Department of Rheumatology and Clinical Immunology, University Medical Centre Utrecht,
Department of Rheumatology and Clinical Immunology, University Medical Centre Utrecht.
Rheumatology (Oxford). 2016 Jan;55(1):94-102. doi: 10.1093/rheumatology/kev291. Epub 2015 Aug 27.
Treatment in general is mostly directly aimed at disease activity, and measures such as the DAS28 might therefore present important additional information. Our aim was to develop and validate a model that uses a combination of disease activity (DAS28) and HAQs to estimate EuroQoL 5-dimension scale (EQ5D) utilities.
Longitudinal data from a cohort study in RA patients from the Utrecht Rheumatoid Arthritis Cohort study Group (Stichting Reumaonderzoek Utrecht) who started treatment with a biologic drug were used for mapping and validation. All 702 observations, including DAS28, HAQ and EQ5D assessed at the same time points, were used. The observations were randomly divided into a subset for development of the model (n = 428 observations) and a subset for validation (n = 274). A stepwise multivariable regression analysis was used to test the association of DAS28 (components) and HAQ (domains) with EQ5D. Model performance was assessed using the explained variance (R(2)) and root mean square errors. Observed and predicted utility scores were compared to check for under- or overestimation of the scores. Finally, the performance of the model was compared with published mapping models.
Lower DAS28 score and HAQ items dressing and grooming, arising, eating, walking and activities were associated with higher EQ5D scores. The final model had an explained variance of 0.35 and a lower root mean square error as compared with other models tested. The agreement between predicted and observed scores was fair.
HAQ components estimate EQ5D better than total HAQ. Adding DAS28 to HAQ components does not result in better utility estimations.
治疗主要是直接针对疾病活动,因此 DAS28 等措施可能提供重要的附加信息。我们的目的是开发和验证一种模型,该模型结合疾病活动(DAS28)和 HAQ 来估计欧洲五维健康量表(EQ5D)效用。
使用来自乌得勒支类风湿关节炎队列研究组(Stichting Reumaonderzoek Utrecht)的生物药物治疗开始的 RA 患者队列研究的纵向数据进行映射和验证。所有 702 个观测值,包括 DAS28、HAQ 和 EQ5D,均在同一时间点进行评估。将观测值随机分为模型开发子集(n = 428 个观测值)和验证子集(n = 274 个观测值)。使用逐步多变量回归分析来测试 DAS28(成分)和 HAQ(域)与 EQ5D 的相关性。通过解释方差(R²)和均方根误差来评估模型性能。比较观察到的和预测的效用得分,以检查得分的低估或高估。最后,将模型的性能与已发表的映射模型进行比较。
较低的 DAS28 评分和 HAQ 项目穿衣、起床、吃饭、行走和活动与较高的 EQ5D 评分相关。与其他测试的模型相比,最终模型的解释方差为 0.35,均方根误差较低。预测和观察得分之间的一致性尚可。
HAQ 成分比总 HAQ 更好地估计 EQ5D。将 DAS28 添加到 HAQ 成分中并不会导致更好的效用估计。