AB0709 EFFECTIVENESS OF JANUS KINASE INHIBITORS IN GIANT CELL ARTERITIS IN CLINICAL PRACTICE. REAL-WORLD CLINICAL PRACTICE STUDY AND LITERATURE REVIEW
Bibliographic record
Abstract
Background Patients with giant cell arteritis (GCA) can relapse despite glucocorticoids, methotrexate and tocilizumab treatment. The JAK/STAT signalling pathway is involved in the pathogenesis of GCA, and JAK inhibitors (JAKi) are a potential treatment alternative. Baricitinib showed positive results in a small uncontrolled study [1]. Objectives To evaluate the effectiveness of JAKi in GCA. Methods Real-world, retrospective clinical practice study of patients with GCA treated with JAKi. Outcomes assessed included disease relapse and safety. A literature search for other JAKi-treated GCA cases was conducted in PubMed, Embase and the Cochrane library from inception to 12/31/2022. We compared results of the previous baricitinib study (1) and the baricitinib recipients in our series. Results We present 32 patients (27 females [84%], mean age, 72.4 years, relapsing disease 32 [100%]) that received JAKi. The initial JAKi was baricitinib (n=12), tofacitinib (n=10) and upadacitinib (n=10) (Table 1 and Figure 1). After a median [IQR] follow-up of 6 [3-15] months, 23 (72%) achieved and maintained remission, and 9 (28%) patients discontinued the initial JAKi due to relapse (n=7, 22%) or severe adverse events (SAEs) (n=2, 6%) including liver dysfunction and dyspnea/palpitations. The 9 patients failing the initial JAKi were switched to an alternative [JAKi (n=4) or to another immunosuppressant (n=5)]. The literature review identified another 21 GCA patients (17 females, mean age 74.2 years) treated with JAKi, mostly with baricitinib (n=18). Most of these patients benefited from JAKi therapy (Table 1). Patients in our series receiving baricitinib had longer disease duration (median [IQR]36 [24-48] vs 9 [7-21] months; p=0.001) and had received biologics (83% vs 6.7%; p<0.001) more frequently than those in the previous baricitinib study [1]. Remaining baseline features were similar. Conclusion This real-world analysis suggests that JAKi could be effective in GCA, including patients failing other immunosuppressive therapies. The results of an ongoing phase 3 randomized controlled trial are awaited to confirm or rule out this observation. References [1]Koster MJ, et al. Ann Rheum Dis. 2022 Acknowledgements: NIL. Disclosure of Interests Carmen Álvarez-Reguera: None declared, J. Loricera: None declared, Toluwalase Tofade: None declared, Diana Prieto-Peña: None declared, Susana Romero-Yuste: None declared, Eugenio de Miguel: None declared, Anne Riveros: None declared, Iván Ferraz-Amaro: None declared, Santos Castañeda: None declared, Eztizen Labrador-Sánchez: None declared, Olga Maiz: None declared, Elena Becerra-Fernández: None declared, J. Narváez: None declared, E. Galíndez-Agirregoikoa: None declared, Ismael González Fernández: None declared, ANA URRUTICOECHEA-ARANA: None declared, Sebastian Unizony: None declared, Ricardo Blanco Speakers bureau: Abbvie, Pfizer, Roche, lilly, Bristol-Myers, Janssen, Galapagos and MSD., Consultant of: Abbvie, Pfizer, Roche, lilly, Bristol-Myers, Janssen., Grant/research support from: Abbvie, MSD, novartis and Roche.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".