Detailed Piperacillin-Tazobactam Use Audit in a North American Tertiary Care Hospital—Not Necessarily Wrong, Not Quite Right: Directions for Stewardship
Bibliographic record
Abstract
Background. Antimicrobial resistance is a global concern and stewardship programs have been recommended as an evidence based method of improving prescribing. Piperacillin-tazobactam (PT) is the most common parenteral broad spectrum agent used in Canadian hospitals, increasing more than 10-fold from 2002 to 2014 (28.17 DDD/1000 to 295.46 DDD/1000 discharges). Given this increase and PT's broad spectrum, we decided to assess PT as a stewardship target. Methods. A prospective audit was performed, on all PT orders on adult inpatients 3 July–29 July 2015 at a large teaching tertiary hospital (651 adult beds) with chart review for demographics, clinical parameters, rationale for PT initiation, antimicrobials, pertinent cultures, and outcomes. Two investigators assessed guideline concordance with standard provincial guidelines, and whether concordant use was optimal (based on local guidelines suggesting other agents as first line), with resolution of differences by consensus review. Results. Of 149 new PT starts assessed, 64% were guideline concordant, 23%were not guideline concordant, and 13% were suboptimal, with services shown in graph. Of these, 49% consisted of one PT dose (primarily prescribed by ER and Medicine, 7/17 each). Thirty percent of PT use was not streamlined with culture receipt (62% ICU, 27% medical, and 11% surgical.) Common reasons for “suboptimal” usage included: no need for Gram-negative/anaerobic or anti-pseudomonal coverage, need for blood brain barrier penetration. Clinical and microbiologic data, and hospital PT use trends will be presented. See the figures. Conclusion. Overall, at least one third (given the broad interpretation of “appropriate” and “optimal” by local guidelines) of prescriptions would have benefited from a stewardship intervention, which would create an average of 8.7 PT assessments and 2.9 interventions per weekday for the duration of our study. “One-time” nonconcordant use was common, possibly arising from a defensive approach to initially unclear clinical diagnoses. Other areas for improvement include using guidelines for empiric therapy, ordering pertinent cultures, and streamlining. These data demonstrate a need to develop a range of PT stewardship interventions in hospital settings. 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".