| 刘金海,王进,刘建斌.基于血清骨代谢标志物的高龄OVCF患者PVP术后伤椎塌陷的危险因素分析及列线图模型构建.骨科,2026,17(4): 332-337. |
| 基于血清骨代谢标志物的高龄OVCF患者PVP术后伤椎塌陷的危险因素分析及列线图模型构建 |
| Risk factors and Nomogram model construction for injured vertebra collapse after percutaneous vertebroplasty in elderly patients with osteoporotic vertebral compression fractures based on serum bone metabolism markers |
| 投稿时间：2025-08-07 |
| DOI：10.3969/j.issn.1674-8573.2026.04.005 |
| 中文关键词: 伤椎塌陷 骨质疏松性椎体压缩骨折 经皮椎体成形术 列线图 风险因素 |
| 英文关键词: Injured vertebra collapse Osteoporotic vertebral compression fractures Percutaneous vertebroplasty Nomogram Risk factors |
| 基金项目:武威市科技计划项目（WW24B01SF072） |
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| 中文摘要: |
| 目的 探讨经皮椎体成形术（percutaneous vertebroplasty，PVP）治疗高龄骨质疏松性椎体压缩骨折（osteoporotic vertebral compression fractures，OVCF）患者术后发生伤椎再塌陷的危险因素，在此基础上构建列线图预测模型，并评价其应用价值。方法 纳入我院2022年1月至2023年6月收治的204例行PVP治疗的高龄OVCF患者作为研究对象。随访时进行X线检查，依据患者骨折塌陷的形态及影像学测量数据判断是否发生伤椎再塌陷。收集记录年龄、性别、术前骨密度T值、抗骨质疏松药物使用情况、骨折部位、术后即刻侧凸角度、伤椎高度恢复等一般资料及临床资料，比较再塌陷患者与未塌陷患者间上述指标的差异；并将单因素分析中差异有统计学意义的变量纳入多因素Logistic回归分析，对OVCF患者术后伤椎再塌陷的独立危险因素展开评估，进而构建列线图预测模型并完成验证。结果 最终纳入178例，其中48例患者术后发生了伤椎再塌陷，发生率为26.97%。与未塌陷患者相比，伤椎再塌陷患者的术前骨密度T值、血钙和25-羟维生素D水平显著降低，术后伤椎高度恢复率显著升高，有糖尿病史、术前椎体裂隙征、发生骨水泥渗漏、团块型骨水泥分布、术后未长期使用抗骨质疏松药物的比例显著增高，差异有统计学意义（P<0.05）。多因素Logistic回归分析结果显示，较低的术前骨密度T值、术前椎体裂隙征、较低的25-羟维生素D水平、骨水泥分布团块状、较高的术后伤椎高度恢复率及术后未长期使用抗骨质疏松药物是高龄OVCF患者术后伤椎再塌陷的独立危险因素（P<0.05）。建立高龄OVCF患者术后伤椎再塌陷危险因素的列线图，拟合优度检验表明模型的准确度较好（χ2=9.685，P=0.253），临床决策曲线也表现为正的净效益，提示该模型的可用性较高。受试者工作特征（ROC）曲线分析结果显示，列线图模型预测高龄OVCF患者术后伤椎再塌陷的曲线下面积为0.915（95% CI：0.867~0.963），灵敏度和特异度分别为95.83%和77.69%。结论 高龄OVCF患者PVP术后发生伤椎再塌陷受多种因素影响，依据危险因素建立的列线图模型具有一定应用价值。 |
| 英文摘要: |
| Objective To investigate the risk factors for postoperative re-collapse of the injured vertebra in elderly patients with osteoporotic vertebral compression fractures (OVCF) treated with percutaneous vertebroplasty (PVP), and to construct and evaluate a Nomogram prediction model based on these factors. Methods A total of 204 elderly patients with OVCF who underwent PVP in our hospital from January 2022 to June 2023 were enrolled. Postoperative follow-up X-rays were performed, and re-collapse of the injured vertebra was determined based on fracture collapse morphology and radiographic measurements. General and clinical data, including age, sex, preoperative bone mineral density (BMD), anti-osteoporosis medication use, fracture location, immediate postoperative scoliosis angle, and height recovery of the injured vertebra, were collected. Differences in these parameters between patients with and without re-collapse were compared. Variables with statistically significant differences in univariate analysis were included in multivariate Logistic regression analysis to evaluate the independent risk factors for postoperative re-collapse of the injured vertebra, followed by the construction and validation of a Nomogram prediction model. Results Cases of 178 patients were finally included. Among them, 48 patients developed postoperative re-collapse of the injured vertebra, representing an incidence rate of 26.97%. Compared with patients without re-collapse, those with re-collapse had significantly lower preoperative BMD T-scores, serum calcium, and 25-hydroxyvitamin D [25(OH)D] levels, as well as a significantly higher height recovery rate of the injured vertebra (P<0.05). Additionally, the proportions of patients with a history of diabetes, preoperative intravertebral cleft (IVC) sign, bone cement leakage, clumped bone cement distribution, and a lack of long-term postoperative anti-osteoporosis therapy were significantly higher in the re-collapse group (P<0.05). Multivariate Logistic regression analysis showed that lower preoperative BMD T-score, preoperative IVC sign, lower 25(OH)D level, clumped bone cement distribution, higher postoperative height recovery rate of the injured vertebra, and lack of long-term postoperative anti-osteoporosis therapy were independent risk factors for postoperative re-collapse of the injured vertebra in elderly patients with OVCF (P<0.05). A Nomogram based on these risk factors was established. The goodness-of-fit test indicated that the model had good accuracy (χ2=9.685, P=0.253), and the decision curve analysis demonstrated a positive net benefit, indicating high clinical utility. Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of the Nomogram model for predicting postoperative re-collapse of the injured vertebra was 0.915 (95% CI: 0.867-0.963), with a sensitivity of 95.83% and a specificity of 77.69%. Conclusion Postoperative re-collapse of the injured vertebra in elderly patients with OVCF after PVP is influenced by multiple factors. The Nomogram model established based on these risk factors holds significant value for clinical application. |
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