Department of Rheumatology, King's College London School of Medicine, Weston Education Centre, King's College London, London SE5 9RS, UK.
J Rheumatol. 2012 Mar;39(3):470-5. doi: 10.3899/jrheum.110169. Epub 2012 Jan 15.
Optimizing therapeutic strategies to induce remission requires an understanding of the initial features predicting remission. Currently no suitable model exists. We aim to develop a remission score using predictors of remission in early rheumatoid arthritis (RA).
We used a dataset from a UK randomized controlled trial that evaluated intensive treatment with conventional combination therapy, to develop a predictive model for 24-month remission. We studied 378 patients in the trial who received 24 months' treatment. Our model was validated using data from a UK observational cohort (Early RA Network, ERAN). A group of 194 patients was followed for 24 months. Remission was defined as 28-joint Disease Activity Score < 2.6. Logistic regression models were used to estimate the associations between remission and potential baseline predictors.
Multivariate logistic regression analyses showed age, sex, and tender joint count (TJC) were independently associated with 24-month remission. The multivariate remission score developed using the trial data correctly classified 80% of patients. These findings were replicated using ERAN. The remission score has high specificity (98%) but low sensitivity (13%). Combining data from the trial and ERAN, we also developed a simplified remission score that showed that younger men with a TJC of 5 or lower were most likely to achieve 24-month remission. Remission was least likely in older women with high TJC. Rheumatoid factor, rheumatoid nodules, and radiographic damage did not predict remission.
Remission can be predicted using a score based on age, sex, and TJC. The score is relevant in clinical trial and routine practice settings.
优化诱导缓解的治疗策略需要了解预测缓解的初始特征。目前尚无合适的模型。我们旨在使用早期类风湿关节炎（RA）缓解的预测因子开发缓解评分。
我们使用了来自英国随机对照试验的数据，该试验评估了常规联合治疗的强化治疗，以开发 24 个月缓解的预测模型。我们研究了试验中接受 24 个月治疗的 378 例患者。我们使用英国观察性队列（早期 RA 网络，ERAN）的数据验证了我们的模型。对 194 例患者进行了 24 个月的随访。缓解定义为 28 关节疾病活动评分<2.6。使用逻辑回归模型估计缓解与潜在基线预测因子之间的关联。
多变量逻辑回归分析显示年龄、性别和压痛关节计数（TJC）与 24 个月缓解独立相关。使用试验数据开发的多变量缓解评分正确分类了 80%的患者。使用 ERAN 复制了这些发现。缓解评分具有高特异性（98%）但低灵敏度（13%）。结合试验和 ERAN 的数据，我们还开发了一个简化的缓解评分，表明 TJC 为 5 或更低的年轻男性最有可能实现 24 个月的缓解。缓解最不可能发生在 TJC 较高的老年女性中。类风湿因子、类风湿结节和放射学损伤不能预测缓解。
可以使用基于年龄、性别和 TJC 的评分来预测缓解。该评分在临床试验和常规实践环境中具有相关性。
