致创伤患者出现严重发病甚至死亡。本研究旨在开发一种简化的院前预测模型，以确定对三级创伤中心（TTC）的需求，使紧急医疗服务（EMS）团队能够及时做出适当的转运决策。
这是一项在2020年1月至2024年4月期间于拉玛蒂博迪医院急诊科进行的回顾性队列研究。纳入了由EMS转运的年龄≥15岁的院前创伤患者。患者被分为需要和不需要TTC的两组，并使用多变量回归分析探索TTC需求的独立预测因素。
该研究纳入了440例创伤患者，其中31.1%需要TTC。TTC需求的预测因素包括年龄（系数（Coef.）-0.003；95%置信区间（CI）：-0.018至0.012；P = 0.693）、交通伤机制（Coef. 0.848；95%CI：0.150至1.546；P = 0.017）、呼吸频率（Coef. 0.044；95%CI：-0.037至1.124；P = 0.285）、心率（Coef. -0.004；95%CI：-0.020至0.012；P = 0.610）和格拉斯哥昏迷量表（Coef. -0.312；95%CI：-0.451至-0.173；P < 0.001）。该预测模型将患者分为低、中、高风险组。被归类为高风险组的患者需要TTC的阳性似然比（LHR +）为14.88。该模型在受试者工作特征曲线下的面积（AuROC）为73%，表明该预测模型具有良好的判别能力。
该预测模型基于五个预后变量将创伤患者分为三个风险组，能够预测需要TTC的可能性。内部验证已证实其在创伤分诊中的高度准确性。
