Management of Patients With Sepsis in Canadian Community Emergency Departments: A Retrospective Multicenter Observational Study
Bibliographic record
Abstract
BACKGROUND: Sepsis is a life-threatening syndrome and a leading cause of morbidity and mortality representing significant financial burden on the health-care system. Early identification and intervention is crucial to maximizing positive outcomes. We studied a quality improvement initiative with the aim of reviewing the initial management of patients with sepsis in Canadian community emergency departments, to identify areas for improving the delivery of sepsis care. We present a retrospective, multicenter, observational study during 2011 to 2015 in the community setting. METHODS: We collected data on baseline characteristics, clinical management metrics (triage-to-physician-assessment time, triage-to-lactate-drawn time, triage-to-antibiotic time, and volume of fluids administered within the first 6 hours of triage), and outcomes (intensive care unit [ICU] admission, in-hospital mortality) from a regional database. RESULTS: A total of 2056 patients were analyzed. The median triage-to-physician-assessment time was 50 minutes (interquartile range [IQR]: 25-104), triage-to-lactate-drawn time was 50 minutes (IQR: 63-94), and triage-to-antibiotics time was 129 minutes (IQR: 70-221). The median total amount of fluid administered within 6 hours of triage was 2.0 L (IQR: 1.5-3.0). The ICU admission rate was 36% and in-hospital mortality was 25%. We also observed a higher ICU admission rate (51% vs 24%) and in-hospital mortality (44% vs 14%) in those with higher lactate concentration (≥4 vs ≤2 mmol/L), independent of other sepsis-related parameters. CONCLUSION: Time-to-physician-assessment, time-to-lactate-drawn, time-to-antibiotics, and fluid resuscitation in community emergency departments could be improved. Future quality improvement interventions are required to optimize management of patients with sepsis. Elevated lactate concentration was also independently associated with ICU admission rate and in-hospital mortality rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".