Abstract：
METHODS: Systematic literature review.
OBJECTIVE: To develop a predictive model for osteoporotic vertebral compression fractures (OVCF) in the elderly, utilizing current tools that are sensitive to bone and paraspinal muscle changes.
METHODS: A retrospective analysis of data from 260 patients from October 2020 to December 2022, to form the Model population. This group was split into Training and Testing sets. The Training set aided in creating a nomogram through binary logistic regression. From January 2023 to January 2024, we prospectively collected data from 106 patients to constitute the Validation population. The model\'s performance was evaluated using concordance index (C-index), calibration curves, and decision curve analysis (DCA) for both internal and external validation.
RESULTS: The study included 366 patients. The Training and Testing sets were used for nomogram construction and internal validation, while the prospectively collected data was for external validation. Binary logistic regression identified nine independent OVCF risk factors: age, bone mineral density (BMD), quantitative computed tomography (QCT), vertebral bone quality (VBQ), relative functional cross-sectional area of psoas muscles (rFCSAPS), gross and functional muscle fat infiltration of multifidus and psoas muscles (GMFIES+MF and FMFIES+MF), FMFIPS, and mean muscle ratio. The nomogram showed an area under the curve (AUC) of 0.91 for the C-index, with internal and external validation AUCs of 0.90 and 0.92. Calibration curves and DCA indicated a good model fit.
CONCLUSIONS: This study identified nine factors as independent predictors of OVCF in the elderly. A nomogram including these factors was developed, proving effective for OVCF prediction.
OBJECTIVE: To develop a predictive model for osteoporotic vertebral compression fractures (OVCF) in the elderly, utilizing current tools that are sensitive to bone and paraspinal muscle changes.
METHODS: A retrospective analysis of data from 260 patients from October 2020 to December 2022, to form the Model population. This group was split into Training and Testing sets. The Training set aided in creating a nomogram through binary logistic regression. From January 2023 to January 2024, we prospectively collected data from 106 patients to constitute the Validation population. The model\'s performance was evaluated using concordance index (C-index), calibration curves, and decision curve analysis (DCA) for both internal and external validation.
RESULTS: The study included 366 patients. The Training and Testing sets were used for nomogram construction and internal validation, while the prospectively collected data was for external validation. Binary logistic regression identified nine independent OVCF risk factors: age, bone mineral density (BMD), quantitative computed tomography (QCT), vertebral bone quality (VBQ), relative functional cross-sectional area of psoas muscles (rFCSAPS), gross and functional muscle fat infiltration of multifidus and psoas muscles (GMFIES+MF and FMFIES+MF), FMFIPS, and mean muscle ratio. The nomogram showed an area under the curve (AUC) of 0.91 for the C-index, with internal and external validation AUCs of 0.90 and 0.92. Calibration curves and DCA indicated a good model fit.
CONCLUSIONS: This study identified nine factors as independent predictors of OVCF in the elderly. A nomogram including these factors was developed, proving effective for OVCF prediction.
摘要：
方法：系统文献综述。
目的：建立老年人骨质疏松性椎体压缩骨折（OVCF）的预测模型，利用目前对骨骼和椎旁肌肉变化敏感的工具。
方法：对2020年10月至2022年12月260名患者的数据进行回顾性分析，形成模型人群。该组分为培训和测试集。训练集通过二元逻辑回归帮助创建列线图。从2023年1月到2024年1月，我们前瞻性地收集了106名患者的数据，以构成验证人群。使用一致性指数(C指数)评估模型的性能，校正曲线,以及内部和外部验证的决策曲线分析(DCA)。
结果：该研究包括366名患者。训练和测试集用于列线图构建和内部验证，而前瞻性收集的数据用于外部验证.二元logistic回归确定了9个独立的OVCF危险因素：年龄，骨矿物质密度（BMD），定量计算机断层扫描（QCT），椎骨质量（VBQ），腰大肌的相对功能横截面积（rFCSAPS），多裂肌和腰大肌的总体和功能性肌肉脂肪浸润（GMFIESMF和FMFIESMF），FMFIPS,和平均肌肉比例。列线图显示C指数的曲线下面积（AUC）为0.91，内部和外部验证AUC为0.90和0.92。校准曲线和DCA表明良好的模型拟合。
结论：本研究确定了9个因素是老年人OVCF的独立预测因子。开发了包括这些因素的列线图，证明了OVCF预测的有效性。
目的：建立老年人骨质疏松性椎体压缩骨折（OVCF）的预测模型，利用目前对骨骼和椎旁肌肉变化敏感的工具。
方法：对2020年10月至2022年12月260名患者的数据进行回顾性分析，形成模型人群。该组分为培训和测试集。训练集通过二元逻辑回归帮助创建列线图。从2023年1月到2024年1月，我们前瞻性地收集了106名患者的数据，以构成验证人群。使用一致性指数(C指数)评估模型的性能，校正曲线,以及内部和外部验证的决策曲线分析(DCA)。
结果：该研究包括366名患者。训练和测试集用于列线图构建和内部验证，而前瞻性收集的数据用于外部验证.二元logistic回归确定了9个独立的OVCF危险因素：年龄，骨矿物质密度（BMD），定量计算机断层扫描（QCT），椎骨质量（VBQ），腰大肌的相对功能横截面积（rFCSAPS），多裂肌和腰大肌的总体和功能性肌肉脂肪浸润（GMFIESMF和FMFIESMF），FMFIPS,和平均肌肉比例。列线图显示C指数的曲线下面积（AUC）为0.91，内部和外部验证AUC为0.90和0.92。校准曲线和DCA表明良好的模型拟合。
结论：本研究确定了9个因素是老年人OVCF的独立预测因子。开发了包括这些因素的列线图，证明了OVCF预测的有效性。
