School of Nursing, Rangsit University, Pathumthani, Thailand.
College of Digital Innovation and Information Technology, Rangsit University, Pathumthani, Thailand.
Australas Emerg Care. 2022 Jun;25(2):121-125. doi: 10.1016/j.auec.2021.09.002. Epub 2021 Oct 23.
Nurses play a key role as the first line of service for patients with medical conditions and injuries in the emergency department (ED), which includes assessing patients for sepsis. The researchers evaluated tools to examine the performance of the Simple Clinical Score (SCS) and the Rapid Emergency Medicine Score (REMS) to predict sepsis severity and mortality among sepsis patients in the ED. A retrospective survey was performed, selecting participants by using a purposive sampling method, and including the medical records of all patients diagnosed with sepsis admitted to the ED at Singburi Hospital, Thailand. Data were analysed using the ROC curve and the Area Under Curve (AUC) to calculate the accuracy of each patient's mortality prediction. A total of 225 patients diagnosed with sepsis was identified, with a mortality rate of 59.11% after admission to the medical service and intensive care unit. The AUC analysis showed that the accuracy of the model generated from the REMS (88.6%) was higher than that of the SCS (76.7%). The authors also recommend that key variables identified in this research should be used to develop screening and assessment tools for sepsis in the context of the ED.
护士在急诊科（ED）中扮演着重要的角色，是为有医疗状况和受伤的患者提供服务的第一线人员，其中包括评估患者是否患有败血症。研究人员评估了用于检查简单临床评分（SCS）和快速急诊医学评分（REMS）在 ED 中预测败血症患者严重程度和死亡率的性能的工具。采用目的抽样法进行回顾性调查，选择参与者，并纳入所有在泰国 Singburi 医院急诊科被诊断为败血症的患者的病历。使用 ROC 曲线和曲线下面积（AUC）分析来计算每个患者死亡率预测的准确性。共确定了 225 名被诊断为败血症的患者，他们在入住医疗服务和重症监护病房后的死亡率为 59.11%。AUC 分析显示，REMS 生成的模型的准确性（88.6%）高于 SCS（76.7%）。作者还建议，应根据 ED 的情况，使用本研究中确定的关键变量来开发败血症的筛查和评估工具。
