Differential outcome of an antimicrobial stewardship audit and feedback program in two intensive care units: a controlled interrupted time series study
Bibliographic record
Abstract
Abstract Background Antimicrobial decision making in intensive care units (ICUs) is challenging. Unnecessary antimicrobials contribute to the development of resistant pathogens, Clostridium difficile infection and drug related adverse events. However, inadequate antimicrobial therapy is associated with mortality in critically ill patients. Antimicrobial stewardship programs are increasingly being implemented to improve antimicrobial prescribing, but the optimal approach in the ICU setting is unknown. We assessed the impact of an audit and feedback antimicrobial stewardship intervention on antimicrobial use, antimicrobial costs, clinical outcomes and microbiologic outcomes in two ICUs with different patient populations. Methods The audit and feedback intervention was implemented in a trauma and neurosurgery ICU (TNICU) and a medical surgical ICU (MSICU) at a 465-bed teaching hospital in Toronto, Canada. ICU patients were reviewed Monday to Friday by a physician and pharmacist with infectious diseases training. Recommendations related to appropriate antimicrobial use were presented to ICU teams during a dedicated daily meeting. A controlled interrupted time series analysis was used to compare outcomes in the 12 months before and after the intervention. Cardiovascular and coronary care ICUs served as control units. Results Mean total monthly antimicrobial use in defined daily doses (DDD) per 1000 patient days was reduced 28 % in the TNICU (1433 vs. 1037) but increased 14 % in the MSICU (1705 vs. 1936). In the time series analysis, total monthly antimicrobial use in the TNICU decreased by 375 DDD per 1000 patient days (p
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".