Rheumatology and Clinical Immunology, UMC Utrecht, Utrecht, The Netherlands
Rheumatology and Clinical Immunology, UMC Utrecht, Utrecht, The Netherlands.
BMJ Open. 2020 Jul 28;10(7):e035101. doi: 10.1136/bmjopen-2019-035101.
The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) consortium intends to prospectively describe in detail, preselected patients with knee osteoarthritis (OA), using conventional and novel clinical, imaging, and biochemical markers, to support OA drug development.
APPROACH is a prospective cohort study including 297 patients with tibiofemoral OA, according to the American College of Rheumatology classification criteria. Patients were (pre)selected from existing cohorts using machine learning models, developed on data from the CHECK cohort, to display a high likelihood of radiographic joint space width (JSW) loss and/or knee pain progression.
Selection appeared logistically feasible and baseline characteristics of the cohort demonstrated an OA population with more severe disease: age 66.5 (SD 7.1) vs 68.1 (7.7) years, min-JSW 2.5 (1.3) vs 2.1 (1.0) mm and Knee injury and Osteoarthritis Outcome Score pain 31.3 (19.7) vs 17.7 (14.6), except for age, all: p<0.001, for selected versus excluded patients, respectively. Based on the selection model, this cohort has a predicted higher chance of progression.
Patients will visit the hospital again at 6, 12 and 24 months for physical examination, pain and general health questionnaires, collection of blood and urine, MRI scans, radiographs of knees and hands, CT scan of the knee, low radiation whole-body CT, HandScan, motion analysis and performance-based tests.After two years, data will show whether those patients with the highest probabilities for progression experienced disease progression as compared to those wit lower probabilities (model validation) and whether phenotypes/endotypes can be identified and predicted to facilitate targeted drug therapy.
NCT03883568.
应用公私合作研究促进骨关节炎临床进展(APPROACH)联盟旨在前瞻性详细描述,根据美国风湿病学会分类标准,选择膝关节骨关节炎(OA)的患者,使用传统和新型临床、影像学和生化标志物,以支持 OA 药物开发。
APPROACH 是一项前瞻性队列研究,纳入了 297 名符合美国风湿病学会分类标准的膝骨关节炎患者。患者是使用机器学习模型从现有队列中(预)选择的,这些模型是基于 CHECK 队列的数据开发的,以显示出较高的放射学关节间隙宽度(JSW)损失和/或膝关节疼痛进展的可能性。
选择似乎在逻辑上是可行的,队列的基线特征表明,OA 人群的疾病更严重:年龄 66.5(7.1)岁与 68.1(7.7)岁,最小 JSW 2.5(1.3)mm 与 2.1(1.0)mm,膝关节损伤和骨关节炎结果评分疼痛 31.3(19.7)分与 17.7(14.6)分,除年龄外,所有:p<0.001,分别为选择与排除患者。基于选择模型,该队列有更高的进展预测概率。
患者将在 6、12 和 24 个月时再次到医院就诊,进行体格检查、疼痛和一般健康问卷、血液和尿液采集、膝关节和手部 MRI 扫描、膝关节 X 线、手部 CT 扫描、膝关节低辐射全身 CT、HandScan、运动分析和基于性能的测试。两年后,数据将显示进展概率最高的患者与进展概率较低的患者相比是否经历了疾病进展(模型验证),以及是否可以识别和预测表型/内表型,以促进靶向药物治疗。
NCT03883568。