141Urine Culture Optimization: A Powerful Antimicrobial Stewardship Strategy
Bibliographic record
Abstract
Background. Inappropriate collection of urine cultures (UC) has the potential to increase the likelihood of antimicrobial prescription for asymptomatic bacteriuria. Antimicrobial overuse can lead to increased cost, mortality and morbidity related to antimicrobial resistance and Clostridium difficile. We postulated that a quality improvement strategy to optimize collection of UCs would effectively reduce the unnecessary use of antibiotics. Methods. The Antimicrobial Stewardship (ASP) team at our hospital initiated interventions aimed at optimizing UC collection in our Emergency Department(ED). Our interventions consisted of a creation of a Working Group involving the ED staff and an Infection Preventionist (IP) trained in Frontline Ownership (FLO) techniques. Thinking sessions involving staff were facilitated by the same IP utilizing FLO principles; the sessions reviewed process, policy and encouraged UC utilization dialogue. Session summaries and UC volume run charts were shared biweekly serving as continuous feedback to the ED. Antimicrobial use was determined through financial charge data and standardized as defined daily doses/1,000 patient days. Results. Pre-intervention UC rate was (0.09 per ED patient visit) compared to after intervention (0.06 per ED patient visit), representing a 24% reduction (p < 0.002) (Figure 1). Use of ciprofloxacillin in the ED from 9.8 to 8.0 (DDD/1,000 ED Visits) an 18 %(p = 0.02) reduction (Figure 2). Urine Culture (UC) testing reduction in the Emergency Department (ED) 2013 from pre-intervention UC rate was (0.09 per ED patient visit) compared to after intervention (0.06 per ED patient visit) a 24% reduction (p<0.002). A reduction of Ciprofloxacillin's Daily Defined Dose (DDD) for the Emergency Department in 2013 from pre-intervention mean 9.8 (DDD/1,000 ED Visits) to post intervention mean of 8.0(DDD/1,000 ED Visits) an 18 %( p=0.02) reduction Conclusion. Our intervention in the ED using FLO methodology, effectively reduced UC testing and in turn reduced the ciprofloxacin. Our novel intervention represents an upstream approach to ASP, capitalizing on frontline staff engagement in stewardship interventions. Optimizing microbiologic evaluation is an important stewardship tool to prevent inappropriate use of antimicrobial agents. Disclosures. All authors: No reported disclosures.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".