Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China.
Department of Orthopedics, Central Hospital of Shenyang Medical College, Shenyang, Liaoning Province, China.
BMC Med Imaging. 2023 Mar 27;23(1):43. doi: 10.1186/s12880-023-01001-w.
Osteoarthritis (OA) is a leading cause of disability worldwide. However, the existing methods for evaluating OA patients do not provide enough comprehensive information to make reliable predictions of OA progression. This retrospective study aimed to develop prediction nomograms based on MRI cartilage that can predict disease progression of OA.
A total of 600 subjects with mild-to-moderate osteoarthritis from the Foundation for National Institute of Health (FNIH) project of osteoarthritis initiative (OAI). The MRI cartilage parameters of the knee at baseline were measured, and the changes in cartilage parameters at 12- and 24-month follow-up were calculated. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to extract the valuable characteristic parameters at different time points including cartilage thickness, cartilage volume, subchondral bone exposure area and uniform cartilage thickness in different sub regions of the knee, and the MRI cartilage parameters score0, scoreΔ12, and scoreΔ24 at baseline, 12 months, and 24 months were constructed. ScoreΔ12, and scoreΔ24 represent changes between 12 M vs. baseline, and 24 M vs. baseline, respectively. Logistic regression analysis was used to construct the nomogram0, nomogramΔ12, and nomogramΔ24, including MRI-based score and risk factors. The area under curve (AUC) was used to evaluate the differentiation of nomograms in disease progression and subgroup analysis. The calibration curve and Hosmer-Lemeshow (H-L) test were used to verify the calibration of the nomograms. Clinical usefulness of each prediction nomogram was verified by decision curve analysis (DCA). The nomograms with predictive efficacy were analyzed by secondary analysis. Internal verification was assessed using bootstrapping validation.
Each nomogram included cartilage score, KL grade, WOMAC pain score, WOMAC disability score, and minimum joint space width. The AUC of nomogram0, nomogramΔ12, and nomogramΔ24 in predicing the progression of radiology and pain were 0.69, 0.64, and 0.71, respectively. All three nomograms had good calibration. Analysis by DCA showed that the clinical effectiveness of nomogramΔ24 was higher than others. Secondary analysis showed that nomogram0 and nomogramΔ24 were more capable of predicting OA radiologic progression than pain progression.
Nomograms based on MRI cartilage change were useful for predicting the progression of mild to moderate OA.
骨关节炎（OA）是全球导致残疾的主要原因。然而，现有的评估 OA 患者的方法并没有提供足够全面的信息来对 OA 进展做出可靠的预测。本回顾性研究旨在开发基于 MRI 软骨的预测列线图，以预测 OA 的疾病进展。
共纳入来自国立卫生研究院（FNIH）骨关节炎倡议（OAI）项目的 600 例轻中度骨关节炎患者。测量基线时膝关节 MRI 软骨参数，并计算 12 个月和 24 个月随访时软骨参数的变化。采用最小绝对收缩和选择算子（LASSO）回归分析，提取不同时间点有价值的特征参数，包括软骨厚度、软骨体积、软骨下骨暴露面积和膝关节不同亚区的均匀软骨厚度，以及基线、12 个月和 24 个月的 MRI 软骨参数评分 0、评分Δ12 和评分Δ24。评分Δ12 和评分Δ24 分别代表 12 个月 vs. 基线和 24 个月 vs. 基线之间的变化。采用 logistic 回归分析构建列线图 0、列线图Δ12 和列线图Δ24，包括基于 MRI 的评分和危险因素。采用曲线下面积（AUC）评价列线图在疾病进展和亚组分析中的区分度。采用校准曲线和 Hosmer-Lemeshow（H-L）检验验证列线图的校准度。采用决策曲线分析（DCA）验证各预测列线图的临床实用性。通过二次分析对有预测效能的列线图进行分析。采用 bootstrap 验证评估内部验证。
每个列线图均包括软骨评分、KL 分级、WOMAC 疼痛评分、WOMAC 残疾评分和最小关节间隙宽度。列线图 0、列线图Δ12 和列线图Δ24 预测影像学和疼痛进展的 AUC 分别为 0.69、0.64 和 0.71。三个列线图均有较好的校准度。DCA 分析显示，列线图Δ24 的临床有效性更高。二次分析显示，列线图 0 和列线图Δ24 较疼痛进展更能预测 OA 影像学进展。
基于 MRI 软骨变化的列线图可用于预测轻中度 OA 的进展。
