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本文引用的文献

1
Derivation of a cardiac arrest prediction model using ward vital signs*.基于病房生命体征的心脏骤停预测模型的推导*。
Crit Care Med. 2012 Jul;40(7):2102-8. doi: 10.1097/CCM.0b013e318250aa5a.
2
Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record.非重症监护患者生理恶化即将发生的早期检测:使用自动化电子病历数据开发预测模型。
J Hosp Med. 2012 May-Jun;7(5):388-95. doi: 10.1002/jhm.1929. Epub 2012 Mar 22.
3
Accuracy of an expanded early warning score for patients in general and trauma surgery wards.一般外科和创伤外科病房中扩展早期预警评分的准确性。
Br J Surg. 2012 Feb;99(2):192-7. doi: 10.1002/bjs.7777. Epub 2011 Dec 20.
4
Validation of a modified pediatric early warning system score: a retrospective case-control study.改良版儿科早期预警系统评分的验证:一项回顾性病例对照研究。
Clin Pediatr (Phila). 2012 May;51(5):431-5. doi: 10.1177/0009922811430342. Epub 2011 Dec 8.
5
Predicting cardiac arrest on the wards: a nested case-control study.预测病房中的心脏骤停:巢式病例对照研究。
Chest. 2012 May;141(5):1170-1176. doi: 10.1378/chest.11-1301. Epub 2011 Nov 3.
6
Validation of an abbreviated Vitalpac™ Early Warning Score (ViEWS) in 75,419 consecutive admissions to a Canadian regional hospital.验证 Vitalpac™ 简化早期预警评分(ViEWS)在加拿大一家地区医院的 75419 例连续入院患者中的应用。
Resuscitation. 2012 Mar;83(3):297-302. doi: 10.1016/j.resuscitation.2011.08.022. Epub 2011 Sep 10.
7
Longitudinal analysis of one million vital signs in patients in an academic medical center.对学术医疗中心 100 万患者生命体征的纵向分析。
Resuscitation. 2011 Nov;82(11):1387-92. doi: 10.1016/j.resuscitation.2011.06.033. Epub 2011 Jul 3.
8
Clinical deterioration in the condition of patients with acute medical illness in Australian hospitals: improving detection and response.澳大利亚医院急性内科疾病患者病情恶化:改善检测和应对。
Med J Aust. 2011 Jun 6;194(11):596-8. doi: 10.5694/j.1326-5377.2011.tb03113.x.
9
Centile-based early warning scores derived from statistical distributions of vital signs.基于百分位的生命体征统计分布衍生的早期预警评分。
Resuscitation. 2011 Aug;82(8):1013-8. doi: 10.1016/j.resuscitation.2011.03.006. Epub 2011 Mar 23.
10
The use of combined physiological parameters in the early recognition of the deteriorating acute medical patient.联合生理参数在急性内科患者病情恶化早期识别中的应用
J R Coll Physicians Edinb. 2010 Mar;40(1):19-25. doi: 10.4997/JRCPE.2010.105.

住院患者的风险分层。

Risk stratification of hospitalized patients on the wards.

机构信息

Section of Pulmonary and Critical Care, University of Chicago, Chicago, IL; Health Studies Department, University of Chicago, Chicago, IL.

Section of Hospital Medicine, University of Chicago, Chicago, IL.

出版信息

Chest. 2013 Jun;143(6):1758-1765. doi: 10.1378/chest.12-1605.

DOI:10.1378/chest.12-1605
PMID:23732586
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3673668/
Abstract

Patients who suffer adverse events on the wards, such as cardiac arrest and death, often have vital sign abnormalities hours before the event. Early warning scores have been developed with the aim of identifying clinical deterioration early and have been recommended by the National Institute for Health and Clinical Excellence. In this review, we discuss recently developed and validated risk scores for use on the general inpatient wards. In addition, we compare newly developed systems with more established risk scores such as the Modified Early Warning Score and the criteria used in the Medical Early Response Intervention and Therapy (MERIT) trial in our database of > 59,000 ward admissions. In general we found the single-parameter systems, such as the MERIT criteria, to have the lowest predictive accuracy for adverse events, whereas the aggregate weighted scoring systems had the highest. The Cardiac Arrest Risk Triage (CART) score was best for predicting cardiac arrest, ICU transfer, and a composite outcome (area under the receiver operating characteristic curve [AUC], 0.83, 0.77, and 0.78, respectively), whereas the Standardized Early Warning Score, VitalPAC Early Warning Score, and CART score were similar for predicting mortality (AUC, 0.88). Selection of a risk score for a hospital or health-care system should be guided by available variables, calculation method, and system resources. Once implemented, ensuring high levels of adherence and tying them to specific levels of interventions, such as activation of a rapid response team, are necessary to allow for the greatest potential to improve patient outcomes.

摘要

病房中出现不良事件(如心脏骤停和死亡)的患者,通常在事件发生前数小时就会出现生命体征异常。早期预警评分系统旨在早期识别临床恶化情况,已被英国国家卫生与临床优化研究所推荐。在本次综述中,我们讨论了最近开发并验证的适用于普通住院病房的风险评分系统。此外,我们还将新开发的系统与更成熟的风险评分系统(如改良早期预警评分和 MERIT 试验标准)进行了比较,该数据库包含了我们的 59,000 多例病房入院患者。总体而言,我们发现单参数系统(如 MERIT 标准)对不良事件的预测准确性最低,而综合加权评分系统的预测准确性最高。心脏骤停风险分层(CART)评分在预测心脏骤停、转入 ICU 和复合结局方面表现最佳(接受者操作特征曲线下面积[AUROC]分别为 0.83、0.77 和 0.78),而标准化早期预警评分、VitalPAC 早期预警评分和 CART 评分在预测死亡率方面表现相似(AUROC 为 0.88)。医院或医疗保健系统应根据可用变量、计算方法和系统资源来选择风险评分。一旦实施,确保高水平的依从性并将其与特定级别的干预措施(如快速反应团队的激活)联系起来,对于提高患者预后的潜力至关重要。