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Enregistrement W4411432504 · doi:10.1016/j.ard.2025.06.1590

ABS0740 UPADACITINIB'S EFFECT ON PAIN CONTROL IN RHEUMATOID ARTHRITIS: A REAL-LIFE SINGLE-CENTER EXPERIENCE

2025· article· en· W4411432504 sur OpenAlexaboutno aff
Leonardo Frascà, M. Vomero, A. Marino, D. Currado, Francesca Trunfio, Larry E. Kun, Francesca Saracino, Erika Corberi, Lauren Lamberti, Giulia Giannini, L. Navarini, R. Giacomelli

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiquePeripheral Neuropathies and Disorders
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineRheumatoid arthritisSingle CenterPain controlPhysical therapyInternal medicineSurgery

Résumé

récupéré en direct d'OpenAlex

Background: Patients affected by Rheumatoid Arthritis (RA) refer to pain as the most important symptom of their condition. Inadequate pain control in RA not only exacerbates the severity of the disease but also significantly affects physical functioning, quality of life (QoL), social interactions, and mood disorders [1]. Nevertheless, even when joint inflammation is controlled, and serum inflammatory markers are within normal ranges, residual pain remains one of the most significant issues for these patients. These findings highlight the crucial role of non-inflammatory pain mechanisms in RA. Data retrieved from clinical trials is starting to point out the efficacy of JAK inhibitors in achieving pain control and lowering residual pain rates, showing greater efficacy than TNFα inhibitors [2, 3]. Improved pain control could result from blocking several cytokines simultaneously. Furthermore, the JAK/STAT pathway involves specific non-inflammatory processes associated with pain control regarding microglial cells and the central nervous system (CNS). Objectives: Our goal is to provide real-life data on pain control and pain-related psychometric parameters in RA patients, undergoing therapy with JAK inhibitors. Methods: A single-center, observational, longitudinal, prospective study was conducted. Patients attended rheumatological visits, at the start of Upadacitinib therapy (T0), followed by visits at 4 (T1), 8 (T2), and 12 months (T3). Disease activity indices were collected during each visit, and questionnaires were administered to investigate the clinical-psychometric variations in pain parameters during treatment. Patients were given the following questionnaires: Pain Catastrophizing Scale (PCS), McGill Pain Questionnaire (MPQ), a shortened version of Pain Quality Assessment Scale (PQAS), Health Assessment Questionnaire (HAQ), and Beck Depression Inventory (BDI). The Friedman test assessed the significance of differences in repeated measures. A Conover post-hoc comparison was conducted to compare differences between pairs of groups, and the Holm correction was applied. A p-value of less than 0.05 was considered statistically significant. Results: A total of 34 patients were enrolled in the study (mean age: 58 ± 13.5 years). Treatment with Upadacitinib demonstrated a statistically significant improvement in both inflammatory and pain-related parameters over the 12-month observation period. Inflammatory markers and disease activity indices showed substantial reductions, including CDAI (p<0.001), SDAI (p<0.001), and DAS28-CRP (p<0.001). Pain-related clinical and psychometric parameters also significantly improved. Patients reported a marked reduction in both pain intensity (VAS pain, p<0.001) and disability (HAQ, p<0.001). Psychometric evaluations revealed a significant decrease in scores for pain catastrophizing (PCS, p<0.001), overall pain quality (MPQ, p=0.006), and depressive symptoms (BDI, p=0.018). Furthermore, improvements were observed across pain quality dimensions of the PQAS, including intense (p<0.001), sharp (p=0.001), hot (p<0.001), and dull pain (p<0.001). All data are presented in Table 1 and Figure 1. Conclusion: The results of this study show the efficacy of JAK inhibitor therapy in improving clinical and psychometric parameters related to pain in patients with RA. The marked reduction in pain control, along with the decrease in pain-related mood disturbance, highlights the potential role of these drugs for managing both inflammatory and non-inflammatory pain mechanisms in RA. Further research with larger groups, multicenter studies, and longer follow-ups is warranted to confirm these results and explore the long-term benefits of JAK inhibitors in managing pain in RA, particularly in real-world settings. References: [1] Walsh, D. A. & McWilliams, D. F. Mechanisms, impact and management of pain in rheumatoid arthritis. Nat Rev Rheumatol 10 , 581–592 (2014). [2] Taylor, P. C. et al. Achieving Pain Control in Rheumatoid Arthritis with Baricitinib or Adalimumab Plus Methotrexate: Results from the RA-BEAM Trial. Journal of Clinical Medicine 8 , 831 (2019). [3] Tóth, L. et al. Janus Kinase Inhibitors Improve Disease Activity and Patient-Reported Outcomes in Rheumatoid Arthritis: A Systematic Review and Meta-Analysis of 24,135 Patients. Int J Mol Sci 23 , 1246 (2022). Table 1. Overall changes in clinical assessment and questionnaire scores from baseline (T0) to 12 months (T3) Figure 1Variation in pain parameters between each timepoint Acknowledgements: NIL . Disclosure of Interests: None declared . © 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.

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)0,000
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,405
Score d'incertitude au seuil0,587

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,020
Tête enseignante GPT0,291
Écart entre enseignants0,271 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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Même revueAnnals of the Rheumatic DiseasesMême sujetPeripheral Neuropathies and DisordersTravaux en français237 207