The impact of a sepsis performance improvement program in the emergency department: a before–after intervention study
Bibliographic record
Abstract
PURPOSE: The latest Surviving Sepsis Campaign guidelines advocate that all hospitals use sepsis performance improvement programs. However, there is a limited evidence about how to structure such programs and what their potential impact is on sepsis management and outcomes in the emergency department (ED). In this study, we evaluated the implementation of a sepsis performance improvement program in the ED including a dedicated sepsis response team and analyzed the management and outcomes of sepsis patients before and after. METHODS: We conducted a before-after interventional study in the ED of the Amsterdam University Medical Centers, the Netherlands. The sepsis performance improvement program included regular educational meetings, daily audits and weekly feedback, a screening tool, and a dedicated multidisciplinary sepsis response team. We studied all adult patients who presented to the ED with a suspected infection and a Modified Early Warning Score (MEWS) ≥ 3 during their stay. In the postintervention phase, these patients were seen by the sepsis team. Process-related and patient-related outcomes were measured between November 2019 and February 2020 (preintervention) and December 2021-May 2022 (postintervention). RESULTS: A total of 265 patients were included in the primary study, 132 patients preintervention and 133 patients postintervention. The postintervention phase was associated with improvements in nearly all process-related outcomes, such as a shorter time to antibiotics (66 vs. 143 min; p < 0.001), increased number of lactate measurements (72.9 vs. 46.2%; p < 0.001), and improved completeness of documented MEWS scores (85.0 vs. 62.9%; p < 0.001). Except for an improvement in the number of immediate versus delayed ICU admissions (100% immediate vs. 64.3% immediate; p = 0.012), there was no improvement in the other patient-related outcomes such as 28 days mortality (14.3 vs. 9.1%; p = 0.261), during the postintervention phase. CONCLUSION: Our program stimulated physicians to make timely decisions regarding diagnostics and treatment of sepsis in the ED. Implementing the sepsis performance improvement program was associated with significant improvements in most process-related outcomes but with minimal improvements in patient-related outcomes in our cohort.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".