PDF(Pubmed)
Abstract：
UNASSIGNED: We aimed to create an imaging biomarker for knee shape using knee dual-energy x-ray absorptiometry (DXA) scans and investigate its potential association with subsequent total knee replacement (TKR), independently of radiographic features of knee osteoarthritis and established risk factors.
UNASSIGNED: Using a 129-point statistical shape model, knee shape (expressed as a B-score) and minimum joint space width (mJSW) of the medial joint compartment (binarized as above or below the first quartile) were derived. Osteophytes were manually graded in a subset of images and an overall score was assigned. Cox proportional hazards models were used to examine the associations of B-score, mJSW and osteophyte score with TKR risk, adjusting for age, sex, height and weight.
UNASSIGNED: The analysis included 37,843 individuals (mean age 63.7 years). In adjusted models, B-score was associated with TKR: each unit increase in B-score, reflecting one standard deviation from the mean healthy shape, corresponded to a hazard ratio (HR) of 2.25 (2.08, 2.43), while a lower mJSW had a HR of 2.28 (1.88, 2.77). Among the 6719 images scored for osteophytes, mJSW was replaced by osteophyte score in the most strongly predictive model for TKR. In ROC analyses, a model combining B-score, osteophyte score, and demographics outperformed a model including demographics alone (AUC = 0.87 vs 0.73).
UNASSIGNED: Using statistical shape modelling, we derived a DXA-based imaging biomarker for knee shape that was associated with kOA progression. When combined with osteophytes and demographic data, this biomarker may help identify individuals at high risk of TKR, facilitating targeted interventions.
UNASSIGNED: Using a 129-point statistical shape model, knee shape (expressed as a B-score) and minimum joint space width (mJSW) of the medial joint compartment (binarized as above or below the first quartile) were derived. Osteophytes were manually graded in a subset of images and an overall score was assigned. Cox proportional hazards models were used to examine the associations of B-score, mJSW and osteophyte score with TKR risk, adjusting for age, sex, height and weight.
UNASSIGNED: The analysis included 37,843 individuals (mean age 63.7 years). In adjusted models, B-score was associated with TKR: each unit increase in B-score, reflecting one standard deviation from the mean healthy shape, corresponded to a hazard ratio (HR) of 2.25 (2.08, 2.43), while a lower mJSW had a HR of 2.28 (1.88, 2.77). Among the 6719 images scored for osteophytes, mJSW was replaced by osteophyte score in the most strongly predictive model for TKR. In ROC analyses, a model combining B-score, osteophyte score, and demographics outperformed a model including demographics alone (AUC = 0.87 vs 0.73).
UNASSIGNED: Using statistical shape modelling, we derived a DXA-based imaging biomarker for knee shape that was associated with kOA progression. When combined with osteophytes and demographic data, this biomarker may help identify individuals at high risk of TKR, facilitating targeted interventions.
摘要：
■我们旨在使用膝关节双能X线骨密度仪（DXA）扫描创建膝关节形状的成像生物标志物，并研究其与后续全膝关节置换（TKR）的潜在关联。与膝骨关节炎的影像学特征和已确定的危险因素无关。
■使用129点统计形状模型，得出膝关节形状(以B分数表示)和内侧关节室的最小关节间隙宽度(mJSW)(二值化为第一四分位数以上或以下).在图像的子集中手动对骨赘进行分级,并分配总体评分。Cox比例风险模型用于检查B评分，具有TKR风险的mJSW和骨赘评分，调整年龄，性别,身高和体重。
■分析包括37,843名个体（平均年龄63.7岁）。在调整后的模型中，B分数与TKR相关：B分数每增加一个单位，反映平均健康形状的一个标准偏差，对应于2.25(2.08，2.43)的危险比(HR)，而较低的mJSW的HR为2.28（1.88，2.77）。在6719张骨赘评分图像中，在TKR的最强预测模型中，mJSW被骨赘评分所取代。在ROC分析中，结合B分数的模型，骨赘评分,人口统计学优于仅包括人口统计学的模型（AUC=0.87vs0.73）。
■使用统计形状建模，我们得出了与kOA进展相关的基于DXA的膝关节形状成像生物标志物.结合骨赘和人口统计数据，这种生物标志物可能有助于识别TKR高风险的个体，促进有针对性的干预。
■使用129点统计形状模型，得出膝关节形状(以B分数表示)和内侧关节室的最小关节间隙宽度(mJSW)(二值化为第一四分位数以上或以下).在图像的子集中手动对骨赘进行分级,并分配总体评分。Cox比例风险模型用于检查B评分，具有TKR风险的mJSW和骨赘评分，调整年龄，性别,身高和体重。
■分析包括37,843名个体（平均年龄63.7岁）。在调整后的模型中，B分数与TKR相关：B分数每增加一个单位，反映平均健康形状的一个标准偏差，对应于2.25(2.08，2.43)的危险比(HR)，而较低的mJSW的HR为2.28（1.88，2.77）。在6719张骨赘评分图像中，在TKR的最强预测模型中，mJSW被骨赘评分所取代。在ROC分析中，结合B分数的模型，骨赘评分,人口统计学优于仅包括人口统计学的模型（AUC=0.87vs0.73）。
■使用统计形状建模，我们得出了与kOA进展相关的基于DXA的膝关节形状成像生物标志物.结合骨赘和人口统计数据，这种生物标志物可能有助于识别TKR高风险的个体，促进有针对性的干预。
