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Enregistrement W4411426356 · doi:10.1016/j.ard.2025.06.026

POS0665 APREMILAST IMPROVES PATIENT-REPORTED PAIN REGARDLESS OF SEX AND AGE IN EARLY OLIGOARTICULAR PSORIATIC ARTHRITIS: A POST-HOC ANALYSIS FROM FOREMOST

2025· article· en· W4411426356 sur OpenAlexaff
Philip J. Mease, U. Mrowietz, J.F. Merola, Fabian Proft, L. Gossec, Dafna D. Gladman, Arthur Kavanaugh, S. Chaudhari, J. Vazquez, Lisong Teng, Laura C. Coates

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueSpondyloarthritis Studies and Treatments
Établissements canadiensToronto Western Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicineApremilastPsoriatic arthritisPost-hoc analysisDermatologyPhysical therapyPost hocArthritisInternal medicine

Résumé

récupéré en direct d'OpenAlex

Background: Psoriatic arthritis (PsA) is characterized by pain, fatigue, stiffness, and swelling, with patients reporting pain to be a prominent symptom negatively impacting their quality of life [1]. However, pain in PsA often differs by sex, with females typically reporting higher pain severity and greater disease burden compared with males, highlighting the need for sex-specific analyses in therapeutic studies [2]. Additionally, little is known about how age affects pain in patients with PsA, and if pain in older patients may be recalcitrant to treatment. The FOREMOST study (NCT03747939) presents a unique opportunity to assess patient-reported pain in early oligoarticular (oligo) PsA across sex and age subgroups, and the benefit of apremilast (APR) in improving pain [3]. Understanding responses to treatment by sex and age is critical for optimizing care for patients with oligo PsA. Objectives: This post hoc analysis of FOREMOST assessed changes in patient-reported pain outcomes across sex and age subgroups through 48 weeks and the impact of apremilast treatment. Methods: FOREMOST enrolled 308 patients with early oligo (>1–≤4 swollen and >1–≤4 tender joint count [SJC and TJC]; 66–68 joints assessed) PsA.[3] Patients were randomized 2:1 to APR (n=203) or placebo (PBO; n=105) for 24 weeks (early escape at week 16: PBO patients with no improvement in SJC could switch to APR), followed by an extension phase in which all patients could receive APR through Week 48. Patient-reported pain outcomes included pain visual analogue scale (VAS; 0 to 100 mm, higher scores indicate more pain), PsA Impact of Disease 12-item questionnaire (PsAID-12) pain score (0 [best status] to 10 [worst status]), and 36-item Short Form Survey (SF-36) bodily pain domain score (norm-based, higher scores indicate less pain). We report changes in these pain outcomes through Week 48 by sex and age (<40 years, 40–55 years, and >55 years). Week-16, PBO-controlled data are reported for the full analysis set (N=308); up to Week-48, extension-phase data are reported as observed for N=291 patients who received at least one dose of apremilast during the study, as randomized (APR/APR, n=203) or transitioned from PBO to APR at Week 16 or 24 (PBO/APR, n=88). Results: Of 308 randomized patients (PBO n=105, APR n=203), 169 (54.9%) were females, with uneven distribution between treatment arms: PBO, n=51 (49%) and APR, n=118 (58%). Summarized by age, 67 (21.8%) patients were <40 years (PBO, n=25; APR, n=42), 124 (40.2%) were 40–55 years (PBO, n=41; APR, n=83), and 117 (38.0%) were >55 years (PBO, n=39; APR, n=78). Baseline Pain VAS (mm) for females was higher than males: mean (SE) for females was PBO 54.5 (2.7) and APR 54.1 (2.0); males was PBO 47.9 (3.4) and APR 49.8 (2.4; Figure 1). In general, baseline pain scores were similar across age groups: for example, baseline mean (SE) pain VAS (mm) for <40 years was PBO 42.8 (4.9) and APR 54.5 (3.0), 40–55 years was PBO 53.6 (3.6) and APR 51.8 (2.5), and >55 years was PBO 53.8 (3.3) and APR 51.6 (2.6; Figure 2); patients <40 years randomized to PBO reported lower baseline Pain VAS. At Week 16 and across all sex and age groups, patients reported greater improvements in pain VAS, PsAID-12 pain, and SF-36 bodily pain domain scores with APR versus PBO, with patients <40 years generally reporting the numerically largest differences (Figure 1–2). At Week 16, improvements in pain scores in the PBO arm were greater in patients aged >55 years compared with other age groups (Figure 2). Patients continuing or switching to APR in the extension phase continued to report improvements in pain scores through Week 48, regardless of sex or age (Figures 1–2). Conclusion: In the FOREMOST study of early oligo PsA, APR improved patient-reported pain regardless of sex or age, with sustained benefits through Week 48. Females reported greater baseline burden of PsA-related pain than males. Younger patients showed the numerically largest improvements in pain with APR treatment compared with PBO; however, in the PBO group, older patients experienced greater improvements in pain than younger patients, potentially due to baseline imbalances in patient characteristics affecting pain outcomes, other causes of pain, or comorbidities. These findings highlight the need to better understand factors contributing to pain in PsA to improve patient care. REFERENCES: [1] Gudu and Gossec. Expert Rev Clin Immunol. 2018;14(5):405–417. [2] Passia et al. Arthritis Res Ther. 2022;24(22). [3] Gossec et al. Ann Rheum Dis. 2024;83(11):1480-1488. Acknowledgements: This study was funded by Amgen Inc. Writing support was funded by Amgen Inc. and provided by Jessica Ma, PhD, employee of and stockholder in Amgen Inc. Disclosure of Interests: Philip J. Mease AbbVie, Amgen Inc., Eli Lilly, Janssen, Novartis, Pfizer, UCB, Century, Cullinan, Inmagene, Moonlake, Takeda, AbbVie, Amgen Inc., Bristol Myers Squibb, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Ulrich Mrowietz AbbVie, Aditxt, Almirall, Amgen Inc., Aristea, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Dr. Reddy's, Eli Lilly, Foamix, Formycon, Immunic, Janssen, LEO Pharma, Medac, MetrioPharm, Novartis, Phi-Stone, Pierre Fabre, Sanofi-Aventis, UCB Pharma, and UNION Therapeutics, Joseph F Merola AbbVie, Amgen, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Dermavant, Janssen, Lilly, MoonLake, Novartis, Pfizer, Regeneron, Sanofi, Sun Pharma, and UCB, Fabian Proft AbbVie, Amgen, BMS, Celgene, Eli Lilly, Hexal, Janssen, Medscape, MSD, Novartis, Pfizer, Roche, and UCB, AbbVie, BMS, Janssen, Novartis, Pfizer, and UCB, Novartis, Eli Lilly, and UCB, Laure Gossec AbbVie, Almirall, AbbVie, Almirall, AbbVie, Almirall, Dafna D. Gladman AbbVie, Amgen Inc., Eli Lilly, Janssen, Novartis, Pfizer, UCB, AbbVie, Amgen Inc., Eli Lilly, Janssen, Novartis, Pfizer, UCB, AbbVie, Amgen Inc., Eli Lilly, Janssen, Novartis, Pfizer, UCB, Arthur Kavanaugh AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Janssen, Novartis, and Pfizer, AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Janssen, Novartis, and Pfizer, Siddharth Chaudhari Amgen Inc, Amgen Inc, JIMENA VAZQUEZ Amgen Inc, Amgen Inc, Lichen Teng Amgen Inc, Amgen Inc, Laura C. Coates AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Enlivex, Janssen, Moonlake, Novartis, Pfizer, Takeda, and UCB, Abbvie, Amgen, Janssen, 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.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,023

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,002
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0040,004
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

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 Opus0,012
Tête enseignante GPT0,260
Écart entre enseignants0,248 · 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 source (Gemma direct ou Codex distillé), 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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