5. How Does Frailty Impact the Efficacy, Reactogenicity, Immunogenicity and Safety of the Adjuvanted Recombinant Zoster Vaccine? A Secondary Analysis of the ZOE-50 and ZOE-70 Studies
Notice bibliographique
Résumé
Abstract Background Herpes zoster can negatively impact older adults’ health and quality of life. An adjuvanted recombinant zoster vaccine (RZV) has excellent vaccine efficacy (VE), including in older adults. Given that frailty is strongly associated with vulnerability to illness and adverse health outcomes, we studied how frailty impacts RZV VE, immunogenicity, reactogenicity, and safety. Methods In the ZOE-50 and ZOE-70 pivotal Phase 3 efficacy studies of RZV, 29,305 participants aged 50–96 received 2 doses of RZV vs. placebo in 1:1 randomization. In this secondary analysis (NCT03563183), a baseline frailty index (FI) was created retrospectively following previously validated methods using pre-existing comorbidities and patient reported outcomes. Participants were categorized as non-frail (FI≤ 0.08), pre-frail (FI=0.08–0.25) or frail (FI≥ 0.25) for stratified analyses. Results FI was calculated for 99.8% of participants included in this secondary analysis (n=26,976), and was balanced between RZV and placebo groups. 45.6% were pre-frail and 11.3% were frail. Mean age was 68.8 years; 58.1% were women. RZV VE against HZ was consistently above 90% for all frailty categories [non-frail: 95.8% (95%CI: 91.6–98.2), pre-frail: 90.4% (84.4–94.4), frail: 90.2% (75.4–97.0)]. The RZV group demonstrated robust antibody responses post-dose 2 across frailty categories. In the RZV group, the percentage of participants reporting solicited adverse events decreased with increasing frailty. Unsolicited medically attended visits and serious adverse events increased with frailty and were balanced between placebo and RZV groups. Conclusion The ZOE studies included older adults who were frail and pre-frail, and VE was high across frailty categories. Reactogenicity decreased with increasing frailty, and no safety concerns were identified in any frailty group. Disclosures Melissa K. Andrew, MD, PhD, MSc(Ph), GSK (Grant/Research Support, Research Grant or Support) Joon Hyung Kim, MD, GSK (Employee, Shareholder) Sean Matthews, MSc, GSK (Consultant) Christophe Dessart, MSc, GSK (Employee) myron J. levin, MD, Curevo (Advisor or Review Panel member)GlaxoSmithKline (Grant/Research Support, Advisor or Review Panel member)GlaxoSmithKline (Grant/Research Support, Advisor or Review Panel member)Merck Research Laboratories (Advisor or Review Panel member, GlaxoSmithKline)Merck Research Laboratories (Advisor or Review Panel member)Merck ResearchLaboratories (Advisor or Review Panel member) Lidia Oostvogels, MD, GSK (Shareholder) Megan Riley, PhD, GSK (Employee) Shelly McNeil, FRCPC, MD, GSK (Grant/Research Support, Scientific Research Study Investigator, Research Grant or Support, Other Financial or Material Support, honoraria for talks) Anne Schuind, MD, GSK (Employee, Other Financial or Material Support, own GSK stock options or restricted shares as part of renumeration) Desmond Curran, PhD, GSK (Employee, Shareholder)
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».