收稿日期: 2023-06-12
网络出版日期: 2023-10-10
基金资助
江西省中医药管理局科技计划(2022B715);江西省卫生健康委科技计划(202211676)
Construction and validation of risk warning model of postoperative re⁃fracture in patients with osteoporosis vertebral compressibility fracture
Received date: 2023-06-12
Online published: 2023-10-10
目的 探讨影响骨质疏松椎体压缩性骨折（osteoporotic vertebral compression fractures， OVCF）患者术后再骨折的危险因素，建立风险预警模型并进行验证。 方法 选取2020年1月至2021年6月于南昌市洪都中医院180例行手术治疗的OVCF患者，依据患者是否发生再骨折将其分为再骨折组和未再骨折组，对相关因素进行logistic回归分析，构建列线图风险模型并进行验证。 结果 180例行手术治疗的OVCF患者中，术后再骨折的发生率为22.78%。因素分析结果显示，年龄 ≥ 60岁、骨水泥渗漏、术后低骨密度、高骨水泥注射率和术后后凸Coob角度是OVCF术后再骨折的独立危险因素（P < 0.05），术后长期抗骨质疏松治疗、高BMI是独立保护因素（P < 0.05）。Hosmer-Lemeshow 拟合优度检验结果显示，ROC曲线下面积为0.979（95%CI：0.957 ~ 1.000， P < 0.001），灵敏度为92.70%，特异度为96.40%，最大约登指数为0.891，模型校正曲线总体趋势接近理想曲线。决策曲线分析结果显示为表现为正的净效益。 结论 OVCF患者术后再骨折的发生受多重因素共同影响，根据危险因素建立的风险预警模型具有较好的预测效能。
关键词： 骨质疏松椎体压缩性骨折; 手术; 再骨折; 风险预警模型
田烨 , 袁秋文 , 胡梁深 . 骨质疏松椎体压缩性骨折患者术后再骨折的风险列阵图构建与验证[J]. 实用医学杂志, 2023 , 39(18) : 2294 -2299 . DOI: 10.3969/j.issn.1006-5725.2023.18.002
Objective To explore the risk factors of postoperative re-fracture in patients with osteoporotic vertebral compressibility fracture （OVCF）， and toestablish a risk early warning model and verify it. Methods One hundred and eighty OVCF patients who underwent surgical treatment in nanchang hongdu traditional chinese medicine hospital from January 2020 to June 2021 were selected and divided into a re-fracture group and a nonre-fracture group. Logistic regression analysis was performed， and a line chart risk model was constructed and validated. Results Among 180 OVCF patients who underwent surgical treatment， the incidence of postoperative fractures was 22.78%. Factor analysis showed that age ≥ 60 years old， bone cement leakage， postoperative low bone mineral density， high bone cement injection rate and postoperative kyphosis Coob angle were independent risk factors for re-fracture after OVCF （P < 0.05）. Long-term anti-osteoporosis treatment and high BMI were independent protective factors （P < 0.05）.Hosmer-Lemeshow Test showed that the area under the ROC curve of 0.979 （95% CI： 0.957 ~ 1.000， P < 0.001）， the sensitivity of 92.70%， the specificity of 96.40%， and the maximum Jordan index of 0.891， the overall trend of the model correction curve was close to the ideal curve. The decision curve analysis showed a positive net benefit. Conclusion The occurrence of postoperative re-fracture in OVCF patients is influenced by multiple factors， and the risk warning model established based on risk factors has good predictive performance.
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