Linking antimicrobial prescribing to antimicrobial resistance in the ICU: Before and after an antimicrobial stewardship program
Bibliographic record
Abstract
Antimicrobials are an effective treatment for many types of infections, but their overuse promotes the spread of resistant microorganisms that defy conventional treatments and complicate patient care. In 2009, an antimicrobial stewardship program was implemented at Mount Sinai Hospital (MSH, Toronto, Canada). Components of this program were to alter the fraction of patients prescribed antimicrobials, to shorten the average duration of treatment, and to alter the types of antimicrobials prescribed. These components were incorporated into a mathematical model that was compared to data reporting the number of patients colonized with Pseudomonas aeruginosa and the number of patients colonized with antimicrobial-resistant P. aeruginosa first isolates before and after the antimicrobial stewardship program. Our analysis shows that the reported decrease in the number of patients colonized was due to treating fewer patients, while the reported decrease in the number of patients colonized with resistant P. aeruginosa was due to the combined effect of treating fewer patients and altering the types of antimicrobials prescribed. We also find that shortening the average duration of treatment was unlikely to have produced any noticeable effects and that further reducing the fraction of patients prescribed antimicrobials would most substantially reduce P. aeruginosa antimicrobial resistance in the future. The analytical framework that we derive considers the effect of colonization pressure on infection spread and can be used to interpret clinical antimicrobial resistance data to assess different aspects of antimicrobial stewardship within the ecological context of the intensive care unit.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".