POS1269 EULAR POINTS TO CONSIDER FOR THE DEFINITIONS OF DIFFICULT-TO-MANAGE AND TREATMENT-REFRACTORY PSORIATIC ARTHRITIS
Notice bibliographique
Résumé
Background: Psoriatic arthritis (PsA) is a heterogeneous, chronic inflammatory condition, with low remission rates, divergence of tissue response to therapy, and inadequate outcomes aggravated by co-morbidities. A proportion of patients with PsA have disease which is refractory or resistant to available therapies with significant health and socioeconomic impact for individuals and health care systems. Objectives: To develop evidence-based consensus definitions of Difficult-to-Manage (D2M) and Treatment-Refractory (TR) PsA. Methods: A multidisciplinary international EULAR task force (TF) comprising 27 members was established and the EULAR standardized operating procedures were followed. Results: A systematic literature review (92 articles, 6 abstracts) identified relevant concepts and possible criteria. Four overarching principles (OAPs) address the subset of patients with PsA with an unsatisfactory treatment response despite best standard of care and for which the causes are likely multi-factorial (Table 1). Six Points to consider (PtC) highlight relevant criterion including failure to achieve or maintain response to ≥2 b/tsDMARDs with ≥2 different mechanisms of action; management of signs and symptoms perceived as problematic by the rheumatologist and/or the patient, and evidence of persistent disease activity in the presence of extra-musculoskeletal manifestations and/or co-morbidities and/or objective evidence of inflammatory activity (Table 1). The TF voted to change the preferred terminology for the concept from difficult-to-treat and agreed unanimously to develop two definitions: 1) D2M PsA, a wider concept including drivers such as inflammation, co-morbidities, psycho-social or other factors and a subgroup of 2) TR PsA defined by persistent disease activity and objective evidence of active inflammation (Table 2). Conclusion: EULAR proposes two consensus definitions to identify a D2M PsA population including a TR subgroup. These definitions should now be applied in well-designed observational and interventional studies to characterise the phenotypes of these two populations, test interventions and guide further research in order to understand disease pathogenesis and improve care. REFERENCES: NIL . Acknowledgements: NIL . Disclosure of Interests: Helena Marzo-Ortega AbbVie, Biogen, Eli-Lilly, Janssen, Novartis, Pfizer, Takeda and UCB, AbbVie, Eli-Lilly, Janssen, Moonlake, Novartis, Pfizer and UCB, Janssen, Novartis, Pfizer and UCB, Stephanie R Harrison Janssen and Novartis, George Fragoulis AbbVie, Amgen, Boerhinger Ingelheim, Farran, GSK, Janssen, MSD, Novartis, Pfizer, UCB, AbbVie, Amgen, Boerhinger Ingelheim, Farran, GSK, Janssen, MSD, Novartis, Pfizer, UCB, Xabier Michelena Janssen and Novartis, Cristina Macía-Villa AbbVie, Amgen, Lilly, Janssen, Novartis, Pfizer and UCB, AbbVie, Amgen, Lilly, Janssen, Novartis, Pfizer and UCB, UCB, Sibel Aydin AbbVie, Fresenius Kabi, Janssen, Lilly, Novartis, Pfizer and UCB, AbbVie, Fresenius Kabi, Janssen, Lilly, Novartis, Pfizer and UCB, AbbVie, Fresenius Kabi, Janssen, Lilly, Novartis, Pfizer and UCB, Andra Bălănescu AbbVie, Amgen, AlphaSigma, Astra-Zeneca, Angellini, Biogen, BMS, Berlin-Chemie, Boerhinger-Ingelheim, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, Sandoz, Teva, UCBA, Zentiva, AbbVie, Amgen, AlphaSigma, Astra-Zeneca, Angellini, Biogen, BMS, Berlin-Chemie, Boerhinger-Ingelheim, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, Sandoz, Teva, UCBA, Zentiva, Heidi Bertheussen: None declared, Christine Bundy AbbVie, Almirall, Beiersdorf, Galapagos, Janssen, Novartis, Pfizer and UCB, AbbVie, Almirall, Beiersdorf, Galapagos, Janssen, Novartis, Pfizer and UCB, AbbVie, Almirall, Beiersdorf, Galapagos, Janssen, Novartis, Pfizer and UCB, Maria Sole Chimenti AbbVie, Amgen, Janssen, Lilly, Novartis, Pfizer and UCB, AbbVie, Amgen, Janssen, Lilly, Novartis, Pfizer and UCB, AbbVie, Amgen, Janssen, Lilly, Novartis, Pfizer and UCB, Paolo Gisondi Amgen, AbbVie, Almirall, Boehringer Ingelheim Janssen, Eli-Lilly, Novartis, Pfizer and UCB, Amgen, AbbVie, Almirall, Boehringer Ingelheim Janssen, Eli-Lilly, Novartis, Pfizer and UCB, Bente Glintborg AbbVie, AlfaSigma, Eli-Lilly, Pfizer and Sandoz, Laure Gossec AbbVie, Amgen, BMS, Celltrion, Janssen, Lilly, MSD, Novartis, Pfizer, Stada, UCB, AbbVie, Biogen, Lilly, Novartis, UCB, Umut Kalyoncu: None declared, Ennio Lubrano Abbvie, Janssen, Lilly, Pfizer, UCB, Abbvie, Janssen, Lilly, Pfizer, UCB, György Nagy Astra Zeneca, AbbVie, Boehringer, GSK, Lilly, Novartis, Pfizer, MSD, Roche, Miltenyi, Richter, SOBI, Swixx and UCB, Astra Zeneca, AbbVie, Boehringer, GSK, Lilly, Novartis, Pfizer, MSD, Roche, Miltenyi, Richter, SOBI, Swixx and UCB, Wendy Wagenaar: None declared, Luis Puig Abbvie, Almirall, Amgen, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Fresenius-Kabi, Horizon, J&J Innovative Medicine, Leo-Pharma, Lilly, Novartis, Pfizer, Samsung-Bioepis, STADA, Sun-Pharma, Takeda, and UCB, Abbvie, Almirall, Amgen, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Fresenius-Kabi, Horizon, J&J Innovative Medicine, Leo-Pharma, Lilly, Novartis, Pfizer, Samsung-Bioepis, STADA, Sun-Pharma, Takeda, and UCB, Abbvie, Almirall, Amgen, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Fresenius-Kabi, Horizon, J&J Innovative Medicine, Leo-Pharma, Lilly, Novartis, Pfizer, Samsung-Bioepis, STADA, Sun-Pharma, Takeda, and UCB, Rubén Queiro AbbVie, Amgen, Janssen, Novartis, Pfizer, UCB and Lilly, AbbVie, Amgen, Janssen, Novartis, Pfizer, UCB and Lilly, Abbvie, Novartis, Janssen, UCB, Proton Rahman Abbott, AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer and UCB, Janssen and Novartis, Russka Shumnalieva AstraZeneca, Novartis, Abbvie, Janssen, AstraZeneca, Novartis, Abbvie, Janssen, Enrique R. Soriano AbbVie, Amgen, BMS, Elea, Janssen, Lilly, Novartis, _Pfizer, UCB, AbbVie, Amgen, BMS, Elea, Janssen, Lilly, Novartis, _Pfizer, UCB, AbbVie, Amgen, BMS, Elea, Janssen, Lilly, Novartis, _Pfizer, UCB, Filip van den Bosch Abbvie, Alfasigma, Amgen, Eli Lilly, Fresenius Kabi, Janssen, Novartis and UCB, Abbvie, Alfasigma, Amgen, Eli Lilly, Fresenius Kabi, Janssen, Novartis and UCB, Marleen G.H. van de Sande Abbvie, Janssen, Eli Lilly, Novartis, UCB, Abbvie, Janssen, Eli Lilly, Novartis, UCB, Janssen, Novartis and UCB, Alexandre Sepriano AbbVie, Novartis, UCB and Lilly, AbbVie, Novartis, UCB and Lilly, Pedro Machado Abbvie, Abcuro, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, Abbvie, Abcuro, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, Stefan Siebert AbbVie, Amgen, AstraZeneca, Janssen, Novartis, Pfizer, Syncona, Teijin Pharma and UCB, AbbVie, Amgen, AstraZeneca, Janssen, Novartis, Pfizer, Syncona, Teijin Pharma and UCB, Amgen (previously Celgene), Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, GSK, 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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,135 | 0,131 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,007 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,007 | 0,006 |
| Science ouverte | 0,007 | 0,010 |
| Intégrité de la recherche | 0,012 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,007 |
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 source (Gemma direct ou Codex distillé), 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 ».