Department of Orthopedic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Graduate School, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Public Health. 2024 Mar 21;12:1380218. doi: 10.3389/fpubh.2024.1380218. eCollection 2024.
To explore the influencing factors of osteoporotic fractures (OPF) in patients with osteoporosis, construct a prediction model, and verify the model internally and externally, so as to provide reference for early screening and intervention of OPF in patients with osteoporosis.
Osteoporosis patients in the First Affiliated Hospital of Soochow University were selected, and the medical records of patients were consulted through the Hospital Information System (HIS) and the data management platform of osteoporosis patients, so as to screen patients who met the criteria for admission and discharge and collect data. SPSS 26.0 software was used for single factor analysis to screen statistically significant variables ( < 0.05). The influencing factors of OPF were determined by multivariate analysis, and a binary Logistic regression model was established according to the results of multivariate analysis. Hosmer-Lemeshow (H-L) goodness of fit and receiver operating characteristic curve (ROC) were used to test the model's efficiency, and Stata 16.0 software was used to verify the Bootstrap model, draw the model calibration curve, clinical applicability curve and nomogram.
In this study, the data of modeling set and verification set were 1,435 and 580, respectively. There were 493 (34.4%) cases with OPF and 942 (65.6%) cases without OPF in the modeling set. There were 204 (35.2%) cases with OPF and 376 (64.8%) cases without OPF. The variables with statistically significant differences in univariate analysis are Age, BMI, History of falls, Usage of glucocorticoid, ALP, Serum Calcium, BMD of lumbar, BMD of feminist neck, value of feminist neck, BMD of total hip and value of total hip. The area under ROC curve of the risk prediction model constructed this time is 0.817 [95%CI (0.794 ~ 0.839)], which shows that the model has a good discrimination in predicting the occurrence of OPF. The optimal threshold of the model is 0.373, the specificity is 0.741, the sensitivity is 0.746, and the AUC values of the modeling set and the verification set are 0.8165 and 0.8646, respectively. The results of Hosmer and Lemeshow test are modeling set: (χ = 6.551, = 0.586); validation set: [(χ = 8.075, = 0.426)]. The calibration curve of the model shows that the reference line of the fitted curve and the calibration curve is highly coincident, and the model has a good calibration degree for predicting the occurrence of fractures. The net benefit value of the risk model of osteoporosis patients complicated with OPF is high, which shows that the model is effective.
In this study, a OPF risk prediction model is established and its prediction efficiency is verified, which can help identify the high fracture risk subgroup of osteoporosis patients in order to choose stronger intervention measures and management.
探讨骨质疏松患者发生骨质疏松性骨折（OPF）的影响因素，构建预测模型，并对模型进行内部和外部验证，为骨质疏松患者 OPF 的早期筛查和干预提供参考。
选取苏州大学附属第一医院骨质疏松症患者，通过医院信息系统（HIS）和骨质疏松症患者数据管理平台查阅患者病历，筛选符合入院和出院标准的患者并采集数据。采用 SPSS 26.0 软件进行单因素分析，筛选出有统计学意义的变量（ < 0.05）。采用多因素分析确定 OPF 的影响因素，并根据多因素分析结果建立二项 Logistic 回归模型。采用 Hosmer-Lemeshow（H-L）拟合优度和受试者工作特征曲线（ROC）检验模型的效率，采用 Stata 16.0 软件验证 Bootstrap 模型，绘制模型校准曲线、临床适用性曲线和列线图。
本研究建模集和验证集的数据分别为 1435 例和 580 例。建模集中 493（34.4%）例发生 OPF，942（65.6%）例未发生 OPF；验证集中 204（35.2%）例发生 OPF，376（64.8%）例未发生 OPF。单因素分析中差异有统计学意义的变量有年龄、BMI、跌倒史、糖皮质激素使用史、ALP、血清钙、腰椎 BMD、股骨颈 BMD、股骨颈 值、全髋 BMD 和全髋 值。本次构建的风险预测模型的 ROC 曲线下面积为 0.817[95%CI（0.794 ~ 0.839）]，提示该模型对 OPF 发生的预测具有较好的区分度。模型的最佳截断值为 0.373，特异度为 0.741，灵敏度为 0.746，建模集和验证集的 AUC 值分别为 0.8165 和 0.8646。Hosmer 和 Lemeshow 检验结果为建模集：（χ = 6.551， = 0.586）；验证集：（χ = 8.075， = 0.426）。模型校准曲线显示拟合曲线的参考线与校准曲线高度吻合，模型对骨折发生的预测具有较好的校准度。骨质疏松症患者并发 OPF 的风险模型的净获益值较高，表明该模型有效。
本研究建立了 OPF 风险预测模型并验证了其预测效能，有助于识别出骨质疏松患者骨折风险较高的亚组，以便选择更强的干预措施和管理。
