Antimicrobial Stewardship Programs in Neonates: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Neonatal sepsis is a significant contributor to mortality and morbidity; however, the uncontrolled use of antimicrobials is associated with significant adverse effects. Our objective with this article is to review the components of neonatal antimicrobial stewardship programs (ASP) and their effects on clinical outcomes, cost-effectiveness, and antimicrobial resistance. METHODS: We selected randomized and nonrandomized trials and observational and quality improvement studies evaluating the impact of ASP with a cutoff date of May 22, 2023. The data sources for these studies included PubMed, Medline, Embase, Cochrane CENTRAL, Web of Science, and SCOPUS. Details of the ASP components and clinical outcomes were extracted into a predefined form. RESULTS: Of the 4048 studies retrieved, 70 studies (44 cohort and 26 observational studies) of >350 000 neonates met the inclusion criteria. Moderate-certainty evidence reveals a significant reduction in antimicrobial initiation in NICU (pooled risk difference [RD] 19%; 95% confidence interval [CI] 14% to 24%; 21 studies, 27 075 infants) and combined NICU and postnatal ward settings (pooled RD 8%; 95% CI 6% to 10%; 12 studies, 358 317 infants), duration of antimicrobial agents therapy (pooled RD 20%; 95% CI 10% to 30%; 9 studies, 303 604 infants), length of therapy (pooled RD 1.82 days; 95% CI 1.09 to 2.56 days; 10 studies, 157 553 infants), and use of antimicrobial agents >5 days (pooled RD 9%; 95% CI 3% to 15%; 5 studies, 9412 infants). Low-certainty evidence reveals a reduction in economic burden and drug resistance, favorable sustainability metrices, without an increase in sepsis-related mortality or the reinitiation of antimicrobial agents. Studies had heterogeneity with significant variations in ASP interventions, population settings, and outcome definitions. CONCLUSIONS: Moderate- to low-certainty evidence reveals that neonatal ASP interventions are associated with reduction in the initiation and duration of antimicrobial use, without an increase in adverse events.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.062 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".