Abstract：
OBJECTIVE: To establish a predictive model to evaluate the risk of adjacent vertebral refracture (VRF) after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fracture (OVCF) based on perioperative imaging data.
METHODS: This study was a retrospective cohort study which established a predictive model of VRF after PKP for OVCF. Patients who underwent PKP for OVCF in our hospital between January 2018 and December 2020 were enrolled and divided into a refracture group and normal group. Perioperative imaging data including preoperative bone mineral density (BMD), fatty infiltration (FI%) of paravertebral muscle, sagittal parameters of the spine and pelvis, and recovery rate of vertebral height were collected. The prediction model is obtained by multifactor logistic regression analysis.
RESULTS: A total of 242 patients were included, including 23 cases in the VRF group and 219 cases in the normal group. There were statistical differences in BMD, FI%, recovery rate of vertebral height, and sagittal imbalance between the 2 groups. Receiver operating characteristic curve analysis of continuous variables showed that BMD ≤-2.80, FI% ≥40%, and recovery rate of vertebral height ≥ 10% were the cutoff values. Logistic regression analysis showed that BMD ≤-2.80, FI% ≥40%, and sagittal imbalance were independent risk factors for VRF. The area under the curve according to the predicted probability was 0.85 (P < 0.05). After simplifying the model, the total point of the model was 7 points, with a cutoff value of 5 points.
CONCLUSIONS: The prediction model obtained in this study can predict refracture after PKP for OVCF early and effectively. It has an excellent predictive effect which is suitable for clinicians.
METHODS: This study was a retrospective cohort study which established a predictive model of VRF after PKP for OVCF. Patients who underwent PKP for OVCF in our hospital between January 2018 and December 2020 were enrolled and divided into a refracture group and normal group. Perioperative imaging data including preoperative bone mineral density (BMD), fatty infiltration (FI%) of paravertebral muscle, sagittal parameters of the spine and pelvis, and recovery rate of vertebral height were collected. The prediction model is obtained by multifactor logistic regression analysis.
RESULTS: A total of 242 patients were included, including 23 cases in the VRF group and 219 cases in the normal group. There were statistical differences in BMD, FI%, recovery rate of vertebral height, and sagittal imbalance between the 2 groups. Receiver operating characteristic curve analysis of continuous variables showed that BMD ≤-2.80, FI% ≥40%, and recovery rate of vertebral height ≥ 10% were the cutoff values. Logistic regression analysis showed that BMD ≤-2.80, FI% ≥40%, and sagittal imbalance were independent risk factors for VRF. The area under the curve according to the predicted probability was 0.85 (P < 0.05). After simplifying the model, the total point of the model was 7 points, with a cutoff value of 5 points.
CONCLUSIONS: The prediction model obtained in this study can predict refracture after PKP for OVCF early and effectively. It has an excellent predictive effect which is suitable for clinicians.
摘要：
方法：本研究为回顾性队列研究，建立了骨质疏松性椎体压缩性骨折（OVCF）经皮椎体后凸成形术（PKP）后相邻椎体再骨折（VRF）的预测模型。
目的：根据围手术期影像学数据建立预测模型，以评估OVCFPKP术后相邻椎体再骨折的风险。
方法：选取2018年1月至2020年12月在我院行PKP治疗OVCF的患者，分为再骨折组和正常组。围手术期影像学数据包括术前骨密度（BMD），椎旁肌肉的脂肪浸润（FI％），脊柱和骨盆矢状参数，收集椎体高度恢复率。通过多因素logistic回归分析得到预测模型。
结果：共纳入242例患者，其中VRF组23例,正常组219例。BMD有统计学差异,FI%,两组椎体高度和矢状不平衡恢复率。连续变量的ROC曲线分析显示，BMD≤-2.80，FI％≥40％和椎体高度恢复率≥10％为临界值。Logistic回归分析显示,BMD≤-2.80、FI%≥40%、矢状面失衡是VRF的独立危险因素。根据预测概率曲线下面积(AUC)为0.85(P<0.05)。简化模型后，模型的总点是7点，截止值为5点。
结论：本研究中获得的预测模型可以早期有效地预测OVCFPKP后的再骨折。它具有良好的预测效果，适用于临床医生。
目的：根据围手术期影像学数据建立预测模型，以评估OVCFPKP术后相邻椎体再骨折的风险。
方法：选取2018年1月至2020年12月在我院行PKP治疗OVCF的患者，分为再骨折组和正常组。围手术期影像学数据包括术前骨密度（BMD），椎旁肌肉的脂肪浸润（FI％），脊柱和骨盆矢状参数，收集椎体高度恢复率。通过多因素logistic回归分析得到预测模型。
结果：共纳入242例患者，其中VRF组23例,正常组219例。BMD有统计学差异,FI%,两组椎体高度和矢状不平衡恢复率。连续变量的ROC曲线分析显示，BMD≤-2.80，FI％≥40％和椎体高度恢复率≥10％为临界值。Logistic回归分析显示,BMD≤-2.80、FI%≥40%、矢状面失衡是VRF的独立危险因素。根据预测概率曲线下面积(AUC)为0.85(P<0.05)。简化模型后，模型的总点是7点，截止值为5点。
结论：本研究中获得的预测模型可以早期有效地预测OVCFPKP后的再骨折。它具有良好的预测效果，适用于临床医生。
