Literature DB >> 30245079

Derivation of a screen to identify severe sepsis and septic shock in the ED-BOMBARD vs. SIRS and qSOFA.

Steven G Rothrock1, David D Cassidy2, Drew Bienvenu3, Erich Heine3, Brian Guetschow3, Joshua G Briscoe4, Sean F Isaak5, Kenneth Chang3, Mikaela Devaux3.   

Abstract

STUDY
OBJECTIVE: To predict severe sepsis/septic shock in ED patients.
METHODS: We conducted a retrospective case-control study of patients ≥18 admitted to two urban hospitals with a combined ED census of 162,000. Study cases included patients with severe sepsis/septic shock admitted via the ED. Controls comprised admissions without severe sepsis/septic shock. Using multivariate logistic regression, a prediction rule was constructed. The model's AUROC was internally validated using 1000 bootstrap samples.
RESULTS: 143 study and 286 control patients were evaluated. Features predictive of severe sepsis/septic shock included: SBP ≤ 110 mm Hg, shock index/SI ≥ 0.86, abnormal mental status or GCS < 15, respirations ≥ 22, temperature ≥ 38C, assisted living facility residency, disabled immunity. Two points were assigned to SI and temperature with other features assigned one point (mnemonic: BOMBARD). BOMBARD was superior to SIRS criteria (AUROC 0.860 vs. 0.798, 0.062 difference, 95% CI 0.022-0.102) and qSOFA scores (0.860 vs. 0.742, 0.118 difference, 95% CI 0.081-0.155) at predicting severe sepsis/septic shock. A BOMBARD score ≥ 3 was more sensitive than SIRS ≥ 2 (74.8% vs. 49%, 25.9% difference, 95% CI 18.7-33.1) and qSOFA ≥ 2 (74.8% vs. 33.6%, 41.2% difference, 95% CI 33.2-49.3) at predicting severe sepsis/septic shock. A BOMBARD score ≥ 3 was superior to SIRS ≥ 2 (76% vs. 45%, 32% difference, 95% CI 10-50) and qSOFA ≥ 2 (76% vs. 29%, 47% difference, 95% CI 25-63) at predicting sepsis mortality.
CONCLUSION: BOMBARD was more accurate than SIRS and qSOFA at predicting severe sepsis/septic shock and sepsis mortality.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Year:  2018        PMID: 30245079     DOI: 10.1016/j.ajem.2018.09.023

Source DB:  PubMed          Journal:  Am J Emerg Med        ISSN: 0735-6757            Impact factor:   2.469


  1 in total

1.  Predicting outcome of patients with severe urinary tract infections admitted via the emergency department.

Authors:  Steven G Rothrock; David D Cassidy; Brian Guetschow; Drew Bienvenu; Erich Heine; Joshua Briscoe; Nicholas Toselli; Michelle Russin; Daniel Young; Caitlin Premuroso; David Bailey
Journal:  J Am Coll Emerg Physicians Open       Date:  2020-06-21
  1 in total

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