POS1044 EFFECT OF SECUKINUMAB VERSUS ADALIMUMAB ON ACR CORE COMPONENTS AND HEALTH-RELATED QUALITY OF LIFE IN PATIENTS WITH PSORIATIC ARTHRITIS: RESULTS FROM THE EXCEED STUDY
Notice bibliographique
Résumé
<h3>Background:</h3> EXCEED (NCT02745080) was the first fully blinded head-to-head trial to evaluate the efficacy and safety of secukinumab (SEC) versus (vs) adalimumab (ADA) monotherapy in patients with active psoriatic arthritis (PsA) with a primary endpoint of American College of Rheumatology (ACR) 20 at Week 52. Although SEC narrowly missed statistical significance for superiority vs ADA, numerically higher response for other musculoskeletal endpoints and composite indices were observed with SEC.<sup>1</sup> <h3>Objectives:</h3> To explore the effect of SEC and ADA on ACR core components, function and Health-related Quality of Life (HRQoL) outcomes. <h3>Methods:</h3> Patients were randomised 1:1 to receive SEC 300 mg (N=426) subcutaneous (s.c.) at baseline, Week 1-4, followed by every 4 weeks until Week 48 or ADA 40 mg (N=427) s.c. at baseline followed by same dosing every 2 weeks until Week 50. The primary, key secondary and some exploratory endpoints at Week 52 were previously reported.<sup>1</sup> A supportive analysis for ACR50 response using logistic regression model and trimmed means model for Health Assessment Questionnaire-Disability Index (HAQ-DI) with gender and smoking status as factors was performed to adjust for imbalances in baseline characteristics. An exploratory analysis of ACR core components with SEC vs ADA at Week 52 was conducted using a mixed-effects repeated measures model that included tender and swollen joint counts, patient and physician global assessment, PsA pain (VAS) and erythrocyte sedimentation rate. HRQoL variables were also exploratory and assessed based on Short Form Health Survey Physical/Mental Component Summary (SF-36 PCS/MCS) scores and Dermatology Life Quality Index (DLQI). <h3>Results:</h3> The demographic and baseline disease characteristics were comparable across treatment groups, except for an imbalance in sex (females: 51.2% vs 46.4%) and smoking status (yes: 21.8% vs 17.8%) in SEC and ADA group, respectively. At Week 52, ACR50 responses were 49.0% and 44.8% (<i>P</i>=0.0929) and HAQ-DI mean change from baseline were −0.69 and −0.58 (<i>P</i>=0.0314) in SEC and ADA treatment groups, respectively after adjusting for gender and smoking status. No major difference across ACR core components was observed in both treatment groups at Week 52 (Table 1). At Week 52, SEC presented similar improvement in SF-36 PCS/MCS score and numerically higher improvement in DLQI compared to ADA (Figure 1). <h3>Conclusion:</h3> Secukinumab provided similar improvements in ACR core components and SF-36 based quality of life at Week 52 with adalimumab. Greater improvement in HAQ-DI response and DLQI was demonstrated with secukinumab compared to adalimumab. <h3>References:</h3> [1]McInnes IB, et al. <i>Lancet</i>. 2020; 395:1496–505. <h3>Disclosure of Interests:</h3> Philippe Goupille Speakers bureau: AbbVie, Amgen, Biogen, BMS, Celgene, Chugai, Janssen, Eli Lilly, Medac, MSD, Nordic Pharma, Novartis, Pfizer, Sanofi and UCB, Consultant of: AbbVie, Amgen, Biogen, BMS, Celgene, Chugai, Janssen, Eli Lilly, Medac, MSD, Nordic Pharma, Novartis, Pfizer, Sanofi and UCB, Grant/research support from: AbbVie, Amgen, Biogen, BMS, Celgene, Chugai, Janssen, Eli Lilly, Medac, MSD, Nordic Pharma, Novartis, Pfizer, Sanofi and UCB, Frank Behrens Paid instructor for: Eli Lilly, Consultant of: Pfizer, AbbVie, Sanofi, Eli Lilly, Novartis, Genzyme, Boehringer Ingelheim, Janssen, MSD, Celgene, Roche and Chugai, Grant/research support from: Pfizer, Janssen, Chugai, Celgene and Roche, Laura C Coates Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Biogen, BMS, Celgene, Domain, Eli Lilly, Gilead, GSK, Janssen, Medac, Novartis, Pfizer, Serac and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer and UCB, Jordi Gratacos-Masmitja Speakers bureau: AbbVie, Amgen, BMS, Celgene, Janssen, Eli Lilly, Novartis and Pfizer, Consultant of: AbbVie, Amgen, BMS, Celgene, Janssen, Eli Lilly, Novartis and Pfizer, Grant/research support from: AbbVie, Amgen, BMS, Celgene, Janssen, Eli Lilly, Novartis and Pfizer, Philip J Mease Speakers bureau: AbbVie, Amgen, Genentech, Janssen, Eli Lilly, Merck, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Boehringer Ingelheim, Galapagos, Celgene, Genentech, Gilead, Janssen, Eli Lilly, Novartis, Pfizer, SUN Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Galapagos, Genentech, Gilead, Janssen, Eli Lilly, Merck, Novartis, Pfizer, SUN Pharma, and UCB, Dafna D Gladman Consultant of: Amgen, AbbVie, BMS, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Novartis, Pfizer and UCB, Grant/research support from: Amgen, AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer and UCB, Peter Nash Speakers bureau: Novartis, Abbvie, Roche, Pfizer, BMS, Janssen, Celgene, UCB, Eli Lilly, MSD, Sanofi, Gilead, Consultant of: Novartis, Abbvie, Roche, Pfizer, BMS, Janssen, Celgene, UCB, Eli Lilly, MSD, Sanofi, Gilead, Grant/research support from: Novartis, Abbvie, Roche, Pfizer, BMS, Janssen, Celgene, UCB, Eli Lilly, MSD, Sanofi, Gilead, Arthur Kavanaugh Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, and UCB, Ruvie Martin Shareholder of: Novartis, Employee of: Novartis, Weibin Bao Shareholder of: Novartis, Employee of: Novartis, Corine Gaillez Shareholder of: Novartis and BMS, Employee of: Novartis, Iain McInnes Speakers bureau: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Janssen, Eli Lilly, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Janssen, Eli Lilly, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Janssen, Eli Lilly, Novartis, Pfizer, and UCB.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| 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 | 0,000 | 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 ».