Pediatric Antimicrobial Stewardship: It's as Easy as 1-2-3 (1 Hour, 2 People, 3 Times a Week)
Bibliographic record
Abstract
Background. Optimizing antimicrobial use through antimicrobial stewardship programs (ASP) in pediatric hospitals has been less urgent because of lower C. difficilerates and less overall resistance in pathogens typically seen in children. We sought to compare antimicrobial days of therapy (DOT) for patients preceding and following implementation of a prospective audit and feedback program. Methods. The ASP takes place at the Children's Hospital of Eastern Ontario, a 120 bed, tertiary care pediatric hospital in Ottawa, Canada. Our institutional ASP consisted of a prospective audit and feedback with an ID physician and pharmacist 3 times per week on two general pediatric medicine wards. Drug utilization reports (DUR) generated from the pharmacy database for all patients prescribed an antimicrobial and admitted under general pediatrics were used to obtain DOT which was calculated as “stop date” minus “start date”. The first 6 months after ASP implementation (4 August 2014 to 31 January 2015) was compared to the same 6-month period in the year preceding ASP implementation (1 August 2013 to 31 January 2014). A Poisson test was used to compare DOT/1000 patient days. Acceptance rates of common ASP recommendations were recorded. Results. DOT/1000 patient days was 512 in the pre-ASP period versus 420 in the post intervention period (−18% change; p < 0.001). Use of aminopenicillins increased by 18.7/1000 patient days (p = 0.004) post ASP whereas use of third generation cephalosporins decreased by 52.3/1000 patient days (p < 0.001) and clindamycin decreased by 12.4/1000 patient days (p < 0.001). The recommendation acceptance rate for targeted antimicrobials, clindamycin and second-/third-generation cephalosporins, was 63.6% and 77.5%, respectively. Conclusion. In the 6 months following ASP implementation, an 18% reduction in DOT/1000 patient days was observed. Most importantly, we decreased the use of targeted antimicrobials which have a broader spectrum and potentially are associated with more adverse events. Continued measurement of antimicrobial use after ASP implementation provides a means to assess the success of our ASP and evaluate its impact on prescribing practices. 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.014 | 0.002 |
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".