Audit and Feedback to Reduce Broad-Spectrum Antibiotic Use among Intensive Care Unit Patients A Controlled Interrupted Time Series Analysis
Bibliographic record
Abstract
OBJECTIVE: We aimed to rigorously evaluate the impact of prospective audit and feedback on broad-spectrum antimicrobial use among critical care patients. DESIGN: Prospective, controlled interrupted time series. SETTING: Single tertiary care center with 3 intensive care units. PATIENTS AND INTERVENTIONS: A formal review of all critical care patients on their third or tenth day of broad-spectrum antibiotic therapy was conducted, and suggestions for antimicrobial optimization were communicated to the critical care team. OUTCOMES: The primary outcome was broad-spectrum antibiotic use (days of therapy per 1000 patient-days; secondary outcomes included overall antibiotic use, gram-negative bacterial susceptibility, nosocomial Clostridium difficile infections, length of stay, and mortality. RESULTS: The mean monthly broad-spectrum antibiotic use decreased from 644 days of therapy per 1,000 patient-days in the preintervention period to 503 days of therapy per 1,000 patient-days in the postintervention period (P < .0001); time series modeling confirmed an immediate decrease (± standard error) of 119 ± 37.9 days of therapy per 1,000 patient-days (P = .0054). In contrast, no changes were identified in the use of broad-spectrum antibiotics in the control group (nonintervention medical and surgical wards) or in the use of control medications in critical care (stress ulcer prophylaxis). The incidence of nosocomial C. difficile infections decreased from 11 to 6 cases in the study intensive care units, whereas the incidence increased from 87 to 116 cases in the control wards (P = .04). Overall gram-negative susceptibility to meropenem increased in the critical care units. Intensive care unit length of stay and mortality did not change. CONCLUSIONS: Institution of a formal prospective audit and feedback program appears to be a safe and effective means to improve broad-spectrum antimicrobial use in critical care.
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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 | 0.040 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".