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Enregistrement W3085841760 · doi:10.1371/journal.pmed.1003293

Interventions for treatment of COVID-19: A living systematic review with meta-analyses and trial sequential analyses (The LIVING Project)

2020· review· en· W3085841760 sur OpenAlexaff
Sophie Juul, Emil Eik Nielsen, Joshua Feinberg, Faiza Siddiqui, Caroline Barkholt Kamp, Emily Barot, Niklas Nielsen, Peter Bentzer, Areti Angeliki Veroniki, Lehana Thabane, Fan-Long Bu, Sarah Louise Klingenberg, Christian Gluud, Janus Christian Jakobsen

Notice bibliographique

RevuePLoS Medicine · 2020
Typereview
Langueen
DomaineMedicine
ThématiqueCOVID-19 Clinical Research Studies
Établissements canadiensMcMaster UniversityImpactSt. Michael's Hospital
Organismes subventionnairesVetenskapsrådetSvenska Forskningsrådet FormasEuropean CommissionPoul Due Jensens Fond (Grundfos Foundation)
Mots-clésCoronavirus disease 2019 (COVID-19)MedicinePsychological intervention2019-20 coronavirus outbreakMeta-analysisMEDLINESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Intensive care medicineVirologyBiologyInternal medicineNursingDiseaseOutbreak

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a rapidly spreading disease that has caused extensive burden to individuals, families, countries, and the world. Effective treatments of COVID-19 are urgently needed. METHODS AND FINDINGS: This is the first edition of a living systematic review of randomized clinical trials comparing the effects of all treatment interventions for participants in all age groups with COVID-19. We planned to conduct aggregate data meta-analyses, trial sequential analyses, network meta-analysis, and individual patient data meta-analyses. Our systematic review is based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and Cochrane guidelines, and our 8-step procedure for better validation of clinical significance of meta-analysis results. We performed both fixed-effect and random-effects meta-analyses. Primary outcomes were all-cause mortality and serious adverse events. Secondary outcomes were admission to intensive care, mechanical ventilation, renal replacement therapy, quality of life, and nonserious adverse events. We used Grading of Recommendations Assessment, Development and Evaluation (GRADE) to assess the certainty of evidence. We searched relevant databases and websites for published and unpublished trials until August 7, 2020. Two reviewers independently extracted data and assessed trial methodology. We included 33 randomized clinical trials enrolling a total of 13,312 participants. All trials were at overall high risk of bias. We identified one trial randomizing 6,425 participants to dexamethasone versus standard care. This trial showed evidence of a beneficial effect of dexamethasone on all-cause mortality (rate ratio 0.83; 95% confidence interval [CI] 0.75-0.93; p < 0.001; low certainty) and on mechanical ventilation (risk ratio [RR] 0.77; 95% CI 0.62-0.95; p = 0.021; low certainty). It was possible to perform meta-analysis of 10 comparisons. Meta-analysis showed no evidence of a difference between remdesivir versus placebo on all-cause mortality (RR 0.74; 95% CI 0.40-1.37; p = 0.34, I2 = 58%; 2 trials; very low certainty) or nonserious adverse events (RR 0.94; 95% CI 0.80-1.11; p = 0.48, I2 = 29%; 2 trials; low certainty). Meta-analysis showed evidence of a beneficial effect of remdesivir versus placebo on serious adverse events (RR 0.77; 95% CI 0.63-0.94; p = 0.009, I2 = 0%; 2 trials; very low certainty) mainly driven by respiratory failure in one trial. Meta-analyses and trial sequential analyses showed that we could exclude the possibility that hydroxychloroquine versus standard care reduced the risk of all-cause mortality (RR 1.07; 95% CI 0.97-1.19; p = 0.17; I2 = 0%; 7 trials; low certainty) and serious adverse events (RR 1.07; 95% CI 0.96-1.18; p = 0.21; I2 = 0%; 7 trials; low certainty) by 20% or more, and meta-analysis showed evidence of a harmful effect on nonserious adverse events (RR 2.40; 95% CI 2.01-2.87; p < 0.00001; I2 = 90%; 6 trials; very low certainty). Meta-analysis showed no evidence of a difference between lopinavir-ritonavir versus standard care on serious adverse events (RR 0.64; 95% CI 0.39-1.04; p = 0.07, I2 = 0%; 2 trials; very low certainty) or nonserious adverse events (RR 1.14; 95% CI 0.85-1.53; p = 0.38, I2 = 75%; 2 trials; very low certainty). Meta-analysis showed no evidence of a difference between convalescent plasma versus standard care on all-cause mortality (RR 0.60; 95% CI 0.33-1.10; p = 0.10, I2 = 0%; 2 trials; very low certainty). Five single trials showed statistically significant results but were underpowered to confirm or reject realistic intervention effects. None of the remaining trials showed evidence of a difference on our predefined outcomes. Because of the lack of relevant data, it was not possible to perform other meta-analyses, network meta-analysis, or individual patient data meta-analyses. The main limitation of this living review is the paucity of data currently available. Furthermore, the included trials were all at risks of systematic errors and random errors. CONCLUSIONS: Our results show that dexamethasone and remdesivir might be beneficial for COVID-19 patients, but the certainty of the evidence was low to very low, so more trials are needed. We can exclude the possibility of hydroxychloroquine versus standard care reducing the risk of death and serious adverse events by 20% or more. Otherwise, no evidence-based treatment for COVID-19 currently exists. This review will continuously inform best practice in treatment and clinical research of COVID-19.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,295
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Méta-épidémiologie (sens large)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,412
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,295
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0170,004
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,772
Tête enseignante GPT0,643
Écart entre enseignants0,129 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations92
Publié2020
Routes d'admission1
Résumé présentoui

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