1198-P: Prevention of COVID-with Subcutaneous Administration of CAS+IMD in Individuals with Diabetes and Other Risk Factors for Severe Disease
Notice bibliographique
Résumé
Background: Subcutaneous (SC) administration of the monoclonal antibody combination casirivimab and imdevimab (CAS+IMD) decreased the risk of COVID-in a phase 3 trial of uninfected adolescents/adults who shared a household with a SARS-CoV-2–infected individual (NCT04452318) . Here, we present a subgroup analysis of this study, assessing the efficacy and safety of CAS+IMD in preventing symptomatic COVID-in individuals with diabetes and other risk factors for progression to severe disease. Methods: Healthy individuals aged ≥12 years, identified within 96 hours of collection of a positive test from a household contact, were randomized 1:1 to receive SC CAS+IMD 1200 mg or placebo. The primary endpoint was the proportion of participants who developed symptomatic COVID-during the 28-day efficacy assessment period among those who were SARS-CoV-2 RT-qPCR negative at baseline. Post hoc analyses assessed efficacy and safety by diabetes history, body mass index (BMI) , and the presence/absence of ≥1 high-risk factor for severe COVID-19. Results: The efficacy analysis included 2067 participants who were enrolled from July 13, 2020–January 28, 2021 and were RT-qPCR negative at baseline. In participants with diabetes (n=139) , the relative risk reduction (RRR) of developing symptomatic COVID-with CAS+IMD vs. placebo was 76.3%. Similar results were observed in participants with BMI ≥30 kg/m2 (n=728; RRR=74.9%) and in those with ≥1 high-risk factor for severe COVID-at baseline (n=620; RRR=77.5%) . Across subgroups, injection-site reaction was more common with CAS+IMD versus placebo whereas asymptomatic COVID-19, COVID-19, and headache were more common with placebo versus CAS+IMD. Conclusions: In study participants with diabetes and those with other risk factors for severe COVID-19, treatment with SC CAS+IMD decreased the risk of developing symptomatic COVID-19, consistent with results in the overall population. Disclosure M.O'brien: Employee; Regeneron Pharmaceuticals Inc., Other Relationship; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. R.V.Barnabas: Other Relationship; Regeneron Pharmaceuticals Inc. M.S.Cohen: Other Relationship; COVPN, Fogarty, HPTN, McGill, National Institutes of Health, Prime Global Options, Regeneron Pharmaceuticals Inc. A.Mahmood: Employee; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. G.A.Herman: Employee; Regeneron Pharmaceuticals Inc., Other Relationship; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. G.D.Yancopoulos: Employee; Regenacy Pharmaceuticals, Inc., Other Relationship; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. D.M.Weinreich: Employee; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. The covid-phase 3 prevention trial team: n/a. E.Forleo-neto: Employee; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. E.Oviedo orta: Employee; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. J.Mcginniss: Employee; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. P.Hou: Employee; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. F.Isa: Employee; Regeneron Pharmaceuticals Inc., Other Relationship; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. K.Chan: Employee; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. N.Sarkar: Employee; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. B.J.Musser: Employee; Regeneron Pharmaceuticals Inc., Stock/Shareholder; Regeneron Pharmaceuticals Inc. Funding Supported by Regeneron Pharmaceuticals, Inc., and F. Hoffmann-La Roche Ltd. This trial was conducted jointly with the COVID-Prevention Network, which was funded by the National Institute of Allergy and Infectious Diseases (NIAID) , National Institutes of Health (NIH) .
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».