Comparison of Five Triage Tools for Identifying Mortality Risk and Injury Severity of Multiple Trauma Patients Admitted to the Emergency Department in the Daytime and Nighttime: A Retrospective Study

作者
Youguo Ying,Bo-Li Huang,Yan Zhu,Xiaobin Jiang,Jinxiu Dong,Yanfen Ding,Lei Wang,Huimin Yuan,Ping Jiang
出处
期刊:Applied Bionics and Biomechanics [Hindawi Publishing Corporation]
卷期号:2022: 1-11 被引量:6
标识
DOI:10.1155/2022/9368920
摘要

Effective triage tools are indispensable for doctors to make a prompt decision for the treatment of multiple trauma patients in emergency departments (EDs). The Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), standardized early warning score (SEWS), Modified Rapid Emergency Medicine Score (mREMS), and Revised Trauma Score (RTS) are five common triage tools proposed for trauma management. However, few studies have compared these tools in a multiple trauma cohort and investigated the influence of nighttime admission on the performance of these tools. This retrospective study was aimed at evaluating and comparing the performance of MEWS, NEWS, SEWS, mREMS, and RTS for identifying the mortality risk and trauma severity of patients with multiple trauma admitted to the ED during the daytime and nighttime. Retrospective data were collected from the medical records of patients with multiple trauma admitted in the daytime or nighttime to calculate scores for each triage tool. Logistic regression analysis was conducted on each triage tool for identifying in-hospital mortality and severe trauma (injury severity score > 15 ) in the daytime and nighttime. The performance of the tools was evaluated and compared by calculating area under the receiver operating characteristic curve (AUROC) of the retrospective logistic model of each tool. We collected data for 1,818 admissions, including 1,070 daytime and 748 nighttime admissions. A comparison of performance for identifying in-hospital mortality between daytime and nighttime yielded the following results (AUROC): MEWS (0.95 vs. 0.93, p = 0.384 ), NEWS (0.95 vs. 0.94, p = 0.708 ), SEWS (0.95 vs. 0.94, p = 0.683 ), mREMS (0.94 vs. 0.92, p = 0.286 ), and RTS (0.93 vs. 0.93, p = 0.87 ). Similarly, a comparison of performance for identifying trauma severity between daytime and nighttime yielded the following results (AUROC): MEWS (0.78 vs. 0.78, p = 0.95 ), NEWS (0.8 vs. 0.8, p = 0.885 ), SEWS (0.78 vs. 0.78, p = 0.818 ), mREMS (0.75 vs. 0.69, p = 0.019 ), and RTS (0.75 vs. 0.74, p = 0.619 ). All five scores are excellent triage tools ( AUROC 0.9 ) for identifying in-hospital mortality for both daytime and nighttime admissions. However, they have only moderate effectiveness ( AUROC < 0.9 ) at identifying severe trauma. The NEWS is the best triage tool for identifying severe trauma for both daytime and nighttime admissions. The MEWS, NEWS, SEWS, and RTS exhibited no significant differences in performance for identifying in-hospital mortality or severe trauma during the daytime or nighttime. However, the mREMS was better at identifying severe trauma during the daytime.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
ypj9777完成签到,获得积分10
刚刚
zh发布了新的文献求助10
1秒前
Lyeming完成签到,获得积分10
1秒前
脑洞疼应助XH采纳,获得10
1秒前
3秒前
Hwtttt完成签到,获得积分10
3秒前
建安风骨发布了新的文献求助10
4秒前
大方妙旋发布了新的文献求助10
4秒前
4秒前
Orange应助丰富的宛筠采纳,获得10
4秒前
cantabile完成签到,获得积分10
5秒前
5秒前
6秒前
醉熏的蚂蚁完成签到,获得积分10
7秒前
好好好发布了新的文献求助10
7秒前
小叶完成签到 ,获得积分10
7秒前
8秒前
时尚的靖完成签到 ,获得积分10
8秒前
lili发布了新的文献求助10
8秒前
8秒前
8秒前
ddddd发布了新的文献求助10
9秒前
9秒前
12345678发布了新的文献求助10
10秒前
10秒前
yi应助临时演员采纳,获得10
11秒前
陈隆发布了新的文献求助10
11秒前
luym发布了新的文献求助10
11秒前
科研通AI6.4应助QIAN采纳,获得10
12秒前
12秒前
12秒前
愉快惜寒发布了新的文献求助10
13秒前
汉堡包应助科研通管家采纳,获得10
13秒前
Jasper应助科研通管家采纳,获得10
13秒前
13秒前
Hello应助科研通管家采纳,获得10
13秒前
简单千秋发布了新的文献求助10
13秒前
14秒前
submarines发布了新的文献求助100
14秒前
无花果应助科研通管家采纳,获得10
14秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Principles of town planning: translating concepts to applications 1000
2016 Venous Blood Study (VBS) (Final V3.0) 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The Effective Clinical Neurologist 3ed 500
The Great Hymn to Šamaš 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7699388
求助须知:如何正确求助?哪些是违规求助? 9258701
关于积分的说明 20015754
捐赠科研通 7274521
什么是DOI,文献DOI怎么找? 3293487
关于科研通互助平台的介绍 2448934
邀请新用户注册赠送积分活动 2299794