1143. Measuring Up! Benchmarking Antimicrobial Use in Canadian Children’s Hospitals
Bibliographic record
Abstract
Abstract Background Inappropriate antimicrobial use (AU) is recognized as a leading cause of antimicrobial resistance. However, quantifying AU in hospitals is challenging due to variability in information systems. Point prevalence surveys (PPS) provide a means to quantify AU in a cross-sectional manner within and between institutions. The aim of the study was to describe and compare the prescription patterns of AU across pediatric hospitals in Canada using PPS. Methods Two PPS (November 2018 and February 2019) were conducted at each of the 15 Canadian pediatric hospitals. For each PPS, AU data were collected for all inpatients ≤ 18 years (excluded mental health and birthing units) on the survey date. Data, including admitting diagnosis, age, comorbidities, Infectious Diseases consult, admitting service, documented pathogen(s), and antimicrobial(s) prescribed, was collected and entered into a RedCap database. Results In total, we surveyed 3826 patient-days. The mean proportion of children receiving at least one antimicrobial was 35.2% [range 25.1% to 42.9%]. Of the 1951 antimicrobials prescribed, the most common were third-generation cephalosporins [3GC] (16%; 321), aminopenicillins (15%; 297), TMP-SMX (11%; 207), piperacillin–tazobactam (10%; 193) and first-generation cephalosporins (9%; 181). Overall, the frequency of carbapenems, quinolones and vancomycin use was 4% (79), 3% (65) and 8% (151), respectively. Of the antimicrobials used for targeted or empiric therapy (n = 1541), 373 (24.2%) were for pneumonia, 278 (18%) for intra-abdominal infections and 251 (16.3%) for fever without a source. For the treatment of community-acquired pneumonia (CAP) (n = 178), aminopenicillins and 3GC use was 31% and 37%, respectively. Conclusion Our study used a standardized approach to assess AU to obtain benchmarking data for Canadian pediatric hospitals. About one-third of children hospitalized in Canadian pediatric hospitals are prescribed at least one antimicrobial. Of patients on treatment for CAP, only 31% were prescribed aminopenicillins. More detailed analysis of the rationale for AU, and assessment of appropriateness is required to fully understand antimicrobial prescribing practices in pediatric hospitals and develop stewardship initiatives. 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".