Utility of Previous Culture Results for Guiding Empirical Treatment of Sepsis in The Emergency Department
Bibliographic record
Abstract
Background: Sepsis is a serious medical condition and a major cause of morbidity and mortality, and poses challenges in terms of recognition and management. Although studies have investigated the early identification of sepsis and early use of broad-spectrum antibiotics, no clear criteria exist to identify those patients needing additional coverage for resistant organisms. Aims: This study aims to evaluate the utility of previous positive blood or urine culture results in predicting the presence of resistant organisms in septic patients in the emergency department (ED). Methods: This retrospective observational study was conducted at King Fahad Medical City (KFMC), a tertiary care centre in Riyadh, Saudi Arabia, between March and August 2021. Patients aged 18 years or older, who visited the ED at KFMC during the study period, were included if they had a positive blood or urine culture and met the sepsis definition. Result: A total of 133 patients were enrolled (mean age 61.6 [18.3] years), of whom approximately half were male (67, 50.4%). We found that previous colonisation with resistant organisms was more likely in patients with resistant organisms at the time of the enrolled visit (n = 17, 77.3%) than in patients with non-resistant organisms (n = 22, 19.8%, p < .05). Therefore, one statically significant predictor of a current resistant organism is a prior colonisation with a resistant organism (OR = 13.8; 95% CIs 3.6, 51.9; p < .05). Conclusion: Previous cultures, from within the last 12 months, are useful predictors of current resistant organisms, and are therefore essential in guiding empirical antibiotic treatment in septic patients in the ED. Further more extensive and prospective cohort studies on this subject are now needed to mitigate the burden of sepsis on healthcare systems worldwide.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".