Kraus Virginia Byers, Feng Sheng, Wang ShengChu, White Scott, Ainslie Maureen, Brett Alan, Holmes Anthony, Charles H Cecil
Duke University Medical Center, Durham, North Carolina 27710, USA.
Arthritis Rheum. 2009 Dec;60(12):3711-22. doi: 10.1002/art.25012.
To evaluate the effectiveness of using subchondral bone texture observed on a radiograph taken at baseline to predict progression of knee osteoarthritis (OA) over a 3-year period.
A total of 138 participants in the Prediction of Osteoarthritis Progression study were evaluated at baseline and after 3 years. Fractal signature analysis (FSA) of the medial subchondral tibial plateau was performed on fixed flexion radiographs of 248 nonreplaced knees, using a commercially available software tool. OA progression was defined as a change in joint space narrowing (JSN) or osteophyte formation of 1 grade according to a standardized knee atlas. Statistical analysis of fractal signatures was performed using a new model based on correlating the overall shape of a fractal dimension curve with radius.
Fractal signature of the medial tibial plateau at baseline was predictive of medial knee JSN progression (area under the curve [AUC] 0.75, of a receiver operating characteristic curve) but was not predictive of osteophyte formation or progression of JSN in the lateral compartment. Traditional covariates (age, sex, body mass index, knee pain), general bone mineral content, and joint space width at baseline were no more effective than random variables for predicting OA progression (AUC 0.52-0.58). The predictive model with maximum effectiveness combined fractal signature at baseline, knee alignment, traditional covariates, and bone mineral content (AUC 0.79).
We identified a prognostic marker of OA that is readily extracted from a plain radiograph using FSA. Although the method needs to be validated in a second cohort, our results indicate that the global shape approach to analyzing these data is a potentially efficient means of identifying individuals at risk of knee OA progression.
评估利用基线时X线片观察到的软骨下骨纹理来预测膝关节骨关节炎(OA)3年进展情况的有效性。
骨关节炎进展预测研究中共有138名参与者在基线时和3年后接受了评估。使用市售软件工具,对248个未置换膝关节的固定屈曲位X线片进行胫骨内侧平台软骨下骨的分形特征分析(FSA)。根据标准化膝关节图谱,OA进展定义为关节间隙狭窄(JSN)或骨赘形成改变1级。使用一种基于分形维曲线的整体形状与半径相关性的新模型对分形特征进行统计分析。
基线时胫骨内侧平台的分形特征可预测膝关节内侧JSN进展(受试者操作特征曲线下面积[AUC]为0.75),但不能预测骨赘形成或外侧间室JSN进展。传统协变量(年龄、性别、体重指数、膝关节疼痛)、总体骨矿物质含量和基线时的关节间隙宽度在预测OA进展方面并不比随机变量更有效(AUC为0.52 - 0.58)。效果最佳的预测模型结合了基线时的分形特征、膝关节对线、传统协变量和骨矿物质含量(AUC为0.79)。
我们确定了一种OA预后标志物,可通过FSA从普通X线片中轻松提取。尽管该方法需要在第二个队列中进行验证,但我们的结果表明,分析这些数据的整体形状方法是识别有膝关节OA进展风险个体的一种潜在有效手段。