Jia Man, Dong Qinzuo, Ren Zeqin, Xing Liwei, Li Jinjie, Wang Xiaomei, Yu Shun, Wang Xiaoyan, Zhao Rong
The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Acupuncture Department and Department of Information Technology, People's Hospital of Wenshan Zhuang and Miao Autonomous Prefecture, Wenshan, Yunnan, China.
Front Endocrinol (Lausanne). 2025 Jun 5;16:1535163. doi: 10.3389/fendo.2025.1535163. eCollection 2025.
Low back pain (LBP) is one of the most common symptoms of osteoporosis (OP), but LBP caused by osteoporosis can easily be masked by other causes, leading to misdiagnosis. However, there are currently no convenient tools available to identify patients with low back pain caused by osteoporosis.
We consecutively enrolled 769 patients diagnosed with low back pain in our hospital from January 2019 to March 2024. A total of 355 cases were excluded due to relevant missing data, leaving a final analysis cohort of 414 cases. The dataset was randomly divided into a training group and a validation group at a ratio of 7:3 for further analysis. in this preliminary analysis were selected for subsequent multivariate analysis. Least absolute shrinkage and selection operator(LASSO) was employed to identify the associated risk factors for osteoporosis. Independent variables with P<0.05 in univariate analysis were included in the multivariate analysis to construct the prediction model. Once the regression equation was established, a nomogram was utilized to visualize the prediction model, while receiver operating characteristic (ROC) curve was plotted to evaluate its performance, specifically by calculating the area under the curve (AUC) which represents discrimination ability of the model. To assess goodness-of-fit, calibration curve was generated for evaluating calibration accuracy. Furthermore, decision curve analysis (DCA) served to determine clinical application value of this predictive model. Statistical significance level was set at P < 0.05.
Building upon the LASSO and multivariate Cox regression, eleven variables were significantly associated with OP (i.e., gender, age, history of fracture, history of alcohol consumption, history of rheumatoid arthritis, hematocrit, red blood cell volume distribution width, lymphocyte percentage, triglyceride, potassium ion, and alanine aminotransferase). In training and validation sets, AUCs and C-indexes of the OP prediction models were all greater than 0.8(AUC: 0.914 for training; 0.833 for validation), which indicated excellent predictability of models. On the whole, the calibration curves coincided with the diagonal in two models. DCA indicated that the models had higher clinical benefit than other risk factors. While confirmed the clinical utility of the model, as it outperformed both the 'treat-all' and 'treat-none' strategies.
After verification, our prediction models of OP are reliable and can predict the incidence of osteoporosis, providing valuable guidance for clinical prognosis estimation and individualized administration of patients with LBP(a new way for early identification and intervention of patients with osteoporosis).
腰痛（LBP）是骨质疏松症（OP）最常见的症状之一，但骨质疏松引起的腰痛很容易被其他原因掩盖，导致误诊。然而，目前尚无便捷的工具可用于识别由骨质疏松引起的腰痛患者。
我们连续纳入了2019年1月至2024年3月在我院诊断为腰痛的769例患者。由于相关数据缺失，共排除355例，最终分析队列共414例。将数据集按7:3的比例随机分为训练组和验证组，用于进一步分析。在本次初步分析中选择的变量用于后续多变量分析。采用最小绝对收缩和选择算子（LASSO）来识别骨质疏松症的相关危险因素。单变量分析中P<0.05的自变量纳入多变量分析以构建预测模型。一旦建立回归方程，利用列线图可视化预测模型，同时绘制受试者工作特征（ROC）曲线来评估其性能，具体通过计算代表模型鉴别能力的曲线下面积（AUC）。为评估拟合优度，生成校准曲线以评估校准准确性。此外，决策曲线分析（DCA）用于确定该预测模型的临床应用价值。统计学显著性水平设定为P<0.05。
基于LASSO和多变量Cox回归，11个变量与骨质疏松症显著相关（即性别、年龄、骨折史、饮酒史、类风湿关节炎史、血细胞比容、红细胞体积分布宽度、淋巴细胞百分比、甘油三酯、钾离子和丙氨酸转氨酶）。在训练集和验证集中，骨质疏松症预测模型的AUC和C指数均大于0.8（训练集AUC：0.914；验证集AUC：0.833），表明模型具有良好的预测能力。总体而言，两个模型的校准曲线与对角线重合。DCA表明该模型比其他危险因素具有更高的临床获益。同时证实了该模型的临床实用性，因为它优于“全部治疗”和“不治疗”策略。
经验证，我们的骨质疏松症预测模型可靠，能够预测骨质疏松症的发病率，为腰痛患者的临床预后评估和个体化治疗提供有价值的指导（一种早期识别和干预骨质疏松症患者的新方法）。
