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.
Resuscitation. 2018 Feb;123:86-91. doi: 10.1016/j.resuscitation.2017.10.028. Epub 2017 Nov 21.
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.
Multicenter retrospective analysis of electronic health record data from all patients admitted to five US hospitals from November 2008-August 2013.
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.
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。
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。
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。
评分后 24 小时内发生心搏骤停、转入 ICU 或死亡。
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。
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。