Suppr超能文献

对比 Flags 呼叫标准与 MEWS、NEWS 和电子心搏骤停风险分诊 (eCART) 评分在识别病房恶化患者中的作用。

Comparison of the Between the Flags calling criteria to the MEWS, NEWS and the electronic Cardiac Arrest Risk Triage (eCART) score for the identification of deteriorating ward patients.

机构信息

Clinical Excellence Commission, Level 17 McKell Building, 2-24 Rawson Place, Sydney 2000, New South Wales, Australia.

Clinical Excellence Commission, Level 17 McKell Building, 2-24 Rawson Place, Sydney 2000, New South Wales, Australia.

出版信息

Resuscitation. 2018 Feb;123:86-91. doi: 10.1016/j.resuscitation.2017.10.028. Epub 2017 Nov 21.

Abstract

INTRODUCTION

Traditionally, paper based observation charts have been used to identify deteriorating patients, with emerging recent electronic medical records allowing electronic algorithms to risk stratify and help direct the response to deterioration.

OBJECTIVE(S): We sought to compare the Between the Flags (BTF) calling criteria to the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS) and electronic Cardiac Arrest Risk Triage (eCART) score.

DESIGN AND PARTICIPANTS

Multicenter retrospective analysis of electronic health record data from all patients admitted to five US hospitals from November 2008-August 2013.

MAIN OUTCOME MEASURES

Cardiac arrest, ICU transfer or death within 24h of a score RESULTS: Overall accuracy was highest for eCART, with an AUC of 0.801 (95% CI 0.799-0.802), followed by NEWS, MEWS and BTF respectively (0.718 [0.716-0.720]; 0.698 [0.696-0.700]; 0.663 [0.661-0.664]). BTF criteria had a high risk (Red Zone) specificity of 95.0% and a moderate risk (Yellow Zone) specificity of 27.5%, which corresponded to MEWS thresholds of >=4 and >=2, NEWS thresholds of >=5 and >=2, and eCART thresholds of >=12 and >=4, respectively. At those thresholds, eCART caught 22 more adverse events per 10,000 patients than BTF using the moderate risk criteria and 13 more using high risk criteria, while MEWS and NEWS identified the same or fewer.

CONCLUSION(S): An electronically generated eCART score was more accurate than commonly used paper based observation tools for predicting the composite outcome of in-hospital cardiac arrest, ICU transfer and death within 24h of observation. The outcomes of this analysis lend weight for a move towards an algorithm based electronic risk identification tool for deteriorating patients to ensure earlier detection and prevent adverse events in the hospital.

摘要

简介

传统上,使用纸质观察图表来识别病情恶化的患者,而新兴的电子病历系统则允许使用电子算法进行风险分层,并有助于指导对病情恶化的反应。

目的

我们旨在比较“Between the Flags(BTF)”呼叫标准与改良早期预警评分(MEWS)、国家早期预警评分(NEWS)和电子心搏骤停风险分诊(eCART)评分。

设计和参与者

这是一项多中心回顾性分析,使用了 2008 年 11 月至 2013 年 8 月期间来自美国 5 家医院的所有住院患者的电子病历数据。

主要观察指标

评分后 24 小时内发生心搏骤停、转入 ICU 或死亡。

结果

总体而言,eCART 的准确性最高,AUC 为 0.801(95%CI 0.799-0.802),其次是 NEWS、MEWS 和 BTF,分别为 0.718(0.716-0.720)、0.698(0.696-0.700)和 0.663(0.661-0.664)。BTF 标准的高危(红色区域)特异性为 95.0%,中危(黄色区域)特异性为 27.5%,与 MEWS 评分≥4 和≥2、NEWS 评分≥5 和≥2、eCART 评分≥12 和≥4 相对应。在这些阈值下,与 BTF 相比,使用中度风险标准,eCART 每 10000 例患者多发现 22 例不良事件,而使用高危标准则多发现 13 例不良事件,而 MEWS 和 NEWS 则发现相同或更少的不良事件。

结论

与常用的纸质观察工具相比,电子生成的 eCART 评分更能准确预测观察后 24 小时内院内心搏骤停、转入 ICU 和死亡的综合结局。本分析结果为转向基于算法的电子风险识别工具以识别病情恶化患者提供了依据,以确保更早地发现并预防医院内的不良事件。

相似文献

2
Predicting clinical deterioration with Q-ADDS compared to NEWS, Between the Flags, and eCART track and trigger tools.
Resuscitation. 2020 Aug;153:28-34. doi: 10.1016/j.resuscitation.2020.05.027. Epub 2020 Jun 3.
4
Comparison of early warning scores for predicting clinical deterioration and infection in obstetric patients.
BMC Pregnancy Childbirth. 2022 Apr 6;22(1):295. doi: 10.1186/s12884-022-04631-0.
5
Early Warning Scores With and Without Artificial Intelligence.
JAMA Netw Open. 2024 Oct 1;7(10):e2438986. doi: 10.1001/jamanetworkopen.2024.38986.
7
Early Deterioration Indicator: Data-driven approach to detecting deterioration in general ward.
Resuscitation. 2018 Jan;122:99-105. doi: 10.1016/j.resuscitation.2017.10.026. Epub 2017 Nov 6.

引用本文的文献

2
Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review.
Biomed Eng Lett. 2025 Jun 25;15(4):717-734. doi: 10.1007/s13534-025-00486-4. eCollection 2025 Jul.
3
Unplanned transfers from wards to intensive care units: how well does NEWS identify patients in need of urgent escalation of care?
Scand J Trauma Resusc Emerg Med. 2025 Jun 13;33(1):105. doi: 10.1186/s13049-025-01371-w.
5
2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association.
Circulation. 2025 Feb 25;151(8):e41-e660. doi: 10.1161/CIR.0000000000001303. Epub 2025 Jan 27.
8
Enhancing Clinical Cardiac Care: Predicting In-Hospital Cardiac Arrest With Machine Learning.
Ann Lab Med. 2025 Mar 1;45(2):117-120. doi: 10.3343/alm.2024.0696. Epub 2025 Jan 8.
9
Use of a continuous single lead electrocardiogram analytic to predict patient deterioration requiring rapid response team activation.
PLOS Digit Health. 2024 Oct 24;3(10):e0000465. doi: 10.1371/journal.pdig.0000465. eCollection 2024 Oct.
10
Wearable devices as part of postoperative early warning score systems: a scoping review.
J Clin Monit Comput. 2025 Feb;39(1):233-244. doi: 10.1007/s10877-024-01224-4. Epub 2024 Oct 8.

本文引用的文献

2
Impact of a standardized rapid response system on outcomes in a large healthcare jurisdiction.
Resuscitation. 2016 Oct;107:47-56. doi: 10.1016/j.resuscitation.2016.07.240. Epub 2016 Aug 6.
3
Real-Time Automated Sampling of Electronic Medical Records Predicts Hospital Mortality.
Am J Med. 2016 Jul;129(7):688-698.e2. doi: 10.1016/j.amjmed.2016.02.037. Epub 2016 Mar 24.
4
Are observation selection methods important when comparing early warning score performance?
Resuscitation. 2015 May;90:1-6. doi: 10.1016/j.resuscitation.2015.01.033. Epub 2015 Feb 8.
6
Multicenter development and validation of a risk stratification tool for ward patients.
Am J Respir Crit Care Med. 2014 Sep 15;190(6):649-55. doi: 10.1164/rccm.201406-1022OC.
7
'Between the flags': implementing a rapid response system at scale.
BMJ Qual Saf. 2014 Sep;23(9):714-7. doi: 10.1136/bmjqs-2014-002845. Epub 2014 Apr 16.
8
Risk stratification of hospitalized patients on the wards.
Chest. 2013 Jun;143(6):1758-1765. doi: 10.1378/chest.12-1605.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验