目的  探讨老年骨质疏松性胸腰椎压缩性骨折患者经皮椎体后凸成形术（PKP）后再发骨折的风险因素并构建列线图预测模型。   方法  选取2016年1月~2019年11月行PKP治疗的182例老年骨质疏松性胸腰椎压缩性骨折患者为研究对象，术后3年持续跟踪随访，根据术后恢复情况将患者分为再发骨折组（n=36） 与无再发骨折组（n=146）。收集两组患者的临床资料；对计量指标行接受者操作特性（ROC）曲线分析；经 Logistic 回归分析影响PKP术后再发骨折的独立危险因素；R语言软件4.0“rms”包构建列线图预测模型，校正及决策曲线对列线图预测模型进行内部验证及临床预测效能评估。   结果  两组患者在骨密度、损伤椎体数、单节骨水泥注入量、骨水泥分布类型、骨水泥渗漏、PKP术前后椎体高度差、后凸角变化方面的差异具有统计学意义（P＜0.05）。 骨密度、损伤椎体数、单节骨水泥注入量、PKP术前和术后椎体高度差、后凸角变化的曲线下面积(AUC)分别为0.772、0.732、0.722、0.801、0.813，最佳截断值分别为-3.1、2个、3.9 ml、0.4mm、8.7°。骨密度、损伤椎体数、单节骨水泥注入量、骨水泥渗漏、PKP术前后椎体高度差、后凸角变化是影响老年骨质疏松性胸腰椎压缩性骨折患者PKP术后再发骨折的独立危险因素。列线图预测模型的校正曲线与原始曲线及理想曲线接近，C-index 为0.818（95%CI：0.762~0.883），模型拟合度高；列线图预测模型的阈值＞0.22，可提供临床净收益，且临床净收益均高于独立预测因子。   结论  骨密度、损伤椎体数、单节骨水泥注入量、骨水泥渗漏、PKP术前和术后椎体高度差、后凸角变化是影响老年骨质疏松性胸腰椎压缩性骨折患者PKP术后再发骨折的独立危险因素，并构建了预测老年骨质疏松性胸腰椎压缩性骨折患者PKP术后再发骨折的列线图模型。

 Objective  To investigate the risk factors for re-fracture after percutaneous kyphoplasty (PKP) in elderly patients with osteoporotic thoracolumbar compression fractures and to construct a line graph prediction model.    Methods  One hundred and eighty-two elderly patients with osteoporotic thoracolumbar compression fractures treated with PKP from January 2016 to November 2019 were selected for the study, and the patients were continuously followed up for 3 years after surgery. Clinical data were collected from both groups; Receiver operating characteristic (ROC) curve analysis was performed on the measures; Logistic regression analysis was performed to determine the independent risk factors affecting postoperative re-fracture in PKP; the R language software 4.0 “rms” package was used to construct a predictive model for the line graph, and the calibration and decision curves were used to internally validate the predictive model for the line graph and for clinical evaluation of predictive performance.   Results  The differences between the two groups were statistically significant (P<0.05) in terms of bone mineral density (BMD), number of injured vertebrae, single-segment cement injection, type of cement distribution, cement leakage, difference in vertebral body height before and after PKP, and change in posterior convexity angle. The area under the curve (AUC) for BMD, number of injured vertebrae, single-segment cement injection volume, cement leakage, pre-and post-PKP vertebral height difference, and posterior convexity change were 0.772, 0.732, 0.722, 0.801, and 0.813, respectively, and the best cutoff values were -3.1, 2, 3.9 ml, 0.4 mm, and 8.7°, respectively. BMD, number of injured vertebrae, single-segment cement injection volume, cement leakage, pre-and post-PKP vertebral height difference, and posterior convexity change were independent risk factors for re-fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fractures. The calibration curve of the column line graph prediction model was close to the original curve and the ideal curve with a C-index of 0.818 (95% CI: 0.762-0.883), and the model fit was good; the threshold value of the column line graph prediction model was >0.22, which could provide a net clinical benefit, and the net clinical benefit was higher than the independent predictors.   Conclusion  BMD, number of injured vertebrae, single-segment cement injection, cement leakage, pre-and post-PKP vertebral height difference, and posterior convexity angle change are independent risk factors affecting the recurrent fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fracture, and this study constructs a column line graph model to predict the recurrent fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fracture as a predictor for clinical. This study provides an important reference for clinical prevention and treatment, and has clinical application value.