P-1132. Patient Pre-Selection Improves Efficiency and Acceptability of Antimicrobial Audit-and-Feedback Rounds in a Neonatal Intensive Care Unit
Bibliographic record
Abstract
Abstract Background Audit-and-Feedback Rounds (AFR) are an effective way of evaluating the appropriateness of antimicrobials in inpatient settings, but they are time- and resource-intensive. In our 52-bed tertiary care neonatal intensive care unit (NICU), AFR methodology was modified in that pharmacists would pre-select patients to be discussed, in order to focus on value-added discussions. This observational study describes the impact of this intervention on key AFR metrics.Figure 1.Percentage of NICU patients on systemic antimicrobials who were discussed at audit-and-feedback rounds, pre- and post-intervention. Methods Since October 2019, the NICU team and an Infectious Diseases (ID) physician met weekly to review all patients on systemic antimicrobials at the time of AFR. The ID physician assessed the appropriateness of antimicrobials and made recommendations to the team. Pharmacists prospectively collected data on the duration of AFR, reasons for inappropriate prescriptions, recommendations made, and adherence to recommendations 24 hours later. To improve efficiency, from September 2021 on, it was agreed that pharmacists would select out some patients from the discussion list as they were considered by default to be on appropriate therapy: those followed by the ID service; neonates on empiric ampicillin and an aminoglycoside for early-onset sepsis; and those on prophylactic antimicrobials if already reviewed once before.Figure 2.Percentage of antimicrobial stewardship recommendations accepted by NICU team, assessed 24 hours post-rounds. Results In the pre-intervention period (Oct 2019 - Oct 2020), 92% (n = 226) of patients on systemic antimicrobials were reviewed, and AFR lasted 27 minutes on average. Antimicrobial use was considered appropriate for 88% of patients and 75% (n=55) of recommendations were accepted by the NICU team. In the initial post-intervention period (Oct 2021- Oct 2022), 30 % (n = 101) of patients on antimicrobials were discussed at AFR (Figure 1), and usage was considered appropriate in 80% (n = 123). Between Oct 2022 and Oct 2023, mean duration of AFR had decreased to 13 minutes, and adherence to recommendations increased to 92% (n=36). Conclusion Patient pre-selection for AFR was associated with shorter rounds and increased uptake of recommendations by clinical teams. This method could be used to conduct AFR in other busy inpatient 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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".