EULAR recommendations for the management of psoriatic arthritis with pharmacological therapies: 2023 update
Bibliographic record
Abstract
OBJECTIVE: New modes of action and more data on the efficacy and safety of existing drugs in psoriatic arthritis (PsA) required an update of the EULAR 2019 recommendations for the pharmacological treatment of PsA. METHODS: Following EULAR standardised operating procedures, the process included a systematic literature review and a consensus meeting of 36 international experts in April 2023. Levels of evidence and grades of recommendations were determined. RESULTS: The updated recommendations comprise 7 overarching principles and 11 recommendations, and provide a treatment strategy for pharmacological therapies. Non-steroidal anti-inflammatory drugs should be used in monotherapy only for mild PsA and in the short term; oral glucocorticoids are not recommended. In patients with peripheral arthritis, rapid initiation of conventional synthetic disease-modifying antirheumatic drugs is recommended and methotrexate preferred. If the treatment target is not achieved with this strategy, a biological disease-modifying antirheumatic drug (bDMARD) should be initiated, without preference among modes of action. Relevant skin psoriasis should orient towards bDMARDs targeting interleukin (IL)-23p40, IL-23p19, IL-17A and IL-17A/F inhibitors. In case of predominant axial or entheseal disease, an algorithm is also proposed. Use of Janus kinase inhibitors is proposed primarily after bDMARD failure, taking relevant risk factors into account, or in case bDMARDs are not an appropriate choice. Inflammatory bowel disease and uveitis, if present, should influence drug choices, with monoclonal tumour necrosis factor inhibitors proposed. Drug switches and tapering in sustained remission are also addressed. CONCLUSION: These updated recommendations integrate all currently available drugs in a practical and progressive approach, which will be helpful in the pharmacological management of PsA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".