The progression of knee osteoarthritis (OA) can be defined as either radiographic progression or pain progression. This study aimed to construct models to predict radiographic progression and pain progression in patients with knee OA. We retrieved data from the FNIH OA Biomarkers Consortium project, a nested case-control study. A total of 600 subjects with mild to moderate OA (Kellgren-Lawrence grade of 1, 2, or 3) in one target knee were enrolled. The patients were classified as radiographic progressors (n = 297), non-radiographic progressors (n = 303), pain progressors (n = 297), or non-pain progressors (n = 303) according to the change in the minimum joint space width of the medial compartment and the WOMAC pain score during the follow-up period of 24–48 months. Initially, 376 variables concerning demographics, clinical questionnaires, imaging measurements, and biochemical markers were included. We developed predictive models based on multivariate logistic regression analysis and visualized the models with nomograms. We also tested whether adding changes in predictors from baseline to 24 months would improve the predictive efficacy of the models. The predictive models of radiographic progression and pain progression consisted of 8 and 10 variables, respectively, with area under curve (AUC) values of 0.77 and 0.76, respectively. Incorporating the change in the WOMAC pain score from baseline to 24 months into the pain progression predictive model significantly improved the predictive effectiveness (AUC = 0.86). We identified risk factors for imaging progression and pain progression in patients with knee OA over a 2- to 4-year period, and provided effective predictive models, which could help identify patients at high risk of progression.

中文翻译：

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膝骨关节炎患者放射学进展和疼痛进展的预测模型：来自 FNIH OA 生物标志物联盟项目的数据


膝骨关节炎 (OA) 的进展可定义为放射学进展或疼痛进展。本研究旨在构建模型来预测膝关节 OA 患者的影像学进展和疼痛进展。我们从 FNIH OA 生物标志物联盟项目（一项嵌套病例对照研究）中检索数据。总共招募了 600 名目标膝关节患有轻度至中度 OA（Kellgren-Lawrence 1 级、2 级或 3 级）的受试者。根据最小关节间隙的变化，将患者分为放射学进展者 (n = 297)、非放射学进展者 (n = 303)、疼痛进展者 (n = 297) 或非疼痛进展者 (n = 303)随访 24-48 个月期间内侧间室宽度和 WOMAC 疼痛评分。最初，包括有关人口统计、临床问卷、影像测量和生化标志物的 376 个变量。我们开发了基于多元逻辑回归分析的预测模型，并使用列线图可视化模型。我们还测试了从基线到 24 个月添加预测变量的变化是否会提高模型的预测功效。放射学进展和疼痛进展的预测模型分别由 8 个和 10 个变量组成，曲线下面积 (AUC) 值分别为 0.77 和 0.76。将 WOMAC 疼痛评分从基线到 24 个月的变化纳入疼痛进展预测模型可显着提高预测有效性 (AUC = 0.86)。我们确定了膝关节 OA 患者 2 至 4 年内影像学进展和疼痛进展的危险因素，并提供了有效的预测模型，这有助于识别进展高风险的患者。