Department of Emergency, Far Eastern Memorial Hospital, 21 Sec. 2, Nanya S. Rd., New Taipei City, Taiwan 220.
Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Rd., Chung-Li, Taiwan 32003.
Am J Emerg Med. 2018 Jul;36(7):1222-1230. doi: 10.1016/j.ajem.2017.12.026. Epub 2017 Dec 13.
This study aimed to develop a triage tool to more effectively triage possible ACS patients presenting to the emergency department (ED) before admission to a protocol-driven chest pain unit (CPU).
Seven hundred ninety-three clinical cases, randomly selected from 7962 possible ACS cases, were used to develop and test an ACS triage model using cluster analysis and stepwise logistic regression.
The ACS triage model, logit (suspected ACS patient)=-5.283+1.894×chest pain+1.612×age+1.222×male+0.958×proximal radiation pain+0.962×shock+0.519×acute heart failure, with a threshold value set at 2.5, was developed to triage patients. Compared to four existing methods, the chest-pain strategy, the Zarich's strategy, the flowchart, and the heart broken index (HBI), the ACS triage model had better performance.
This study developed an ACS triage model for triaging possible ACS patients. The model could be used as a rapid tool in EDs to reduce the workloads of ED nurses and physicians in relation to admissions to the CPU.
本研究旨在开发一种分诊工具，以便在将疑似 ACS 患者收入以协议为导向的胸痛单元（CPU）之前，更有效地对就诊于急诊科（ED）的可能 ACS 患者进行分诊。
从 7962 例可能的 ACS 病例中随机抽取 793 例临床病例，使用聚类分析和逐步逻辑回归方法来开发和测试 ACS 分诊模型。
ACS 分诊模型，logit（疑似 ACS 患者）=-5.283+1.894×胸痛+1.612×年龄+1.222×男性+0.958×近端辐射痛+0.962×休克+0.519×急性心力衰竭，阈值设为 2.5，用于对患者进行分诊。与四个现有的方法，即胸痛策略、Zarich 策略、流程图和心碎指数（HBI）相比，ACS 分诊模型具有更好的性能。
本研究开发了一种 ACS 分诊模型，用于对疑似 ACS 患者进行分诊。该模型可作为 ED 中的一种快速工具，以减轻 ED 护士和医生与 CPU 入院相关的工作量。
