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Enregistrement W4410335824 · doi:10.1016/s2213-2600(25)00055-4

Effects of Janus kinase inhibitors in adults admitted to hospital due to COVID-19: a systematic review and individual participant data meta-analysis of randomised clinical trials

2025· review· en· W4410335824 sur OpenAlexaff
Alain Amstutz, Stefan Schandelmaier, Hannah Ewald, Benjamin Speich, Johannes M. Schwenke, Christof M Schönenberger, Stephan Schobinger, Thomas Agoritsas, Kay M. Tomashek, Seema Nayak, Mat Makowski, Alejandro Morales‐Ortega, David Bello, Giovanni Pomponio, Alessia Ferrarini, Monireh Ghazaeian, Frances Hall, Simon Bond, María Teresa García-Morales, María Jiménez-González, José Ramón Arribas, Patricia O Guimaraães, Caio de Assis Moura Tavares, Otávio Berwanger, Yazdan Yazdanpanah, Victoria Charlotte Simensen, Karine Lacombe, Maya Hites, Florence Ader, Evelina Tacconelli, France Mentré, Drifa Belhadi, Clément Massonnaud, Cédric Laouenan, Alpha Diallo, Aliou Baldé, Lambert Assoumou, Dominique Costagliola, Erica Ponzi, Corina S. Rueegg, Inge Christoffer Olsen, Marius Trøseid, Matthias Briel

Notice bibliographique

RevueThe Lancet Respiratory Medicine · 2025
Typereview
Langueen
DomaineMedicine
ThématiqueLong-Term Effects of COVID-19
Établissements canadiensPublic Safety Canada
Organismes subventionnairesNational Institute of Allergy and Infectious DiseasesEli Lilly and CompanyHorizon 2020 Framework ProgrammeNational Science FoundationIncyteSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Mots-clésMedicineMeta-analysisCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakMEDLINEClinical trialPandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Janus kinaseRandomized controlled trialInternal medicineVirology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Evidence from randomised clinical trials (RCTs) of Janus kinase (JAK) inhibitors-compared with usual care or placebo-in adults treated in hospital for COVID-19 is conflicting. We aimed to evaluate the benefits and harms of JAK inhibitors compared with placebo or usual care and whether treatment effects differed between prespecified participant subgroups. METHODS: For this systematic review and individual participant data meta-analysis (IPDMA), we searched Medline via Ovid, Embase via Elsevier, the Cochrane Central Register of Controlled Trials, the Cochrane COVID-19 Study Register, and the COVID-19 L·OVE Platform, including backward and forward citation searching (last search Nov 28, 2024), for RCTs (unpublished or published in any format and any language) that randomly assigned adults (aged ≥16 years) admitted to a hospital due to COVID-19 to receive either a JAK inhibitor (any type) or no JAK inhibitor (ie, received site-specific standard of care with or without placebo), and requested individual participant data (IPD) from the original trial teams. The primary outcome was all-cause mortality at day 28 after random assignment. We used two-stage meta-analyses adjusting for age and respiratory support, and pooled estimates using random-effects models. The assessment of individual-level effect modifiers was based solely on within-trial information and continuous modifiers were investigated as both linear and non-linear interactions. We used the Instrument for Assessing the Credibility of Effect Modification Analyses to appraise the subgroup analyses and the Grading of Recommendations Assessment, Development, and Evaluation approach to adjudicate the certainty of evidence. Grade 3 or 4 adverse events and serious adverse events by day 28, and adverse events of special interest within 28 days, were assessed among secondary outcomes. This study was registered with PROSPERO (CRD42023431817). FINDINGS: We identified 16 eligible trials. IPD were obtained from 12 trials, corresponding to 12 902 adults admitted to hospital between May, 2020, and March, 2022. These trials represented 12 902 [96·1%] of 13 423 participants from all eligible trials worldwide. Seven trials evaluated baricitinib, three evaluated tofacitinib, and two evaluated ruxolitinib. Overall, 755 (11·7%) of 6465 participants in the JAK inhibitor group died by day 28 compared with 805 (13·2%) of 6108 participants in the no JAK inhibitor group (adjusted odds ratio [aOR] 0·67 [95% CI 0·55-0·82]; high-certainty evidence; 39 fewer per 1000 [95% CI 55 fewer to 21 fewer]). JAK inhibitors decreased the need for new mechanical ventilation or other respiratory support and allowed for faster discharge from hospital by about 1 day. We observed fewer grade 3 and 4 adverse events and serious adverse events in the JAK inhibitor group (14 fewer per 1000 [95% CI 24 fewer to 4 fewer]; moderate-certainty evidence). The rates of adverse events of special interest were similar across both groups. No credible subgroup effect on mortality at day 28 was found for ventilation status, type of JAK inhibitor, presence of comorbidities, timing of treatment initiation after symptom onset, C-reactive protein concentration, or concomitant use of dexamethasone or tocilizumab. We found a moderately credible effect modification by age, with younger participants showing larger relative treatment effects than older participants, but similar absolute treatment effects due to higher baseline risk for older participants. INTERPRETATION: This IPDMA of RCTs in adults admitted to hospital due to COVID-19 found that JAK inhibitors reduced mortality across all levels of respiratory support, independent of dexamethasone or tocilizumab, and probably decreased serious and severe adverse events compared with no JAK inhibitors. FUNDING: This project has received funding from the EU's Horizon 2020 research and innovation programme under grant agreement number 101015736.

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)0,075
score de la tête « metaresearch » (Gemma)0,456
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Méta-épidémiologie (sens large)
Catégories consensuellesMétarecherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,532
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0750,456
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0510,003
Bibliométrie0,0020,004
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0020,001
Intégrité de la recherche0,0000,001
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 Opus0,372
Tête enseignante GPT0,517
Écart entre enseignants0,145 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

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

Citations10
Publié2025
Routes d'admission1
Résumé présentoui

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