Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
Department of Digital Health, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
Arthritis Res Ther. 2022 Mar 23;24(1):74. doi: 10.1186/s13075-022-02751-8.
Biological disease-modifying antirheumatic drugs (bDMARDs) are effective in the treatment of rheumatoid arthritis. However, as bDMARDs may also lead to adverse events and are expensive, tapering them is of great clinical interest. Tapering according to disease activity-guided dose optimization (DGDO) does not seem to affect long term remission rates, but flares are frequent during this process. Our objective was to develop a model for the prediction of flares during bDMARD tapering using data from routine care and to evaluate its potential clinical impact.
We used a joint latent class model to repeatedly predict the probability of a flare occurring within the next 3 months. The model was developed using longitudinal data on disease activity (DAS28) and other routine care data from two clinics. Predictive accuracy was assessed in cross-validation and external validation was performed with data from the DRESS (Dose REduction Strategy of Subcutaneous tumor necrosis factor inhibitors) trial. Additionally, we simulated the reduction in number of flares and bDMARD dose when implementing the model as a decision aid during bDMARD tapering in the DRESS trial.
Data from 279 bDMARD courses were used for model development. The final model included two latent DAS28-trajectories, bDMARD type and dose, disease duration, and seropositivity. The area under the curve of the final model was 0.76 (0.69-0.83) in cross-validation and 0.68 (0.62-0.73) in external validation. In simulation of prediction-aided decisions, the mean number of flares over 18 months decreased from 1.21 (0.99-1.43) to 0.75 (0.54-0.96). The reduction in he bDMARD dose was mostly maintained, increasing from 54 to 64% of full dose.
We developed a dynamic flare prediction model, exclusively based on data typically available in routine care. Our results show that using this model to aid decisions during bDMARD tapering may significantly reduce the number of flares while maintaining most of the bDMARD dose reduction.
The clinical impact of the prediction model is currently under investigation in the PATIO randomized controlled trial (Dutch Trial Register number NL9798).
生物改善病情的抗风湿药物（bDMARDs）在类风湿关节炎的治疗中具有疗效。然而，由于 bDMARDs 也可能导致不良反应且价格昂贵，因此逐渐减少剂量具有重要的临床意义。根据疾病活动指导的剂量优化（DGDO）逐渐减少剂量似乎不会影响长期缓解率，但在此过程中频繁出现病情复发。我们的目标是使用常规护理数据建立一个预测 bDMARD 逐渐减少过程中病情复发的模型，并评估其潜在的临床影响。
我们使用联合潜在类别模型反复预测接下来 3 个月内病情复发的概率。该模型使用来自两个诊所的疾病活动（DAS28）的纵向数据和其他常规护理数据进行开发。在交叉验证中评估预测准确性，并使用 DRESS（皮下肿瘤坏死因子抑制剂剂量减少策略）试验的数据进行外部验证。此外，我们模拟了在 DRESS 试验中作为 bDMARD 逐渐减少时的决策辅助工具实施该模型时，病情复发次数和 bDMARD 剂量的减少情况。
该研究共纳入 279 例 bDMARD 治疗疗程的数据用于模型开发。最终模型包含两个潜在的 DAS28 轨迹、bDMARD 类型和剂量、疾病持续时间和血清阳性。最终模型的曲线下面积在交叉验证中为 0.76（0.69-0.83），在外部验证中为 0.68（0.62-0.73）。在预测辅助决策的模拟中，18 个月内的平均病情复发次数从 1.21（0.99-1.43）减少到 0.75（0.54-0.96）。bDMARD 剂量的减少大多得以维持，从全剂量的 54%增加到 64%。
我们开发了一种动态复发预测模型，该模型完全基于常规护理中通常可用的数据。我们的结果表明，使用该模型来辅助 bDMARD 逐渐减少过程中的决策可能会显著减少病情复发次数，同时维持大部分 bDMARD 剂量减少。
该预测模型的临床影响目前正在 PATIO 随机对照试验（荷兰试验注册编号 NL9798）中进行研究。
