ABS0675 COMPARATIVE EFFECTIVENESS OF UPADACITINIB VERSUS OTHER JAK INHIBITORS IN PATIENTS WITH RHEUMATOID ARTHRITIS IN A GLOBAL REAL-WORLD SETTING
Notice bibliographique
Résumé
Background: Network meta-analyses of phase 3 clinical trial data involving JAK inhibitor (JAKi)-treated patients with RA and an inadequate response to conventional synthetic DMARDs showed that upadacitinib (UPA) 15 mg had numerically higher efficacy vs other approved JAKis [1]. However, real-world data on the effectiveness of UPA in clinical practice relative to other JAKis is limited. Objectives: We assessed the effectiveness of UPA vs other JAKis, including tofacitinib, baricitinib, and peficitinib, using real-world data. Methods: Data were extracted from the Adelphi RA Disease-Specific Programme, a cross-sectional study with elements of retrospective data collection. Surveys were administered to rheumatologists and their patients in the EU, UK, Japan, Canada, and USA from July 2021 to February 2022. Patients receiving UPA 15 mg or other JAKis for ≥6 months were included. Physician-reported clinical outcomes included disease activity (with formal DAS28 scoring available for 35.5% of patients) categorized as follows: DAS28 remission (<2.6) and low, (2.6–< 3.2), moderate (3.2–5.1), and high (>5.1) disease activity (LDA, MDA, and HDA, respectively); pain and fatigue (none, mild, moderate, or severe); and medication adherence (completely adherent or not). A high degree of correlation (Spearman's coefficient, 0.68) was observed between formal DAS28 scores and disease activity category, justifying the use of physician-reported DAS28 categories. Unadjusted physician-reported outcomes are descriptively reported as the change in disease activity from initiation of current treatment to most recent follow up at ≥6 months. Adjusted physician-reported outcomes were compared for UPA vs other JAKis at the most recent follow-up visit using inverse probability weighted regression adjustment (IPWRA) methods. Results are reported as predicted percentages along with P values for each treatment group. Results: A total of 1440 patients were included (UPA 15 mg, n=1205; other JAKis, n=235), with most patients in the other JAKis group receiving baricitinib (n=120) or tofacitinib (n=113) and the remainder receiving peficitinib (n=2). Baseline characteristics are shown in Table 1, and the two treatment groups were weighted based on multiple covariates. At treatment initiation, 63% of UPA-treated patients were in MDA/HDA, while 51% of other JAKi-treated patients were in MDA/HDA. At the most recent follow-up visit, 85% and 88% of patients receiving UPA and other JAKis, respectively, were in LDA/remission (Table 1). IPWRA showed that patients on UPA were significantly more likely to have achieved physician-reported DAS28 remission (54% vs 44%, P =.03), no pain (43% vs 33%, P =.02), and complete medication adherence (60% vs 49%, P =.03) compared to those receiving other JAKis (Figure 1). Additionally, 43% of patients receiving UPA were evaluated as having no fatigue compared to 36% of patients receiving other JAKis ( P =.17). Conclusion: The findings of this real-world study of patients with RA demonstrate that greater proportions of patients attained physician-reported DAS28 remission, absence of pain, and medication adherence with UPA vs other JAKis. REFERENCES: [1] Pope J, et al. Adv Ther 2020;37:2356-72. Table 1 . Figure 1 Acknowledgements: Data collection was undertaken by Adelphi Real World as part of an independent survey, entitled the Adelphi Rheumatoid Arthritis Disease Specific Programme (DSP). The DSP is a wholly owned Adelphi product and is the intellectual property of Adelphi Real World. The analysis described here used data from the Adelphi RA DSP. AbbVie was one of multiple subscribers to the DSP and did not influence the original survey through either contribution to the design of questionnaires or data collection. All authors had access to the data results and participated in the development, review, and approval of this publication. No honoraria or payments were made for authorship. Medical writing support was provided by Matthew Eckwahl, PhD, of AbbVie. Disclosure of Interests: Peter C. Taylor AbbVie, Acelyrin Inc., Biogen, Eli Lilly and Company, Fresenius, Gilead, GSK, Janssen, Nordic Pharma, Pfizer, UCB Pharma, Immunovant, Sanofi and Kymab, Galapagos, Aditi Kadakia ABV, AbbVie Inc., Jack Milligan AbbVie, Sander Strengholt ABV, AbbVie, Oliver Howell AbbVie, Pankaj Patel ABV, AbbVie, Sophie Barlow AbbVie, Roberto F. Caporali AbbVie, Alfasigma, Accord, Celltrion, Lilly, Fresenius, Galapagos, Janssen, MSD, Novartis, Pfizer Inc, and UCB. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».