Lin Shengliang, Luo Yixin, Xie Yafen, Liao Yuanjing, Niu Shangbo, Zheng Yurong, Que Qiuyang, Ye Shuxi, Liu Fucheng, Feng Lan, Yan Wenjuan, Duan Chongyang, Yang Dehong
Division of Spine Surgery, Department of Orthopaedics, Nanfang Hospital, Southern Medical University, 1838 Guangzhou North Avenue, Guangzhou, Guangdong province, China.
School of Medical Technology, Beijing Institute of Technology, Beijing, China.
Eur Spine J. 2024 Jun 7. doi: 10.1007/s00586-024-08333-3.
This study aimed to develop and validate a new model that focused on the risk of imminent vertebral fractures in women with osteoporosis.
Data from 2,048 patients were extracted from three hospitals, of which 1,720 patients passed the inclusion and exclusion screen. The patients from Nanfang Hospital (NFH) were randomized at a 2:1 ratio to create a training cohort (n = 709) and an internal validation cohort (n = 355), with the patients from the other two hospitals (n = 656) used for external validation. The risk factors included in the imminent osteoporotic vertebral compression fractures (OVCFs) prediction model (labelled TVF) were sorted by the least absolute shrinkage and selection operator and constructed by logistic regression. The area under the receiver operating characteristic curve (AUC), the decision curve, and the clinical impact curves of the optimal model were analyzed to verify the model.
There were 138 and 161 fresh fractures in NFH and the other two hospitals, respectively. The lowest BMD T value and the history of vertebral fracture were integrated into the TVF model. The prediction power of TVF was demonstrated by the AUCs of 0.788 (95% confidence interval [CI], 0.728-0.849) in the training cohort and 0.774 (95% CI, 0.705-0.842) in the internal validation cohort, and 0.790 (95% CI, 0.742-0.839) and 0.741 (95% CI, 0.668-0.813) in the external validation cohorts.
The TVF model demonstrated good discrimination to stratify the imminent risk of OVCFs. We therefore consider the model as a pertinent commencement in the search for more accurate imminent OVCFs prediction.
本研究旨在开发并验证一种聚焦于骨质疏松症女性即将发生椎体骨折风险的新模型。
从三家医院提取了2048例患者的数据，其中1720例患者通过了纳入和排除筛选。南方医院（NFH）的患者按2:1的比例随机分组，形成一个训练队列（n = 709）和一个内部验证队列（n = 355），另外两家医院的患者（n = 656）用于外部验证。即将发生的骨质疏松性椎体压缩骨折（OVCFs）预测模型（标记为TVF）中纳入的风险因素通过最小绝对收缩和选择算子进行排序，并通过逻辑回归构建。分析最佳模型的受试者操作特征曲线（AUC）下面积、决策曲线和临床影响曲线以验证该模型。
NFH和另外两家医院分别有138例和161例新发骨折。最低骨密度T值和椎体骨折病史被纳入TVF模型。TVF的预测能力在训练队列中的AUC为0.788（95%置信区间[CI]，0.728 - 0.849），内部验证队列中为0.774（95%CI，0.705 - 0.842），外部验证队列中分别为0.790（95%CI，0.742 - 0.839）和0.741（95%CI，0.668 - 0.813）。
TVF模型在分层OVCFs即将发生的风险方面表现出良好的区分能力。因此，我们认为该模型是寻找更准确的OVCFs即将发生风险预测的一个相关开端。
