Antimicrobial stewardship in the intensive care setting – a review and critical appraisal of the literature
Bibliographic record
Abstract
BACKGROUND: Many antimicrobial stewardship programmes (ASPs) target the intensive care unit owing to high antimicrobial utilisation. In this review, we summarise and assess the quality of evidence supporting the implementation of various ASP strategies in the intensive care unit setting with a focus on publications between 2010 and 2015. METHODS: We searched Medline up to April 2015 and screened publications of interest for additional relevant articles. We grouped the strategies into four categories: audit and feedback, formulary restrictions, guidelines/clinical pathways, and procalcitonin. We used GRADE terminology to describe the quality of evidence. RESULTS AND CONCLUSIONS: We identified several studies reporting optimisation and reduction of antibiotic utilisation as well as cost reduction in all four strategies. Randomised controlled trials reviewing the role of procalcitonin demonstrate a moderate level of evidence. Given the lack of randomised controlled trials to support the role of guidelines, formulary restrictions, and audit and feedback, the level of evidence supporting these strategies is low. Importantly, there is no convincing evidence to support the main goal of ASP, namely to improve patient outcomes. Larger, rigorous long-term studies using a cluster randomised controlled trial or at least a controlled quasi-experimental design with time series are required to assess the impact of ASP on patient-important outcomes and on the emergence of resistance in the intensive care unit setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".