OA23 Time to address the challenge of difficult-to-treat psoriatic arthritis: results from an international survey
Notice bibliographique
Résumé
Abstract Background/Aims The term difficult-to-treat psoriatic arthritis (D2TPsA) is increasingly used to describe a small but growing number of patients who fail to respond to multiple biological disease-modifying treatments (bDMARDs). There is, however, no consensus on the scope and treatment of so-called D2TPsA. This study aimed to explore international rheumatologists’ emerging perceptions of the terminology, scope and treatment of D2TPsA Methods A web-based survey consisting of 19 questions, including multiple choice and free text responses was designed, peer-reviewed and piloted by a team of expert rheumatology consultants with a special interest in PsA. The purpose of the survey was to characterise rheumatologist opinions of D2TPsA. The survey was distributed internationally via professional organisations, mailing lists and social media between 1st June - 31stJuly 2023. Results were analysed using R Studio. Results Two-hundred and forty-four (244) rheumatologists responded to the survey across 5 continents (Europe=79.4%, South America=11.5%, Asia=3.7%, North America=3.3%, Australia=0.8%). The majority (96.2%) were aged 30-69, therefore reflecting opinions spanning the entirety of the workforce, including junior trainees and senior consultants. Most (72.5%) worked in academic/teaching institutions and 37.5% cared for >300 PsA patients in their service. Despite this, only 20.5% of clinicians worked in a combined rheumatology/dermatology service.[HMO1] There was a range of opinion on what constituted D2TPsA, the most popular definition being failure of ≥ 2 b/tsDMARD classes (34.8%) with 68.3% wanting failure of ≥ 1 csDMARD to be included. Considering the subgroup with true resistant or refractory disease, 74.3% supported a more pragmatic definition, that being “failure of all available bDMARD classes”. Nearly all respondents felt that “low disease activity” was a more realistic target to achieve than “remission” (86.5%). There was disagreement on use of single (38.1%) vs. composite measures (40.6%) for the assessment of disease activity, and thus treatment response. Of note, 41% preferred the term “difficult-to-manage” as opposed to “difficult-to-treat” as this would encompass factors other than drug non-response such as presence of comorbidities, mental health and lack of efficacy. Others suggested that a holistic approach to the diagnosis and management of D2TPsA was important. Yet, most physicians felt that the diagnosis and treatment of D2TPsA should include reference to relevant physical and psychosocial comorbidities such as cardiometabolic disease, mental health, infection and malignancy. Conclusion This survey showcases the breadth of rheumatologist opinions of what may constitute D2T PsA, highlighting commonalities and disagreements in the terminology. These now require further discussion and debate in a consensus working group including allied specialties and patient representatives in order to agree a standardized definition for D2TPsA to be used in the clinical and research settings and enable guidelines and management pathways to be developed. Disclosure H. Marzo-Ortega: Honoraria; HMO recieved honoraria/speaker fees from AbbVie, Biogen, Eli-Lily, Janssen, Moonlake, Novartis, Pfizer, Takeda and UCB. Grants/research support; HMO received grant support from Janssen, Novartis and UCB. S.R. Harrison: Honoraria; SRH recieved fees from Novartis and Janssen to give a non-promotional educational lecture. Other; SRH received support from UCB and Janssen to attend EULAR conference. G. Nagy: Honoraria; •GN has received consulting/speaker’s fees from Abbvie, Amgen, Astra Zeneca, Lilly, Janssen, Miltényi, Novartis, Pfizer, Richter, Roche, Sobi and Swixx. P.M. Machado: Honoraria; PMM has received consulting/speaker’s fees from Abbvie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB. D.G. McGonagle: None. S.Z. Aydin: Consultancies; SZA received consultancy feeds from: AbbVie, Janssen, Lilly, Novartis, Pfizer, Fresenius-Kabi, UCB. Grants/research support; SZA received research grants from AbbVie, Lilly, Novartis, UCB, Pfizer, Fresenius-Kabi. R. Almodovar: Honoraria; RA has received consulting/speaker’s fees from Abbvie, Almirall, Amgen, Galápagos, Gebro, Janssen, Lilly, MSD, Nordic, Novartis, Pfizer, UCB. W. Bautista-Molano: None. L. Gossec: Consultancies; LG recieved consultancy fees from: AbbVie, Amgen, BMS, Celltrion, Galapagos, Janssen, Lilly, MSD, Novartis, Pfizer, Sandoz, UCB. Grants/research support; LG received research grants from: AbbVie, Biogen, Lilly, Novartis, UCB. E. Lobrano: None. P. Nash: None. F. Pimentel dos Santos: None. E. Soriano: Consultancies; ERS has received consulting/speaker’s fees and/or grant support from Abbvie, Amgen, BMS, Lilly, Janssen, Novartis, Pfizer, Roche and UCB. S. Siebert: Consultancies; SS has recieved consultancy/ speakers fees from: consultancy/speaker fees from AbbVie, Amgen, AstraZeneca, Eli Lilly, GSK, Janssen, UCB. Grants/research support; SS has received institutional research grants from Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, GSK, Janssen 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 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 | 0,007 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
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 ».