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Abstract Number: 2650

Predictors of Relapse in Giant Cell Arteritis: Data from an International Collaboration

Laure Delaval 1, Raphael Porcher 2, Kenneth Warrington 3, Francesco Muratore 4, Cynthia Crowson 5, Daniel Blockmans 6, Christian Agard 7, Alexis Régent 8, Maxime Samson 9, Loic Guillevin 8, Carlo Salvarani 10 and Benjamin Terrier8, 1Department of Internal Medicine, Hôpital Cochin, Université Paris Descartes, Sorbonne Paris Cité, INSERM Unité 1016, Centre de Référence pour les Maladies Auto-immunes Rares, Paris, France, Paris, France, 2Hotel Dieu Hospital, Paris, France, 3Mayo Clinic Rochester, Rochester, MN, 4Division of Rheumatology, Azienda Unita’ Sanitaria Locale IRCCS di Reggio Emilia, Reggio Emilia, Italy, 5Mayo Clinic Rochester, Rochester, 6Department of General Internal Medicine, University Hospitals Gasthuisberg, Leuven, Belgium, 7CHU Nantes, Nantes, France, 8National Referral Center for Rare Systemic Autoimmune Diseases Paris Cochin, Paris, France, 9Service de Médecine Interne et Immunologie Clinique, CHU Dijon Bourgogne, Hôpital François Mitterrand, Dijon ; Université Bourgogne-Franche Comté, INSERM, EFS BFC, UMR1098, F-21000 Dijon, Dijon, France, 10Division of Rheumatology, Arcispedale Santa Maria Nuova-IRCCS, Reggio Emilia, Italy

Meeting: 2019 ACR/ARP Annual Meeting

Keywords: giant cell arteritis, outcomes and international

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Session Information

Date: Tuesday, November 12, 2019

Title: Vasculitis – Non-ANCA-Associated & Related Disorders Poster III: Giant Cell Arteritis

Session Type: Poster Session (Tuesday)

Session Time: 9:00AM-11:00AM

Background/Purpose: Roughly half of giant-cell arteritis (GCA) patients taking only glucocorticoids (GCs) relapse. The relapse rate seems to reflect, in part, the duration of GC intake, more than the initial dose at induction. However, the GCA-relapse rate varied widely in observational studies and randomized–controlled trials. We aimed to identify factors predictive of GCA relapse based on data from patients enrolled in 3 international GCA cohorts.

Methods: This international study included patients ≥50 years old and fulfilling ACR criteria for GCA from three cohorts : Northern Europe (n=351), US (n=285) and Southern Europe (n=142) cohorts. The primary endpoint was the time from diagnosis to first relapse or death. Relapse-free survival was estimated with the Kaplan–Meier product-limit method. Cox proportional hazards models were stratified by cohort. Missing data were handled through multiple imputation by chained equations. Internal validation of the model was carried out using 200 bootstrap resamples, on which the entire model building strategy was repeated. Model performance was evaluated both by the concordance (c) statistic, as a measure of discrimination, and the calibration curve. The final model was presented with hazard ratios (HR) obtained after shrinkage by the calibration slope and their 95% confidence intervals (95% CI).  

Results: This study included 778 patients (24% men; median age 71 [IQR 61–78] years). Median follow-up was 51 [IQR 24–102] months. During 36 months of follow-up, 382 patients relapsed; relapse-free survival at 36 months was 45.3% (95% CI 41.6–49.2), with marked differences among the cohorts. French and Reggio Emilia cohorts had better relapse-free survival rates than the Mayo Clinic cohort.

To adjust for optimism induced by the model-selection procedure, regression coefficients where shrunk by multiplying them by the calibration slope, which was 0.714, indicating a reasonable level of overfitting in the original model. The final model (corrected for over-optimism) comprised headaches (HR 1.18, 95% CI 1.00–1.39; P=0.052), limb claudication (HR 1.34, 95% CI 0.95–1.89; P=0.091), aortitis (HR 1.20, 95% CI 0.97–1.48; P=0.096) and CRP levels (after logarithmic transformation) (HR 1.11, 95% CI 1.02–1.22; P=0.014). The model was well calibrated, but with relatively limited discriminative ability. Using the pooled coefficients for each imputed dataset yielded a corrected c-statistic of 0.562 (95% CI 0.525–0.598).

We set up a nomogram allowing the calculation of the predicted 3-year relapse free survival. Points (ranging from 0 to a maximum of 100) is attributed for each of the four previous items (Figure A). Total score (ranging from 0 to 200) is used to predict the 3-year relapse free survival in each cohort (Figure B).

Conclusion: The risk of GCA relapse varies among observational cohorts. A model comprising the headaches, limb claudication, aortitis and CRP levels was well calibrated, and could help identify patients at high-risk of relapse. However, this model had relatively limited discriminative ability, possibly due to unmeasured variables that might explain the observed heterogeneity among cohorts.

Nomogram allowing the calculation of the predicted 3-year relapse free survival.


Disclosure: L. Delaval, None; R. Porcher, None; K. Warrington, Eli Lilly, 2, GlaxoSmithKline, 2, roche, 2, 8, Roche, 2, 5, 8, Sanofi, 5, sanofi, 5; F. Muratore, None; C. Crowson, Crescendo Bioscience, 5, Crescendo BioScience Inc., 5, Crescendo Bioscience Inc., 5, Crescendo Biosciences inc., 5, Pfizer, 2; D. Blockmans, None; C. Agard, None; A. Régent, None; M. Samson, None; L. Guillevin, None; C. Salvarani, None; B. Terrier, Grifols, 8, GSK, 8, LFB, 8, Roche, 8.

To cite this abstract in AMA style:

Delaval L, Porcher R, Warrington K, Muratore F, Crowson C, Blockmans D, Agard C, Régent A, Samson M, Guillevin L, Salvarani C, Terrier B. Predictors of Relapse in Giant Cell Arteritis: Data from an International Collaboration [abstract]. Arthritis Rheumatol. 2019; 71 (suppl 10). https://acrabstracts.org/abstract/predictors-of-relapse-in-giant-cell-arteritis-data-from-an-international-collaboration/. Accessed .
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