Nurse Prompting for Prescriber-Led Review of Antimicrobial Use in the Critical Care Unit: A Quality Improvement Intervention with Controlled Interrupted Time Series Analysis.
Bibliographic record
Abstract
Audit-and-feedback (A&F) is a core strategy for antimicrobial stewardship programs (ASPs). However, it is resource-intensive, and may not be practical in every setting. Recent guidelines support the non-ASP-led review of antimicrobials by prescribers (AM-REV) on a routine basis. A sustainable strategy for AM-REV in a critical care unit (CrCU) may improve antimicrobial utilization without additional ASP resources. Using a quality improvement framework, a prompt for AM-REV strategy was created. The primary outcome was antimicrobial utilization defined by days of therapy/1000 patient-days (AM-DOT). A secondary process outcome was the proportion of relevant cases for which an antimicrobial prompt was provided to the prescriber (AM-PRT). Balancing measures included CrCU mortality rates, length of stay, and 48-hours re-admission rates. Utilization data of a control class of medications (proton-pump inhibitors) was also collected. AM-DOT was collected for 34 months pre- and 14 months post-intervention. AM-PRT was collected for 3 months pre- and 12 months post-intervention. Segmented regression analysis was used for the primary outcome, with a descriptive analysis of secondary outcomes. CrCU nurses were recruited to prompt AM-REV during CrCU rounds. A standardized script was developed to insert day of antimicrobial therapy into rounds; prescribers were primed to respond with affirmation, rationale, and clinical decisions. Plan-Do-Study-Act (PDSA) cycles further refined the intervention to include nursing reminders from CrCU pharmacists and increased engagement of nurses during formal A&F rounds. Prior to the intervention, monthly AM-DOT was 804 with a positive trend (7.3 DOT/1000PD, P < 0.05). Post-intervention resulted in an immediate reduction of 217 DOT/1000 PD (P < 0.05) with a non-significant negative AM-DOT trend, representing a 20% (95% CI –15%, -25%) reduction in AM-DOT per month. There was no significant change in utilization of the control class of medications. The ABX-PRT increased from 17% to 50% during the intervention period. Balancing measures were comparable pre and post-intervention. Nurse prompting of AM-REV can lead to significant reductions in antimicrobial utilization, providing a non-ASP mechanism of sustaining antimicrobial awareness. 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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".