Trial reportPloS one2020
Institution of an emergency department "swarming" care model and sepsis door-to-antibiotic time: A quasi-experimental retrospective analysis.
Trial report in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03226366 (Effect of Emergency Department Care Reorganization on Door-to-antibiotic Times for Sepsis), which is not on this map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Effect of Emergency Department Care Reorganization on Door-to-antibiotic Times for Sepsis (LDS SWARM)
Who cites it
1 citing paper in PubMed.
- Sepsis Diagnostic Excellence: Development and Validation of a Theoretical Framework for Conceptualizing Accurate, Timely Sepsis Diagnosis.Critical care explorations · 2026Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundPrompt sepsis treatment is associated with improved outcomes but requires a complex series of actions by multiple clinicians. We investigated whether simply reorganizing emergency department (ED) care to expedite patients' initial evaluation was associated with shorter sepsis door-to-antibiotic times.
methodsPatients eligible for this retrospective study received IV antibiotics and demonstrated acute organ failure after presenting to one of three EDs in Utah. On May 1, 2016, the intervention ED instituted "swarming" as the default model for initial evaluation of all mid- and low-acuity patients. Swarming involved simultaneous patient evaluation by the ED physician, nurse, and technician followed by a team discussion of the initial care plan. Care was unchanged at the two control EDs. A 30-day wash-in period separated the baseline (May 16, 2015 to April 15, 2016) and post-intervention (May 16, 2016 to November 15, 2016) analysis periods. We conducted a quasi-experimental analysis comparing door-to-antibiotic time for sepsis patients at the intervention ED after versus before care reorganization, applying difference-in-differences methods to control for trends in door-to-antibiotic time unrelated to the studied intervention and multivariable regression to adjust for patient characteristics.
resultsThe analysis included 3,230 ED sepsis patients, including 1,406 from the intervention ED. Adjusted analyses using difference-in-differences methods to control for temporal trends unrelated to the studied intervention revealed no significant change in door-to-antibiotic time after care reorganization (-7 minutes, 95% CI -20 to 6 minutes, p = 0.29). Multivariable pre/post analyses using data only from the intervention ED overestimated the magnitude and statistical significance of outcome changes associated with ED care reorganization.
conclusionsImplementation of an ED care model involving parallel multidisciplinary assessment and early team discussion of the care plan was not associated with improvements in mid- and low-acuity sepsis patients' door-to-antibiotic time after accounting for changes in the outcome unrelated to the studied intervention.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.