Impact of antimicrobial stewardship in critical care: a systematic review
Bibliographic record
Abstract
OBJECTIVES: To evaluate the current state of evidence for antimicrobial stewardship interventions in the critical care unit. METHODS: We performed a systematic search of OVID MEDLINE, Embase and Cochrane electronic databases from 1996-2010. Studies were included if they involved any experimental intervention to improve antimicrobial utilization in the critical care setting. RESULTS: Thirty-eight studies met the inclusion criteria, of which 24 met our quality inclusion criteria. The quality of research was poor, with only 3 randomized controlled trials, 3 interrupted time series and 18 (75%) uncontrolled before-and-after studies. We identified six intervention types: studies of antibiotic restriction or pre-approval (six studies); formal infectious diseases physician consultation (five); implementation of guidelines or protocols for de-escalation (two); guidelines for antibiotic prophylaxis or treatment in intensive care (two); formal reassessment of antibiotics on a pre-specified day of therapy (three); and implementation of computer-assisted decision support (six). Stewardship interventions were associated with reductions in antimicrobial utilization (11%-38% defined daily doses/1000 patient-days), lower total antimicrobial costs (US$ 5-10/patient-day), shorter average duration of antibiotic therapy, less inappropriate use and fewer antibiotic adverse events. Stewardship interventions beyond 6 months were associated with reductions in antimicrobial resistance rates, although this differed by drug-pathogen combination. Antibiotic stewardship was not associated with increases in nosocomial infection rates, length of stay or mortality. CONCLUSIONS: More rigorous research is needed, but available evidence suggests that antimicrobial stewardship is associated with improved antimicrobial utilization in the intensive care unit, with corresponding improvements in antimicrobial resistance and adverse events, and without compromise of short-term clinical outcomes.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".