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奥地利早期关节炎登记处。

The Austrian Early Arthritis Registry.

作者信息

Machold K P, Nell V P, Stamm T A, Eberl G, Steiner G, Smolen J S

机构信息

Division of Rheumatology, Department of Internal Medicine III, University of Vienna, Austria.

出版信息

Clin Exp Rheumatol. 2003 Sep-Oct;21(5 Suppl 31):S113-7.

Abstract

The Austrian Early Arthritis Registry (Austrian Early Arthritis Action, EAA) enrols and follows patients with inflammatory arthritis of very short (< 12 weeks) duration. Currently, data on 375 patients (almost 2000 individual follow-up examinations) have been entered into the EA database. Evaluations of data from 182 patients with a follow-up of at least one year are available. 65% of these patients have RA, as diagnosed using the ACR classification criteria in a cumulative fashion. Approximately 15% of these patients still have no established diagnosis and are being carried forward and observed as cases of "undifferentiated arthritis". In RA patients, the mean DAS 28 decreased significantly from an initial mean score of 5.5 (high disease activity) into the range of low disease activity. At the end of one year a DAS 28 of < 3.2 was observed in 52% of the RA patients. Radiological progression in these RA patients, who also received treatment very early, appears to be less severe than in other cohorts, although direct comparisons are impossible due to different methods of patient selection. In addition, the serological data from our cohort in cooperation with other study groups will allow development and validation of possible prediction algorithms for early arthritis patients which could improve the diagnostic and therapeutic approach to this patient group.

摘要

奥地利早期关节炎登记处(奥地利早期关节炎行动,EAA)招募并跟踪病程非常短(<12周)的炎性关节炎患者。目前,已有375例患者的数据(近2000次个体随访检查)录入EA数据库。现有182例至少随访一年的患者的数据评估结果。这些患者中65%患有类风湿关节炎(RA),采用美国风湿病学会(ACR)分类标准以累积方式诊断。这些患者中约15%仍未确诊,作为“未分化关节炎”病例继续随访观察。在类风湿关节炎患者中,DAS28均值从初始的平均5.5分(高疾病活动度)显著下降至低疾病活动度范围。一年后,52%的类风湿关节炎患者DAS28<3.2。这些极早期接受治疗的类风湿关节炎患者的放射学进展似乎比其他队列中的患者轻,不过由于患者选择方法不同,无法进行直接比较。此外,我们队列与其他研究组合作获得的血清学数据将有助于开发和验证针对早期关节炎患者的可能预测算法,从而改进对该患者群体的诊断和治疗方法。

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