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.
Original language | English |
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Pages (from-to) | 848-856 |
Number of pages | 9 |
Journal | EMA - Emergency Medicine Australasia |
Volume | 33 |
Issue number | 5 |
Early online date | 23 Feb 2021 |
DOIs | |
Publication status | Published - Oct 2021 |
Keywords
- algorithm
- decision support system
- emergency service
- hospital
- sepsis
- systemic inflammatory response syndrome