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OA23 Time to address the challenge of difficult-to-treat psoriatic arthritis: results from an international survey

2024· article· en· W4395077501 on OpenAlexaff
Helena Marzo-Ortega, Stephanie R Harrison, György Nagy, Pedro Machado, Dennis McGonagle, Sibel Zehra Aydın, Raquel Almodóvar, Wilson Bautista‐Molano, Laure Gossec, Ennio Lobrano, Peter Nash, Fernando Pimentel‐Santos, Enrique R. Soriano, Stefan Siebert

Bibliographic record

VenueLara D. Veeken · 2024
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicinePsoriatic arthritisArthritisDermatologyPsoriasisIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.296
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
Has abstractyes

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