The impact of preadmission/prediagnosis use of GLP‐1 receptor agonists on COVID‐19 mortality in patients with diabetes: A systematic review and meta‐analysis
Notice bibliographique
Résumé
From the onset of the coronavirus disease 2019 (COVID-19) pandemic, there has been considerable interest in exploring the potential of repurposing antidiabetic drugs with anti-inflammatory properties to improve the outcomes of patients with COVID-19. 1 Among these drugs, glucagon-like peptide-1 (GLP-1) receptor agonists have emerged as promising candidates owing to their potential to alleviate inflammation.Previous studies have reported the potential of GLP-1 receptor agonists to lower the levels of C-reactive protein and interleukin-6, both of which have prognostic significance in patients with COVID-19.[2][3][4][5][6][7] However, further clinical evidence is needed to fully establish the efficacy of GLP-1 receptor agonists in this population of patients.Therefore, our objective is to conduct an updated systematic review and meta-analysis of covariate-adjusted real-world studies to evaluate the impact of preadmission/prediagnosis use of GLP-1 receptor agonists on the risk of mortality in patients with COVID-19 and diabetes. | METHODSThis systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement.8 | Literature screeningWe conducted a systematic literature search of electronic databases (PubMed, Web of Science, Scopus) and preprint servers (medRxiv, Research Square, SSRN) without any language restrictions, aiming to identify studies that included human subjects.The search spanned from the beginning of available records until May 20, 2023.We employed a comprehensive search strategy using relevant keywords and MeSH terms-"COVID-19," "SARS-CoV-2," "GLP," "glucagon," "antidiabetic," and "glucose-lowering."Additionally, we manually searched the references of relevant articles for additional studies. | Study selectionThe literature screening process was conducted independently by two investigators (CSK and SSH) to identify eligible studies.The inclusion criteria for this systematic review were limited to observational studies that provided information on the risk of COVID-19-associated mortality in patients who had used GLP-1 receptor agonists before COVID-19 hospital admission or diagnosis, compared to those who had not used them.These studies were required to report adjusted mortality estimates in the form of odds ratio, hazard ratio, or relative risk, along with their corresponding 95% confidence intervals.Excluded from consideration were studies that reported nonadjusted mortality estimates, as well as comments, case reports, conference papers, animal experiments, letters, and review articles that lacked original data. | Study outcomeThe primary outcome of interest was COVID-19-associated mortality. | Data extractionData extraction was carried out by two investigators (CSK and DSR), who extracted important characteristics from each study.In cases where there were disagreements in the data extraction process, the investigators resolved them through discussion and consensus. | Risk of bias assessmentThe methodological quality of the observational studies included in the review was evaluated using the Newcastle-Ottawa Scale.This scale categorized the studies as low, moderate, or high quality based on assigned scores of 0-5, 6-7, and 8-9, respectively.The assessment of study quality was conducted independently by two
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 | 0,010 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,018 | 0,036 |
| Bibliométrie | 0,006 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».