Real-world Risk of Relapse of Giant Cell Arteritis Treated With Tocilizumab: A Retrospective Analysis of 43 Patients
Bibliographic record
Abstract
Objective Tocilizumab (TCZ), an interleukin 6 (IL-6) receptor antagonist, is approved for giant cell arteritis (GCA) as a cortisone-sparing strategy and in refractory patients. This study assessed the real-world efficacy, safety, and long-term outcomes of patients with GCA treated with TCZ. Methods We conducted a multicenter retrospective observational study at 3 French centers. All patients aged ≥ 50 years who met the American College of Rheumatology (ACR) criteria, and had received at least 1 dose of TCZ were included. Relapse was defined by therapeutic escalation, such as increased doses of corticosteroids (CS), resumption of CS after weaning, or introduction or intensification of adjuvant therapy. Results Between 2013 and 2019, 43 patients were included. Patients were followed up for a median 511 days between GCA diagnosis and inclusion, with 34/43 (79%) patients experiencing relapses. At inclusion, median age was 77 years, and median dose of CS was 15 mg/day. After inclusion, the mean cumulative dose of CS was 2.1 g/year vs 9.4 g/year before inclusion ( P < 2 × 10 –7 ), with 12/43 (28%) patients experiencing relapses on TCZ. Among 29 patients undergoing TCZ discontinuation, 18 (62%) experienced relapses. Factors associated with relapse after inclusion were introduction of TCZ > 6 months after diagnosis ( P = 0.005), absence of ischemic signs at diagnosis ( P = 0.006), relapse rate > 0.8/year ( P = 0.03), and absence of CS tapering ≤ 5 mg/day ( P = 0.03) before inclusion. Serious adverse events occurred in 18/43 patients (42%), including 4 deaths. Conclusion Our results confirm the effectiveness of TCZ for CS sparing, but after discontinuation of treatment, TCZ allows for a prolonged remission in < 50% of patients. Attention must be paid to the tolerance of this long-term treatment in this elderly, heavily treated refractory population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".