Antimicrobial use in a critical care unit: a prospective observational study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to describe antimicrobial utilization, consumption, indications and microbial resistance in a medical-surgical-trauma intensive care unit (ICU) of a teaching hospital to identify potential targets for antimicrobial stewardship. METHODS: This was a 30-day prospective observational study enrolling adults admitted to the ICU for at least 24 h and having received antimicrobial therapy. Primary endpoints included utilization as percentage use of antimicrobials by class and agent, consumption measured as days of therapy per 1000 patient days (DOT/1000PD), indications for use and prescriber. Secondary endpoints included reasons for modifications to therapy and microbial resistance. KEY FINDINGS: Eighty-three patients were screened and 61 enrolled, receiving 133 courses of antimicrobial therapy, mainly intravenously and prescribed by ICU staff. The most frequently prescribed agents were piperacillin/tazobactam (20%), cefazolin (17%) and vancomycin (13%). The indications for therapy were empirical (50%), directed (27%) and prophylactic (23%). Overall consumption was 1368.54 DOT/1000PD and was mainly attributed to empirical therapy (734.25). Prolonged durations were noted for carbapenems and for surgical prophylaxis. There were 86 therapy modifications involving indication (36), efficacy (25), safety (18) and route (7). Suboptimal or excessive dosing were common contributors to efficacy and safety modifications, respectively. Infections due to microorganisms with notable resistance included methicillin-resistant Staphylococcus aureus (5), Pseudomonas aeruginosa (1) and Streptococcus pneumoniae (1). CONCLUSIONS: Antimicrobial utilization and consumption based on DOT/1000PD were prospectively determined providing a comparator for other ICUs. Potential targets identified for antimicrobial stewardship initiatives include empirical therapy, treatment duration, dosing and route.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".