Emergency department early mortality model for patients admitted after presenting to a tertiary medical center emergency department.

IF 3.4 3区医学 Q1 EMERGENCY MEDICINE Academic Emergency Medicine Pub Date : 2025-01-16 DOI:10.1111/acem.15096

Justin K Brooten, Jaime L Speiser, Jennifer L Gabbard, David P Miller, Simon A Mahler, Adam S Turner, Rebecca L Omlor, Michelle M Mielke, David M Cline

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Abstract

Objectives: Identifying patients in the emergency department (ED) at higher risk for in-hospital mortality can inform shared decision making and goals-of-care discussions. Electronic health record systems allow for integrated multivariable logistic regression (LR) modeling, which can provide early predictions of mortality risk in time for crucial decision making during a patient's initial care. Many commonly used LR models require blood gas analysis values, which are not frequently obtained in the ED. The goal of this study was to develop an all-cause mortality prediction model, derived from commonly collected ED data, which can assess mortality risk early in ED care.

Methods: Data were obtained for all patients, age 18 and older, admitted from the ED to Atrium Health Wake Forest Baptist from April 1, 2016, through March 31, 2020. Initial vital signs including heart rate, respiratory rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse oximetry, weight, body mass index, comprehensive metabolic panel, and a complete blood count were electronically retrieved for all patients. The prediction model was developed using LR. The ED early mortality (EDEM) model was compared with the rapid Emergency Medicine Score (REMS) for performance analysis.

Results: A total of 45,004 patients met inclusion criteria, comprising a total of 77,117 admissions. In this cohort, 52.8% of patients were male and 47.2% were female. The model used 35 variables and yielded an area under the receiver operating characteristic curve (AUC) of 0.889 (95% CI 0.874-0.905) with a sensitivity of 0.828 (95% CI 0.791-0.860), a specificity of 0.788 (95% CI 0.783-0.794), a negative predictive value of 0.995 (95% CI 0.994-0.996), and a positive predictive value of 0.084 (95% CI 0.076-0.092). This outperformed REMS in this data set, which yielded an AUC of 0.500 (95% CI 0.455-0.545).

Conclusions: The EDEM model was predictive of in-hospital mortality and was superior to REMS.

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急诊科对三级医疗中心急诊科入院患者的早期死亡率模型。

目的：识别急诊科（ED）中住院死亡风险较高的患者可以为共同决策和护理目标讨论提供信息。电子健康记录系统允许集成多变量逻辑回归（LR）建模，可以及时提供死亡风险的早期预测，以便在患者初始护理期间做出关键决策。许多常用的LR模型需要血气分析值，而这些值在急诊科中并不常见。本研究的目的是建立一个全因死亡率预测模型，该模型来源于通常收集的急诊科数据，可以评估急诊科早期的死亡风险。方法：数据来自2016年4月1日至2020年3月31日期间从急诊科入住Atrium Health Wake Forest Baptist的所有18岁及以上患者。所有患者的初始生命体征包括心率、呼吸频率、收缩压、舒张压、平均动脉压、脉搏血氧饱和度、体重、体重指数、综合代谢指数和全血细胞计数。采用LR建立预测模型。将急症早期死亡率（EDEM）模型与快速急诊医学评分（REMS）模型进行性能分析。结果：共有45,004例患者符合纳入标准，其中77,117例入院。在该队列中，52.8%的患者为男性，47.2%为女性。该模型使用35个变量，得出受试者工作特征曲线下面积（AUC）为0.889 (95% CI 0.874-0.905)，敏感性为0.828 (95% CI 0.791-0.860)，特异性为0.788 (95% CI 0.783-0.794)，阴性预测值为0.995 (95% CI 0.994-0.996)，阳性预测值为0.084 （95% CI 0.076-0.092）。这优于该数据集中的REMS，其AUC为0.500 （95% CI 0.455-0.545）。结论：EDEM模型预测住院死亡率优于REMS。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.