医学
内科学
格雷夫斯病
危险系数
比例危险模型
背景(考古学)
回顾性队列研究
人口
疾病
置信区间
古生物学
环境卫生
生物
作者
Tristan Struja,Marina Kaeslin,Fabienne Boesiger,Rebecca Jutzi,Noemi Imahorn,Alexander Kutz,Luca Bernasconi,Esther Mundwiler,Beat Müeller,Mirjam Christ‐Crain,Fabian Meienberg,Fahim Ebrahimi,Christoph Henzen,Stefan Fischli,Marius E. Kraenzlin,Christian Meier,Philipp Schüetz
出处
期刊:European journal of endocrinology
[Bioscientifica]
日期:2017-01-19
卷期号:176 (4): 413-419
被引量:56
摘要
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 T 4 , 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 < 0.001) and 73.6% in class III (4–6 points) with a hazard ratio of 2.24 (95% CI: 1.64–3.06, P < 0.001). 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI