Fan Cheng-Yi, Huang Edward Pei-Chuan, Huang Chun-Hsiang, Huang Sih-Shiang, Huang Chien-Tai, Ho Yi-Ju, Chen Ching-Yu, Chen Chi-Hsin, Lien Chun-Ju, Chang Wei-Tien, Sung Chih-Wei
Department of Emergency Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan; Institute of Molecular Medicine, National Tsing Hua University, Hsinchu, Taiwan.
Department of Emergency Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan; Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan; Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
Am J Emerg Med. 2025 Jul;93:57-63. doi: 10.1016/j.ajem.2025.03.048. Epub 2025 Mar 24.
Although three established models for predicting the return of spontaneous circulation (ROSC) in out-of-hospital cardiac arrest (OHCA) exist, combinational external validation of these models remains limited. This study aimed to externally validate and compare the performance of three predictive models-RACA, P-ROSC, and UB-ROSC-and provide evidence to guide the selection and application of predictive models for prehospital ROSC in diverse settings.
A retrospective validation was conducted using the National Taiwan University Hospital Hsinchu and Yunlin Branch Out-of-Hospital Cardiac Arrest Research Databases. Patients with EMS-treated OHCAs admitted to the hospital between January 2016 and July 2023 were recruited. The primary outcome was prehospital ROSC. Model performance was evaluated using discrimination, calibration, sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic odds ratio. Calibration and density distribution plots were generated.
All three models demonstrated moderate-to-high discrimination with AUROCs of 0.758 (RACA), 0.755 (P-ROSC), and 0.747 (UB-ROSC). The RACA score exhibited better calibration across the risk deciles, whereas the P-ROSC and UB-ROSC scores tended to overestimate the probabilities at higher predicted risk levels. The P-ROSC score required fewer variables and showed the best separation between prehospital and non-prehospital ROSC cases. Optimal cut-off values for the RACA, P-ROSC, and UB-ROSC scores were 0.45, 41, and - 13, respectively, with corresponding sensitivities of 62 %, 56 %, and 71 % and specificities of 78 %, 82 %, and 69 %. All models achieved high NPVs (>96 %), but PPVs remained low (16-21 %).
The P-ROSC, which requires fewer variables, has emerged as the most practical model for Taiwanese populations. However, the choice of the model should be guided by the availability of variables, regional EMS characteristics, and trends in prehospital ROSC rates.
虽然存在三种用于预测院外心脏骤停（OHCA）自主循环恢复（ROSC）的既定模型，但对这些模型的联合外部验证仍然有限。本研究旨在对三种预测模型——RACA、P-ROSC和UB-ROSC——进行外部验证并比较其性能，并提供证据以指导在不同环境中选择和应用院前ROSC预测模型。
使用国立台湾大学医院新竹和云林分院院外心脏骤停研究数据库进行回顾性验证。招募了2016年1月至2023年7月期间因院外心脏骤停接受紧急医疗服务（EMS）治疗并入院的患者。主要结局是院前ROSC。使用鉴别力、校准度、敏感性、特异性、阳性预测值、阴性预测值和诊断比值比评估模型性能。生成校准和密度分布图。
所有三种模型均表现出中度至高度的鉴别力，RACA的曲线下面积（AUROC）为0.758，P-ROSC为0.755，UB-ROSC为0.747。RACA评分在各风险十分位数中表现出更好的校准度，而P-ROSC和UB-ROSC评分在预测风险水平较高时往往高估概率。P-ROSC评分所需变量较少，并且在院前和非院前ROSC病例之间表现出最佳区分度。RACA、P-ROSC和UB-ROSC评分的最佳截断值分别为0.45、41和 - 13，相应的敏感性分别为62%、56%和71%，特异性分别为78%、82%和69%。所有模型均实现了较高的阴性预测值（>96%），但阳性预测值仍然较低（16 - 21%）。
所需变量较少的P-ROSC已成为台湾人群中最实用的模型。然而，模型的选择应根据变量的可用性、区域EMS特征以及院前ROSC率的趋势来指导。
