0484: Epidemiology and Predictors of Relapse in Giant Cell Arteritis: A Systematic Review and Meta-analysis

Lien Moreel¹, Albrecht Betrains¹, Geert Molenberghs², Steven Vanderschueren¹ and Daniel Blockmans¹, ¹University Hospitals Leuven, KU Leuven, Leuven, Belgium, ²KU Leuven, Leuven, Belgium

Background/Purpose: The aim of this study was to estimate the timing of relapse, the prevalence of multiple relapses and the predictors of relapse in patients with giant cell arteritis (GCA).

Methods: PubMed, Embase and Cochrane databases were searched from inception till November, 30 2021. Outcome measures include cumulative relapse rate (CRR) of first relapse at year 1, 2, and 5 after treatment initiation, CRR of second and third relapse and predictors of relapse. A random-effects model was used.

Results: Thirty studies (2595 patients) were included for timing of relapse, 16 studies (1947 patients) for prevalence of multiple relapses and 40 studies (4213 patients) for predictors of relapse. One-year, 2-year and 5-year CRRs were 32.0% [95% CI 22.4 – 43.6%], 44.3% [95% CI 30.5 – 59.1%], and 46.6% [95% CI 27.2 – 67.1%], respectively. The duration of scheduled glucocorticoid therapy was negatively associated with the 1-year CRR (p = 0.05). CRR of second and third relapse were 29.6% [95% CI 20.6 – 40.0] and 16.9% [95% CI 8.0 – 32.5%], respectively. Female sex (OR 1.43) and large vessel involvement (OR 2.04) were predictors of relapse.

Conclusion: Relapse occurred in almost half of GCA patients mainly during the first two years after diagnosis. One in three patients had multiple relapses. The optimal glucocorticoid tapering schedule, which seeks a balance between the lowest relapse risk and the shortest glucocorticoid duration, needs to be determined in future studies. Longer scheduled glucocorticoid therapy or early introduction of glucocorticoid-sparing agents may be warranted in female patients and patients with large vessel involvement.
Figure 1: Forest plot of the cumulative relapse rate of first relapse at year 1, 2 and 5 after treatment initiation.

Figure 2: Forest plot of the cumulative relapse rate of second and third relapse

Figure 3: Forest plot of predictors of relapse A. Binary predictors B. Continuous predictors
Disclosures: L. Moreel, None; A. Betrains, None; G. Molenberghs, None; S. Vanderschueren, None; D. Blockmans, None.