External validation of the GREAT score to predict relapse risk in Graves' disease: results from a multicenter, retrospective study with 741 patients

Autor: Fabienne Boesiger, Rebecca Jutzi, Luca Bernasconi, Fabian Meienberg, Marina Kaeslin, Stefan Fischli, Marius E. Kraenzlin, Noemi Imahorn, Christian Meier, Beat Mueller, Fahim Ebrahimi, Alexander Kutz, Esther Mundwiler, Tristan Struja, Mirjam Christ-Crain, Christoph Henzen, Philipp Schuetz
Rok vydání: 2016
Předmět:
Zdroj: European journal of endocrinology. 176(4)
ISSN: 1479-683X
Popis: Context First-line treatment in Graves’ disease is often done with antithyroid agents (ATD), but relapse rates remain high making definite treatment necessary. Predictors for relapse risk help guiding initial treatment decisions. Objective We aimed to externally validate the prognostic accuracy of the recently proposed Graves’ Recurrent Events After Therapy (GREAT) score to predict relapse risk in Graves’ disease. Design, setting and participants We retrospectively analyzed data (2004–2014) of patients with a first episode of Graves’ hyperthyroidism from four Swiss endocrine outpatient clinics. Main outcome measures Relapse of hyperthyroidism analyzed by multivariate Cox regression. Results Of the 741 included patients, 371 experienced a relapse (50.1%) after a mean follow-up of 25.6 months after ATD start. In univariate regression analysis, higher serum free T4, higher thyrotropin-binding inhibitor immunoglobulin (TBII), younger age and larger goiter were associated with higher relapse risk. We found a strong increase in relapse risk with more points in the GREAT score from 33.8% in patients with GREAT class I (0–1 points), 59.4% in class II (2–3 points) with a hazard ratio of 1.79 (95% CI: 1.42–2.27, P P Conclusions Based on this retrospective analysis within a large patient population from a multicenter study, the GREAT score shows good external validity and can be used for assessing the risk for relapse in Graves’ disease, which influence the initial treatment decisions.
Databáze: OpenAIRE