Antimicrobial Resistance In Nosocomial Bacterial Infections: A Systematic Review Of Global Trends And Laboratory Detection Methods
Notice bibliographique
Résumé
Background: Nosocomial bacterial infections due to antimicrobial-resistant (AMR) bacteria are among the most critical public health concerns in the world in the 21st century. Healthcare-associated infections (HAI) cause millions of patients to fall ill every year. These pose a significant public health burden in terms of mortality rates, morbidity rates, increased hospitalization times, and financial costs. The rapid transmission of multidrug-resistant (MDR), extensively drug-resistant (XDR), and pan-drug-resistant (PDR) bacteria in hospital environments has seriously undermined the therapeutic options available to clinicians. Laboratory detection of resistance mechanisms is critical to appropriate therapy and effective infection control. Objectives: The objectives of this systematic review were to synthesize evidence on global epidemiological trends of AMR in nosocomial bacterial infections, identify the nature of the most common bacteria that are resistant to antimicrobials in healthcare environments, and critically evaluate existing laboratory techniques for the detection of clinically relevant resistance mechanisms. Methods: An extensive literature search was conducted on several databases, including PubMed/MEDLINE, Scopus, Web of Science, CINAHL, and Cochrane Library, for literature published from January 2020 to March 2026. Relevant literature was included in this study if it discussed AMR rates in nosocomial infections, resistance mechanisms, and detection methods. Authors of this study strictly followed the guidelines of PRISMA 2020 for this study. For assessing the quality of included literature, Newcastle-Ottawa Scale was used for observational studies, and for assessing diagnostic accuracy, QUADAS-2 was used. Results: For this study, a total of 10 high-quality literature was included in the final data extraction. The global prevalence of MDR nosocomial infections varied from 38.2% to 76.4%. ESKAPE organisms, including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumonia, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter, were responsible for a large number of resistant nosocomial infections. The prevalence of MRSA in ICU settings varied from 18.3% to 54.7%.superior diagnostic accuracy for resistance detection in comparison with conventional phenotypic approaches. Conclusion: The burden of AMR in nosocomial infections is rising globally with alarming trends in some areas of the world. It is imperative that the use of molecular diagnostic tools, ASPs, and effective infection prevention and control strategies be implemented globally to curb the rising burden of AMR. It is also vital that there be effective international collaborations in the sharing of data and information on the rising burden of resistance in the world.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,000 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».