Analysis of the Effectiveness of Early Intervention on Carbapenem Antibiotic Use
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».