Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts.
Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts.
J Orthop Res. 2019 Nov;37(11):2420-2428. doi: 10.1002/jor.24413. Epub 2019 Jul 29.
We assessed whether adding magnetic resonance (MR)-based features to a base model of clinically accessible participant characteristics (i.e., serological, radiographic, demographic, symptoms, and physical function) improved classification of adults who developed accelerated radiographic knee osteoarthritis (AKOA) or not over the subsequent 4 years. We conducted a case-control study using radiographs from baseline and the first four annual visits of the osteoarthritis initiative to define groups. Eligible individuals had no radiographic KOA in either knee at baseline (Kellgren-Lawrence [KL] grade <2). We classified two groups matched on sex (i) AKOA: at least one knee developed advanced-stage KOA (KL = 3 or 4) within 48 months and (ii) did not develop AKOA within 48 months. The MR-based features were assessments of bone, effusion/synovitis, tendons, ligaments, cartilage, and menisci. All characteristics and MR-based features were from the baseline visit. Classification and regression tree analyses were performed to determine classification rules and identify statistically important variables. The CART models with and without MR features each explained approximately 40% of the variability. Adding MR-based features to the model yielded modest improvements in specificity (0.90 vs. 0.82) but lower sensitivity (0.62 vs. 0.70) than the base model. There was consistent evidence that serum glucose, effusion-synovitis volume, and cruciate ligament degeneration are statistically important variables in classifying individuals who will develop AKOA. We found common MR-based measures failed to dramatically improve classification. These findings also show a complex interplay among participant characteristics and a need to identify novel characteristics to improve classification. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2420-2428, 2019.
我们评估了在基于临床可获得的参与者特征(即血清学、影像学、人口统计学、症状和身体功能)的基本模型中添加磁共振(MR)特征是否可以改善在随后的 4 年内发展为加速放射学膝关节骨关节炎(AKOA)或未发展为 AKOA 的成年人的分类。我们使用骨关节炎倡议的基线和前四年的放射照片进行了病例对照研究,以定义组。合格的个体在基线时两个膝关节均无放射学 KOA(Kellgren-Lawrence [KL] 分级<2)。我们将两组按照性别进行匹配:(i)AKOA:至少一个膝关节在 48 个月内发展为晚期 KOA(KL=3 或 4),(ii)在 48 个月内未发展为 AKOA。基于 MR 的特征是对骨骼、积液/滑膜炎、肌腱、韧带、软骨和半月板的评估。所有特征和基于 MR 的特征均来自基线就诊。进行分类和回归树分析以确定分类规则并确定统计学重要变量。具有和不具有 MR 特征的 CART 模型各自解释了大约 40%的可变性。与基本模型相比,将基于 MR 的特征添加到模型中可适度提高特异性(0.90 对 0.82),但敏感性降低(0.62 对 0.70)。有一致的证据表明,血清葡萄糖、积液-滑膜炎体积和交叉韧带变性是分类将发展为 AKOA 的个体的统计学重要变量。我们发现常见的基于 MR 的测量方法未能显著改善分类。这些发现还表明参与者特征之间存在复杂的相互作用,需要确定新的特征来改善分类。©2019 骨科研究协会。由 Wiley Periodicals,Inc. 出版。J Orthop Res 37:2420-2428,2019。