Impact of an Unsolicited, Standardized Form–Based Antimicrobial Stewardship Intervention to Improve Guideline Adherence in the Management of<i>Staphylococcus aureus</i>Bacteremia
Bibliographic record
Abstract
Antimicrobial stewardship programs (ASPs) improve Staphylococcus aureus bacteremia (SAB) management. The objective of the current study was to evaluate the effect of unsolicited prospective audit and feedback (PAF) using a standardized SAB bundle form on the management of SAB. Multicenter, pre-post quasi-experimental study of inpatients with SAB. The ASP developed an evidence-based SAB management bundle that included recommendations for infectious diseases consultation, blood culture clearance, appropriate empiric and definitive therapy, echocardiography, adequate treatment duration, and source control where applicable. ASP pharmacists performed PAF using a standardized form outlining bundle components. The primary outcome was bundle component adherence. Secondary outcomes were length of stay, 30-day readmission rate, and in-hospital and 30-day mortality rates. A total of 199 patients were included (preintervention group, 62; intervention group, 137). Bundle implementation with PAF resulted in significant improvements in infectious diseases consultation (56.5% in preintervention vs 93.4% in intervention group), appropriate definitive antibiotic therapy (83.9% vs 99.3%), ordering echocardiography (72.6% vs 95.6%), and adequate treatment duration (87.0% vs 100%) (all P < .001). Overall bundle adherence increased by 43.8% (P < .001). Readmission and 30-day mortality rates decreased, but this difference did not reach statistical significance. Unsolicited PAF using a standardized SAB management bundle significantly improved adherence to evidence-based recommendations. This simple yet effective ASP-driven intervention can ensure consistent management of a highly morbid infection.
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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 | 0.004 | 0.013 |
| 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".