A prediction model for prehospital clinical deterioration: The use of early warning scores.

IF 3.4 3区 医学 Q1 EMERGENCY MEDICINE
Academic Emergency Medicine Pub Date : 2024-11-01 Epub Date: 2024-06-11 DOI:10.1111/acem.14963
Emma Bourke-Matas, Tan Doan, Kelly-Ann Bowles, Emma Bosley
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引用次数: 0

Abstract

Background: Various prognosticative approaches to assist in recognizing clinical deterioration have been proposed. To date, early warning scores (EWSs) have been evaluated in hospital with limited research investigating their suitability in the prehospital setting. This study evaluated the predictive ability of established EWSs and other clinical factors for prehospital clinical deterioration.

Methods: A retrospective cohort study investigating adult patients of all etiologies attended by Queensland Ambulance Service paramedics between January 1, 2018, and December 31, 2020, was conducted. With logistic regression, several models were developed to predict adverse event outcomes. The National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), Queensland Adult Deterioration Detection System (Q-ADDS), and shock index were calculated from vital signs taken by paramedics.

Results: A total of 1,422,046 incidents met the inclusion criteria. NEWS, MEWS, and Q-ADDS were found to have comparably high predictive ability with area under the receiver operating characteristic curve (AUC-ROC) between 70% and 90%, whereas shock index had relatively low AUC-ROC. Sensitivity was lower than specificity for all models. Although established EWSs performed well when predicting adverse events, these scores require complex calculations requiring multiple vital signs that may not be suitable for the prehospital setting.

Conclusions: This study found NEWS, MEWS, and Q-ADDS all performed well in the prehospital setting. Although a simple shock index is easier for paramedics to use in the prehospital environment, it did not perform comparably to established EWSs. Further research is required to develop suitably performing parsimonious solutions until established EWSs are integrated into technological solutions to be used by prehospital clinicians in real time.

院前临床恶化预测模型:使用预警评分。
背景:人们提出了各种预后方法来帮助识别临床恶化。迄今为止,早期预警评分(EWS)都是在医院中进行评估的,对其在院前环境中的适用性研究有限。本研究评估了已建立的 EWS 和其他临床因素对院前临床恶化的预测能力:本研究对 2018 年 1 月 1 日至 2020 年 12 月 31 日期间昆士兰救护服务局护理人员接诊的各种病因的成年患者进行了回顾性队列研究。通过逻辑回归,建立了多个模型来预测不良事件的结果。根据救护人员采集的生命体征计算出国家预警评分(NEWS)、修正预警评分(MEWS)、昆士兰成人病情恶化检测系统(Q-ADS)和休克指数:共有 1,422,046 起事件符合纳入标准。结果发现,NEWS、MEWS 和 Q-ADDS 具有相当高的预测能力,其接收器操作特征曲线下面积 (AUC-ROC) 在 70% 到 90% 之间,而休克指数的接收器操作特征曲线下面积 (AUC-ROC) 相对较低。所有模型的灵敏度均低于特异性。虽然已有的 EWS 在预测不良事件时表现良好,但这些评分需要进行复杂的计算,需要多个生命体征,可能不适合院前环境:本研究发现,NEWS、MEWS 和 Q-ADDS 在院前环境中均表现良好。虽然简单的休克指数更便于医护人员在院前环境中使用,但其表现却无法与已有的 EWS 相比。在已有的 EWS 被整合到技术解决方案中供院前临床医生实时使用之前,还需要进一步的研究,以开发出性能合适的简易解决方案。
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来源期刊
Academic Emergency Medicine
Academic Emergency Medicine 医学-急救医学
CiteScore
7.60
自引率
6.80%
发文量
207
审稿时长
3-8 weeks
期刊介绍: Academic Emergency Medicine (AEM) is the official monthly publication of the Society for Academic Emergency Medicine (SAEM) and publishes information relevant to the practice, educational advancements, and investigation of emergency medicine. It is the second-largest peer-reviewed scientific journal in the specialty of emergency medicine. The goal of AEM is to advance the science, education, and clinical practice of emergency medicine, to serve as a voice for the academic emergency medicine community, and to promote SAEM''s goals and objectives. Members and non-members worldwide depend on this journal for translational medicine relevant to emergency medicine, as well as for clinical news, case studies and more. Each issue contains information relevant to the research, educational advancements, and practice in emergency medicine. Subject matter is diverse, including preclinical studies, clinical topics, health policy, and educational methods. The research of SAEM members contributes significantly to the scientific content and development of the journal.
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