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Original Article
Incidence and risk factors associated with early death in patients with emergency department septic shock
Matthew S. Reaven, Nigel L. Rozario, Maggie S. J. McCarter, Alan C. Heffner
Acute Crit Care. 2022;37(2):193-201.   Published online February 11, 2022
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  • 3 Web of Science
  • 3 Crossref
AbstractAbstract PDF
Limited research has explored early mortality among patients presenting with septic shock. The objective of this study was to determine the incidence and factors associated with early death following emergency department (ED) presentation of septic shock.
A prospective registry of patients enrolled in an ED septic shock clinical pathway was used to identify patients. Patients were compared across demographic, comorbid, clinical, and treatment variables by death within 72 hours of ED presentation.
Among the sample of 2,414 patients, overall hospital mortality was 20.6%. Among patients who died in the hospital, mean and median time from ED presentation to death were 4.96 days and 2.28 days, respectively. Death at 24, 48, and 72 hours occurred in 5.5%, 9.5%, and 11.5% of patients, respectively. Multivariate regression analysis demonstrated that the following factors were independently associated with early mortality: age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.03–1.05), malignancy (OR, 1.53; 95% CI, 1.11–2.11), pneumonia (OR, 1.39; 95% CI, 1.02–1.88), urinary tract infection (OR, 0.63; 95% CI, 0.44–0.89), first shock index (OR, 1.85; 95% CI, 1.27–2.70), early vasopressor use (OR, 2.16; 95% CI, 1.60–2.92), initial international normalized ratio (OR, 1.14; 95% CI, 1.07–1.27), initial albumin (OR, 0.55; 95% CI, 0.44–0.69), and first serum lactate (OR, 1.21; 95% CI, 1.16–1.26).
Adult septic shock patients experience a high rate of early mortality within 72 hours of ED arrival. Recognizable clinical factors may aid the identification of patients at risk of early death.


Citations to this article as recorded by  
  • Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study
    Sang Won Park, Na Young Yeo, Seonguk Kang, Taejun Ha, Tae-Hoon Kim, DooHee Lee, Dowon Kim, Seheon Choi, Minkyu Kim, DongHoon Lee, DoHyeon Kim, Woo Jin Kim, Seung-Joon Lee, Yeon-Jeong Heo, Da Hye Moon, Seon-Sook Han, Yoon Kim, Hyun-Soo Choi, Dong Kyu Oh, S
    Journal of Korean Medical Science.2024;[Epub]     CrossRef
  • Predicting sepsis at emergency department triage: Implementing clinical and laboratory markers within the first nursing assessment to enhance diagnostic accuracy
    Ugo Giulio Sisto, Stefano Di Bella, Elisa Porta, Giorgia Franzoi, Franco Cominotto, Elena Guzzardi, Nicola Artusi, Caterina Anna Giudice, Eugenia Dal Bo, Nicholas Collot, Francesca Sirianni, Savino Russo, Gianfranco Sanson
    Journal of Nursing Scholarship.2024;[Epub]     CrossRef
  • Red cell distribution width and in‐hospital mortality in septic shock: A public database research
    Qiong Ding, Yingjie Su, Changluo Li, Ning Ding
    International Journal of Laboratory Hematology.2022; 44(5): 861.     CrossRef

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