Literature DB >> 33622028

Evaluation of an augmented emergency department electronic medical record-based sepsis alert.

Amith Shetty1,2,3,4, Margaret Murphy4,5, Catriona Middleton-Rennie6, Angelo Lancuba4, Malcolm Green6, Harvey Lander6, Mary Fullick6, Ling Li7, Jonathan Iredell2,3, Naren Gunja3,4.   

Abstract

OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
© 2021 Australasian College for Emergency Medicine.

Entities:  

Keywords:  algorithm; decision support system; emergency service; hospital; sepsis; systemic inflammatory response syndrome

Year:  2021        PMID: 33622028     DOI: 10.1111/1742-6723.13748

Source DB:  PubMed          Journal:  Emerg Med Australas        ISSN: 1742-6723            Impact factor:   2.151


  1 in total

Review 1.  Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review.

Authors:  Khalia Ackermann; Jannah Baker; Malcolm Green; Mary Fullick; Hilal Varinli; Johanna Westbrook; Ling Li
Journal:  J Med Internet Res       Date:  2022-02-23       Impact factor: 7.076

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.