Development and External Validation of a Multivariable Predictive Model for Progression to Difficult-to-Treat Rheumatoid Arthritis in Biologic-Experienced Patients.
Misti L Paudel, Nancy Shadick, Michael Weinblatt, George Reed, Heather J Litman, Joel M Kremer, Dimitrios A Pappas, Daniel H Solomon
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引用次数: 0
Abstract
Objective: Approximately 20% of patients with rheumatoid arthritis (RA) cycle through multiple therapies without achieving treatment goals and are classified as "difficult-to-treat" RA (D2T-RA); however, no risk-prediction tools exist to identify which patients are at highest risk. Our aim was to develop and validate a predictive model for progression to D2T-RA among patients with RA.
Methods: We used data from two large independent observational cohorts of patients with RA to develop and externally validate a multivariable prediction model to identify participants at risk of D2T-RA, defined using EULAR 2021 criteria. We developed a multivariable predictive model for D2T-RA using Random Survival Forests in participants treated with their first biologic and/or targeted synthetic disease modifying anti-rheumatic drug (b/tsDMARD) (derivation cohort). We validated the model in a cohort of participants initiating or switching b/tsDMARD therapies.
Results: A total of 700 participants were in the derivation cohort (84% females, mean age=55 years, median follow-up=40 months, 113 (16%) with D2T-RA), and 2,070 participants were included in the validation cohort (79% females, mean age=56 years, median follow-up=8 months, 571 (28%) with D2T-RA). We observed C-index values of 0.643 (95% CI 0.585-0.698; derivation cohort) and 0.620 (95% CI 0.596-0.643; validation cohort). Calibration measures suggested overall moderate predictive ability. Worsened functional status, pain, fatigue, and global disease activity were consistently top predictors across both cohorts.
Conclusions: Our model demonstrated moderate discrimination and calibration, highlighting the challenge in accurately predicting D2T-RA outcomes. These findings underscore the need for further research to improve predictive performance, potentially through the incorporation of additional biomarkers.
目的：大约20%的类风湿关节炎（RA）患者通过多种治疗循环而未达到治疗目标，并被归类为“难以治疗”的RA (D2T-RA)；然而，目前还没有风险预测工具来确定哪些患者的风险最高。我们的目的是开发和验证RA患者进展为D2T-RA的预测模型。方法：我们使用来自两个大型独立观察性RA患者队列的数据来开发和外部验证一个多变量预测模型，以确定使用EULAR 2021标准定义的D2T-RA风险参与者。我们在首次使用生物和/或靶向合成疾病修饰抗风湿药物（b/tsDMARD）（衍生队列）的参与者中使用随机生存森林建立了D2T-RA的多变量预测模型。我们在开始或转换b/tsDMARD治疗的参与者队列中验证了该模型。结果：衍生队列共有700名参与者（84%为女性，平均年龄为55岁，中位随访=40个月，113名（16%）患有D2T-RA），验证队列共有2070名参与者（79%为女性，平均年龄为56岁，中位随访=8个月，571名（28%）患有D2T-RA）。我们观察到c指数为0.643 （95% CI 0.585-0.698；衍生队列）和0.620 （95% CI 0.596-0.643；验证队列）。校正措施显示整体的预测能力中等。在两个队列中，功能状态恶化、疼痛、疲劳和全球疾病活动始终是最重要的预测因素。结论：我们的模型显示出适度的辨别和校准，突出了准确预测D2T-RA结果的挑战。这些发现强调了进一步研究的必要性，以提高预测性能，可能通过结合其他生物标志物。
期刊介绍：
Arthritis Care & Research, an official journal of the American College of Rheumatology and the Association of Rheumatology Health Professionals (a division of the College), is a peer-reviewed publication that publishes original research, review articles, and editorials that promote excellence in the clinical practice of rheumatology. Relevant to the care of individuals with rheumatic diseases, major topics are evidence-based practice studies, clinical problems, practice guidelines, educational, social, and public health issues, health economics, health care policy, and future trends in rheumatology practice.
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