Antimicrobial consumption in hospitalised COVID-19 patients: a systematic review and meta-analysis
Notice bibliographique
Résumé
Abstract Introduction Despite COVID-19 being a viral illness, antibiotic use has been more prevalent. In addition, co-infection (3.5%) and secondary infection (14.3%) were relatively low in hospitalised patients with COVID-19. A major concern is the increased risk of antimicrobial resistance (AMR) due to inappropriate antibiotic consumption (1). Aim This review aims to evaluate antimicrobial consumption (excluding repurposed drugs such as remdesivir) in hospitals and determine the prevalence of COVID-19 patients who received antibiotic therapy using meta-analysis. Methods The review was conducted according to PRISMA guidelines (2). The two investigators independently developed and applied eligibility criteria to examine original articles. Studies were eligible for inclusion if they met the following criteria: (i) original research studies with a minimum sample of 50 patients; (ii) focussed on antibiotic consumption (AMC); (iii) patients with COVID-19 or consumption amid COVID-19 pandemic; (iv) any age group or gender; and (v) reported in the English language. The included articles were retrieved from MEDLINE, CINAHL, WHO COVID-19 databases, including studies published in EMBASE, Scopus, WHO-COVID, and LILACS between December 2019 to July 2021. The modified version of Newcastle-Ottawa Scale (NOS) was used to measure biases in included studies after the consensus by both authors. The random-effects model was used to estimate the pooled prevalence or proportion of AMC among hospitalized COVID-19 patients. Results A total of 34 studies conducted among hospitalised COVID-19 patients were included. The extracted studies presented AMC in defined daily doses (DDD) or frequency and percentages. Azithromycin was the most frequently prescribed antibiotic in almost all studies. The meta-analysis that examined overall AMC using data from 25 studies (17 studies from high income countries and eight from low-middle income countires) revealed 69% (95% CI:63%-74%) of hospitalized COVID-19 received at least one course of antibiotics. The sub-group analysis of studies from high income countries (HICs) revealed 59% (95% CI: 51%-66%) consumed antibiotics compared with 89% (95% CI: 82% to 94%) among hospitalised COVID-19 patients in low-middle income countries (LMICs). Conclusion This review highlights the trend of antibiotic consumption in hospitalised COVID-19 patients. A significant rise in antibiotic consumption was observed in LMICs and increased antibiotic consumption in the first few months of the COVID-19 pandemic in HIC. The review outcomes emphasised the importance of rational and judicious use of antimicrobial therapy as well as to strenghting the antimicrobial stewardship policies and activities, particularly during a global pandemic. The limitation of the review undertaken was not identified incidence of co-infection and don’t include studies on reported AMC in immunocompromised patients. References (1) Rawson TM, Ming D, Ahmad R, Moore LSP, Holmes AH. Antimicrobial use, drug-resistant infections and COVID-19. Nature reviews Microbiology. 2020;18(8):409-10. (2) Beller EM, Glasziou PP, Altman DG, Hopewell S, Bastian H, Chalmers I, et al. PRISMA for Abstracts: Reporting Systematic Reviews in Journal and Conference Abstracts. PLOS Medicine. 2013;10(4):e1001419.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».