目的 基于脊柱-骨盆矢状面参数、骨折特征及治疗相关等临床特征分析骨质疏松性椎体压缩骨折（OVCF）患者经皮椎体后凸成形术（PKP）术后邻近椎体继发骨折（AVCF）的影响因素。 方法 研究对象选自2020年11月至2023年8月医院收治的OVCF患者240例，对其临床资料进行回顾性分析，均进行PKP术，并随访2年，根据随访期间所选患者是否发生AVCF将其分为发生组（54例）和未发生组（186例）。比较两组临床资料及脊柱-骨盆矢状面参数收集，予以多因素logistic回归分析法分析危险因素，并绘制受试者工作特征（ROC）曲线分析回归方程的预测价值。 结果 发生组有糖尿病、Ⅱ度/Ⅲ度骨折压缩、初始骨折部位为T₁₀—L₂、骨水泥椎间盘渗漏、多节段骨折、椎间盘损伤的患者占比分别为53.70%、62.96%、72.22%、40.74%、44.44%、33.33%，均高于未发生组的24.19%、47.31%、45.70%、13.44%、19.35%、13.98%，椎体高度恢复率低于未发生组，胸椎后凸角（TK）、矢状面偏移（SVA）、T1骨盆角（TPA）高于未发生组（P < 0.05）。有糖尿病（OR = 2.408）、Ⅱ度/Ⅲ度骨折压缩（OR = 2.838）、骨水泥椎间盘渗漏（OR = 1.547）、多节段骨折（OR = 2.155）、椎间盘损伤（OR = 3.043）、TK升高（OR = 2.081）、SVA升高（OR = 2.298）、TPA升高（OR = 1.636）是OVCF患者PKP术后AVCF的独立危险因素（P < 0.05），椎体高度恢复率增加（OR = 0.328）是独立保护因素（P < 0.05）。构建回归方程：logit（P） = -7.087 + 糖尿病 × 0.879 + 骨折压缩程度 × 1.043 + 骨水泥椎间盘渗漏 × 0.436 + 多节段骨折 × 0.768 + 椎间盘损伤 × 1.113 - 椎体高度恢复率 × 1.114 + TK × 0.733 + SVA × 0.832 + TPA × 0.436。logistic多因素回归诊断回归方程构建有效。ROC曲线显示，当logit（P）> 0.174时，曲线下面积（AUC）值为0.898，诊断敏感度、特异度分别为83.33%、82.80%。 结论 有糖尿病、Ⅱ度/Ⅲ度骨折压缩、骨水泥椎间盘渗漏、多节段骨折、椎间盘损伤、TK升高、SVA升高、TPA升高是OVCF患者PKP术后AVCF的独立危险因素，椎体高度恢复率增加是独立保护因素，构建的回归方程预测价值较高。

Objective To analyze the influencing factors for adjacent vertebral compression fractures （AVCF） after percutaneous kyphoplasty （PKP） in patients with osteoporotic vertebral compression fractures （OVCF）， taking into account the sagittal parameters of the spine-pelvis， fracture characteristics， and treatment-related clinical features. Methods The research subjects were selected from OVCF patients admitted to the hospital between November 2020 and August 2023. A total of 240 cases were included， and their clinical data were retrospectively analyzed. All the patients underwent PKP surgery and were followed up for 2 years. Based on whether the selected patients experienced AVCF during the follow-up period， they were divided into the occurrence group （54 cases） and the non-occurrence group （186 cases）. The clinical data and spinal pelvic sagittal plane parameters of the two groups were compared， and the risk factors were analyzed by multivariate Logistic regression analysis. The predictive value of the regression equation was analyzed by receiver operating characteristic （ROC） curves. Results The proportions of patients with diabetes， Ⅱ/Ⅲ degree fracture compression， initial fracture site T₁₀-L₂， bone cement intervertebral disc leakage， multi-segment fracture， and intervertebral disc injury in the occurrence group were 53.70%， 62.96%， 72.22%， 40.74%， 44.44%， and 33.33%， respectively， which were higher than those of 24.19%， 47.31%， 45.70%， 13.44%， 19.35%， and 13.98% in the non-occurrence group. The recovery rate of vertebral body height was lower than that in the non-occurrence group， and the TK， SVA， and TPA were higher than those in the non-occurrence group （P < 0.05）. Diabetes （OR = 2.408）， Ⅱ/Ⅲ degree fracture compression （OR = 2.838）， bone cement disc leakage （OR = 1.547）， multi-level fracture （OR = 2.155）， disc injury （OR = 3.043）， elevated TK （OR = 2.081）， elevated SVA （OR = 2.298）， and elevated TPA （OR = 1.636） were independent risk factors for AVCF in OVCF patients after PKP （P < 0.05）. The increased vertebral height recovery rate （OR = 0.328） was an independent protective factor （P < 0.05）. A regression equation was constructed： logit （P） = -7.087 + diabetes × 0.879 + fracture compression degree × 1.043 + bone cement intervertebral disc leakage × 0.436 + multi-level fracture × 0.768 + intervertebral disc injury × 1.113 - vertebral height recovery rate × 1.114 + TK × 0.733 + SVA × 0.832 + TPA × 0.436. The construction of the Logistic multiple regression diagnostic regression equation is effective. The ROC curve shows that when logit （P） > 0.174， the area under the curve （AUC） value is 0.898， and the diagnostic sensitivity and specificity are 83.33% and 82.80%， respectively. Conclusions Diabetes， Ⅱ/Ⅲ degree fracture compression， bone cement disc leakage， multi-level fracture， disc injury， elevated TK， elevated SVA， and elevated TPA were identified as independent risk factors for AVCF in OVCF patients after PKP， the increased vertebral height recovery rate was an independent protective factor. The regression equation constructed exhibited a high predictive value.