Antimicrobial Stewardship and Intensive Care Unit Mortality: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Antimicrobial stewardship programs (ASPs) using audit and feedback in the intensive care unit (ICU) setting can reduce harms related to inappropriate antibiotic use. However, inappropriate discontinuation or narrowing of antibiotic treatment could increase infection-related mortality in this population. Individual ASP studies are underpowered to detect differences in mortality. METHODS: We conducted a systematic review and meta-analysis of audit and feedback in the ICU setting, using mortality as our outcome. RESULTS: Of 2447 citations, 11 studies met our inclusion criteria. Although a variety of study designs were used to assess reductions in antibiotic use, mortality was analyzed using an uncontrolled before-after study design in all studies. Five studies directed audit and feedback to all or most ICU patients receiving antibiotics and measured overall ICU mortality. In the meta-analysis of these studies, the pooled relative risk of ICU mortality was 1.03 (95% confidence interval, .93-1.14). A second meta-analysis of 3 smaller studies that evaluated mortality only in patients directly assessed by the ASP found a pooled relative risk of ICU mortality of 1.06 (95% confidence interval, .80 to 1.4). Three studies were not appropriate for meta-analysis, but their results were consistent with our overall findings. CONCLUSIONS: Our systematic review did not identify a change in mortality associated with antimicrobial stewardship using audit and feedback in the ICU setting. These results increase our confidence that audit and feedback can be safely implemented in this setting. Future studies should report standardized estimates of mortality and use more robust study designs to assess mortality, when feasible.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".