Patient Experience and System Performance Division, NSW Ministry of Health, Sydney, New South Wales, Australia.
Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, Sydney, New South Wales, Australia.
Emerg Med Australas. 2021 Oct;33(5):848-856. doi: 10.1111/1742-6723.13748. Epub 2021 Feb 23.
Electronic medical records-based alerts have shown mixed results in identifying ED sepsis. Augmenting clinical patient-flagging with automated alert systems may improve sepsis screening. We evaluate the performance of a hybrid alert to identify patients in ED with sepsis or in-hospital secondary outcomes from infection.
We extracted a dataset of all patients with sepsis during the study period at five participating Western Sydney EDs. We evaluated the hybrid alert's performance for identifying patients with a discharge diagnosis related to infection and modified sequential sepsis-related organ functional assessment (mSOFA) score ≥2 in ED and also compared the alert to rapid bedside screening tools to identify patients with infection for secondary outcomes of all-cause in-hospital death and/or intensive care unit admission.
A total of 118 178 adult patients presented to participating EDs during study period with 1546 patients meeting ED sepsis criteria. The hybrid alert had a sensitivity - 71.2% (95% confidence interval 68.8-73.4), specificity - 96.4% (95% confidence interval 96.3-96.5) for identifying ED sepsis. Clinician flagging identified additional alert-negative 232 ED sepsis and 63 patients with secondary outcomes and 112 alert-positive patients with infection and ED mSOFA score <2 went on to die in hospital.
The hybrid alert performed modestly in identifying ED sepsis and secondary outcomes from infection. Not all infected patients with a secondary outcome were identified by the alert or mSOFA score ≥2 threshold. Augmenting clinical practice with auto-alerts rather than pure automation should be considered as a potential for sepsis alerting until more reliable algorithms are available for safe use in clinical practice.
基于电子病历的警报在识别急诊科脓毒症方面的效果参差不齐。通过自动警报系统增强临床患者标记可能会改善脓毒症筛查。我们评估了混合警报在识别急诊科患有脓毒症或院内感染相关不良结局患者方面的性能。
我们从参与研究的五个西悉尼急诊科所有脓毒症患者中提取了一个数据集。我们评估了混合警报在识别急诊科有感染相关出院诊断和改良序贯器官衰竭评估(SOFA)评分≥2 的患者方面的性能,还比较了该警报与床边快速筛查工具,以识别感染患者是否存在全因院内死亡和/或重症监护病房入住的次要结局。
研究期间,共有 118178 名成年患者到参与急诊科就诊,其中 1546 名患者符合急诊科脓毒症标准。混合警报在识别急诊科脓毒症方面的敏感性为 71.2%(95%置信区间 68.8-73.4),特异性为 96.4%(95%置信区间 96.3-96.5)。临床医生标记确定了另外 232 例急诊科脓毒症和 63 例有不良结局的患者,以及 112 例警报阳性、感染且 ED SOFA 评分<2 的患者,这些患者最终在医院死亡。
混合警报在识别急诊科脓毒症和感染的不良结局方面表现中等。并非所有有不良结局的感染患者都能被警报或 SOFA 评分≥2 阈值识别。在更可靠的算法可安全用于临床实践之前,应考虑将自动警报与临床实践相结合,作为脓毒症警报的一种潜在手段,而不仅仅是纯粹的自动化。