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Record W4411441277 · doi:10.7759/cureus.86390

Analysis of the Effectiveness of Early Intervention on Carbapenem Antibiotic Use

2025· article· en· W4411441277 on OpenAlexaff
Yoritake Sakoda, Takanori Matsumoto, Masaki Yamaguchi, Kotaro Yoshida, Yasuki Maeno

Bibliographic record

VenueCureus · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsSt Mary's Hospital Centre
Fundersnot available
KeywordsMedicineCarbapenemAntibioticsIntensive care medicineMicrobiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.058
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.009
GPT teacher head0.257
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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