MétaCan
Menu
← Back to cohort
Record W4411416212 · doi:10.1016/j.ard.2025.06.119

POS0759 CHARACTERISTICS OF RELAPSES AND THERAPEUTIC MANAGEMENTS IN GIANT CELL ARTERITIS IN MODERN ERA, NEWTON STUDY

2025· article· en· W4411416212 on OpenAlexaff
A. Kante, G. Peyrac, N. Lomba Goncalves, P. Cacoub, Karim Sacré, D. Saadoun, T. Papo, J.F. Alexandra, V. Pagis, V. Bourdin, P. Richette, A. Vanjak, A. Latourte, D. Elessa, R. Burlacu, K. Champion, Blanca Amador Borrero, Ana Rita Lopes, A. Depond, Pierre Bonnin, Alexandre Boutigny, F. Paycha, Anne Couvelard, H. Adle, P. Reiner, Aude Couturier, A. Régent, B. Chaigne, Yann Nguyen, A. Lefort, O. Bory, E. Aslangul, S. Mouly, D. Sène, Viet‐Thi Tran, C. Comarmond

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsMedicineGiant cell arteritisArteritisImmunologyPathologyVasculitisDisease

Abstract

fetched live from OpenAlex

Background: The management of giant cell arteritis (GCA) has evolved with the arrival of tocilizumab (TCZ) and the use of PET/CT. In modern era, a double clinical challenge persists: to reduce relapse rate and glucocorticoids (GC) exposure. Objectives: Our objective is to describe the characteristics of relapses and outcomes of patients with recent diagnosis of GCA in current care. Methods: The NEWTON cohort is a French multicentric retrospective cohort based on data collected from GCA patients diagnosed after 2016 and who satisfied the ACR/EULAR 2022 criteria. Relapse definition was 1/ clinical symptom related to GCA and/or elevated C-reactive protein and/or worsening or new vascular lesion, in a patient previously in remission, and 2/ the need for the reinstitution or an increase in prednisone, and/or the addition of, or a change in, immunosuppressive drug (IS). Relapse characteristics, outcomes, factor associated with the first relapse and therapeutic managements were analysed. Results: We identified 211 GCA diagnosed between 2017 and 2023, with a mean (± SD) age at diagnosis of 77.2 (± 9.58) years, female predominance (n=142; 67.3%) and followed up for a median duration [IQR Q1; Q3] of 35 [19; 56] months. GCA relapse occurred in 109/211 (51.6%) patients with 240 relapses. The median time at first relapse was 261 [125; 468] days, following GCA diagnosis. Most relapses occurred when GC therapy was still not discontinued (Figure 1). At relapse, prednisone discontinuation was observed in 40/240 (16.7%), the median dose of prednisone was 6.5 [0; 12.5] mg daily, increased to 20 [10; 35] mg daily after therapeutic intensification. Relapses characteristics included clinical and biological criteria in 82/240 (34%), clinical criteria alone in 69/240 (29%), biological criteria alone in 36/240 (15%), clinical and imaging criteria in 15/240 (6%), imaging and biological criteria in 7/240 (3%) or imaging criteria alone in 7/240 (3%). Therapeutic intensifications following relapse included reinstitution or increase in GC alone in 43%, GC and IS intensification in 32%, and addition of IS alone in 25%. During the disease course, 64/211 (30%) patients received TCZ either from diagnosis in 16/64 (25%), either at relapse in 48/64 (75%). Subcutaneous TCZ was used in 41/64 (64%) and intravenous TCZ in 23/64 (36%). Among them, 31 (48%) patients discontinued TCZ, 19 (30%) because of remission while 12 (18%) patients discontinued because of TCZ adverse events. After TCZ discontinuation with a median follow-up of 17.5 [11.5; 31] months, 11/31 (35.5%) patients relapsed in a median time of 133 [90; 303.5] days. Twenty (64.5%) patients did not relapse after TCZ cessation with a median follow-up of 511 [153.3; 611.5] days. Multivariable Cox regression model, including clinical symptom and age at GCA diagnosis, gender, vascular lesion in different topography related to GCA as covariates, showed that only limb arteries involvement (HR 1.9 [1.23-2.98], P<0.01) at diagnosis was associated with GCA relapse (Figure 2). Conclusion: Relapses occur mainly during the year following diagnosis, despite GC are not discontinued. The use of TCZ concerns a third of GCA recently diagnosed, however more than one third relapsed after TCZ cessation. Limb arteries involvement at GCA diagnosis is a predictor of relapse. REFERENCES: [1] Goncalves L, Tran V-T, Chauffier J, Bourdin V, Nassarmadji K, Vanjak A, et al. [Clinical characteristics and follow-up of 60 patients with recent diagnosis of giant cell arteritis, NEWTON study]. Rev Med Interne 2024:S0248-8663(23)01322-X. [2] Alba MA, Kermani TA, Unizony S, Murgia G, Prieto-González S, Salvarani C, et al. Relapses in giant cell arteritis: Updated review for clinical practice. Autoimmun Rev 2024;23:103580. [3] Hellmich B, Agueda A, Monti S, Buttgereit F, Boysson H de, Brouwer E, et al. 2018 Update of the EULAR recommendations for the management of large vessel vasculitis. Ann Rheum Dis 2020;79:19–30. [4] Maz M, Chung SA, Abril A, Langford CA, Gorelik M, Guyatt G, et al. 2021 American College of Rheumatology/Vasculitis Foundation Guideline for the Management of Giant Cell Arteritis and Takayasu Arteritis. Arthritis Rheumatol 2021;73:1349–1365. [5] Boysson H de, Devauchelle-Pensec V, Agard C, André M, Bienvenu B, Bonnotte B, et al. French protocol for the diagnosis and management of giant cell arteritis. Rev Med Interne 2024:S0248-8663(24)00810–5. Figure 1Curves and bars proportion of study population according to relapse status and GC therapy discontinuation during follow-up. Light green area (1 ) represents the proportion of ACG patients with no relapse and discontinuation of GC therapy, red area (2 ) = the proportion of ACG patients who relapse when GC therapy is discontinued, orange area (3 ) = the proportion of ACG patients who relapse under GC therapy and dark green area (4 ) = the proportion of ACG patients with no relapse under GC therapy. Figure 2Kaplan-Meier curves of study population. Patients with limb arteries involvement at GCA diagnosis (green curve _1) had higher rates of relapse than patients without limb arteries involvement (blue curve _0) (log-rank; P < 0.01). Acknowledgements: SNFMI, FAI2R, Chugaï Pharma. Disclosure of Interests: None declared . © 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.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.287
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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the Rheumatic Diseases→Same topicVasculitis and related conditions→French-language works237,207→