Department of Joint Surgery, Xi'an Hong Hui Hospital, Xi'an Jiaotong University Health Science Center, No.555 Youyi East Road, Nanshaomen, Xi'an, 710054, Shaanxi Province, People's Republic of China.
Shaanxi University of Traditional Chinese Medicine, Xi'an, 712046, Shaanxi Province, People's Republic of China.
J Orthop Surg Res. 2024 Jan 31;19(1):100. doi: 10.1186/s13018-024-04587-1.
There are many predictions about the progression of natural collapse course of osteonecrosis of the femoral head. Here, we aimed to combine the three classical prediction methods to explore the progression of the natural collapse course.
This retrospective study included 127 patients admitted to our hospital from October 2016 to October 2017, in whom the femoral head had not collapsed. Logistic regression analysis was performed to determine the collapse risk factors, and Kaplan-Meier survival curves were used for femoral head survival analysis. The collapse rate of the femoral head was recorded within 5 years based on the matrix model. The specificity of the matrix model was analyzed using the receiver operating characteristic curve.
A total of 127 patients with a total of 202 hips were included in this study, and 98 hips collapsed during the follow-up period. Multivariate logistics regression analysis showed that the predictive ability of the matrix model was stronger than Association Research Circulation Osseous staging, Japanese Investigation Committee classification, and area (P < 0.05). Kaplan-Meier survival curve showed that the median survival time of femoral head in patients was 3 years. The result of the receiver operating characteristic curve analysis showed that the area under the curve (AUC) of the matrix model had better predictive value (AUC = 0.771, log-rank test: P < 0.001).
We creatively combined the three classical prediction methods for evaluating the progression of the natural collapse course based on the matrix model and found that the higher the score of the matrix model, the higher the femoral head collapse rate. Specifically, the matrix model has a potential value in predicting femoral head collapse and guiding treatment selection.
关于股骨头坏死自然塌陷病程的进展有很多预测。在这里,我们旨在结合三种经典的预测方法来探讨自然塌陷病程的进展。
本回顾性研究纳入了 2016 年 10 月至 2017 年 10 月我院收治的未发生股骨头塌陷的 127 例患者。采用 logistic 回归分析确定塌陷的危险因素,并采用 Kaplan-Meier 生存曲线进行股骨头生存分析。根据矩阵模型记录 5 年内股骨头的塌陷率。使用受试者工作特征曲线分析矩阵模型的特异性。
本研究共纳入 127 例患者,共 202 髋,随访期间 98 髋发生塌陷。多变量 logistic 回归分析显示,矩阵模型的预测能力强于 Association Research Circulation Osseous 分期、日本研究委员会分类和面积(P < 0.05)。Kaplan-Meier 生存曲线显示,患者股骨头的中位生存时间为 3 年。受试者工作特征曲线分析结果表明,矩阵模型的曲线下面积(AUC)具有更好的预测价值(AUC = 0.771,对数秩检验:P < 0.001)。
我们创新性地结合了三种经典的预测方法,基于矩阵模型评估自然塌陷病程的进展,发现矩阵模型的评分越高,股骨头塌陷率越高。具体而言,矩阵模型在预测股骨头塌陷和指导治疗选择方面具有潜在价值。