Strategies to reduce 28-day mortality in adult patients with bacteremia in the emergency department
Bibliographic record
Abstract
BACKGROUND: Bacteremia, a common emergency department presentation, has a high burden of mortality, cost and morbidity. We aimed to identify areas for potential improvement in emergency department bacteremia management. METHODS: This retrospective cohort study included adults with bacteremia in an emergency department in 2019 and 2022. The primary outcome was 28-day mortality. Descriptive analyses evaluated demographics, comorbidities and clinical characteristics. Univariate and multivariate analyses identified mortality predictors. RESULTS: Overall, 433 patients were included [217 males (50.1%), mean ± SD age: 74.1 ± 15.2 years]. The 28-day mortality rate was 15.2% (n = 66). In univariate analysis, age ≥ 70 years, arrival by ambulance, arrhythmia, congestive heart failure, recent steroid use, hypotension (< 90/60 mmHg), mechanical ventilation, cardiac arrest, intensive care unit (ICU) admission, intravenous antibiotics, pneumonia as bacteremia source, non-urinary tract infections, no infectious disease consultation, no antibiotic adjustment and no control blood cultures were significantly associated with 28-day mortality (p < 0.05). Malignancy showed a statistical trend (0.05 < p < 0.15). The above-stated sixteen variables, identified in univariate analysis, were assessed via multivariate analysis. Primarily, clinical relevance and, secondarily, statistical significance were used for multivariate model creation to prioritize pertinent variables. Five risk factors, significantly associated with mortality (p < 0.05), were included in the model: ICU admission [adjusted OR (95% CI): 6.03 (3.08-11.81)], pneumonia as bacteremia source [4.94 (2.62-9.32)], age ≥ 70 [3.16 (1.39-7.17)], hypotension [2.12 (1.02-4.40)], and no infectious disease consultation [2.02 (1.08-3.78)]). Surprisingly, initial antibiotic administration within 6 h, inappropriate initial antibiotic regimen and type of bacteria (Gram-negative, Gram-positive) were non-significant (p > 0.05). CONCLUSIONS: We identified significant mortality predictors among emergency department patients presenting with bacteremia. Referral to an infectious disease physician is the only modifiable strategy to decrease 28-day mortality with long-term effect and should be prioritized.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".