Analysis of the Effectiveness of Early Intervention on Carbapenem Antibiotic Use
Bibliographic record
Abstract
Introduction The use of carbapenem antibiotics is a known risk factor for the emergence of carbapenem-resistant Enterobacteriaceae (CRE), a growing global public health concern. In this study, we focused on cases where carbapenems were selected as the initial empirical therapy and where early intervention strategies were implemented to assess and potentially modify such prescribing practices. Our primary aim was to evaluate changes in carbapenem usage following the initiation of early interventions and to determine whether these measures contributed to more appropriate antimicrobial use. Additionally, by analyzing cases subject to intervention, we sought to identify key factors that can help prevent the unnecessary initial selection of carbapenems. Methods We conducted a retrospective analysis of cases subjected to early intervention for carbapenem use over a one-year period from April 2024 to March 2025. In principle, early intervention involved clinical rounds or review of medical records within 24 hours of carbapenem initiation. When antibiotics were deemed necessary to be changed or discontinued, this was documented in the medical record, and feedback was provided. Interventions were classified into four categories: (1) no recommendation for change (appropriate use), (2) recommendation to switch to an alternative agent (change in empirical therapy), (3) recommendation for de-escalation, and (4) recommendation for discontinuation. For categories (2) to (4), we also collected data on whether the recommendations were accepted. Carbapenem use was assessed using days of therapy (DOT) and antimicrobial use density (AUD). Monthly trends in DOT and AUD before and after the start of the intervention program were analyzed. In addition, we evaluated annual changes in the use of carbapenems and other broad-spectrum antibiotics. Results Between April 2024 and March 2025, early interventions were conducted in 377 cases. Among these, 220 cases (58%) were deemed appropriate and required no change in therapy. The second most common category comprised 106 cases (28%) in which a switch to an alternative agent was recommended. In 33 cases (9%), de-escalation was suggested based on the identification of the causative pathogen, and antimicrobial susceptibility results were available at the time of intervention. In 18 cases (5%), no evidence of infection was found, and antibiotics were considered unnecessary. The acceptance rate of recommendations was generally favorable across all categories. Following the implementation of early intervention, both the DOT and AUD for carbapenems showed a notable decline. Conclusion Early intervention after prescribing carbapenem was associated with a reduction in both AUD and DOT, suggesting improved antimicrobial stewardship. These findings underscore the importance of appropriate empirical antibiotic selection in minimizing unnecessary carbapenem use. To curb the inappropriate initial use of carbapenems, it is essential to follow the fundamental principles of infectious disease management when selecting antibiotics and to accurately interpret culture and susceptibility data. Interventions and education focused on these areas are crucial for promoting responsible antimicrobial prescribing.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".