椎体成形术后再发椎体骨折风险预测模型的建立

Nomogram to Predict the Occurrence of Recurrent Vertebral Fracture after Vertebroplasty

摘要: 目的：探讨骨质疏松性椎体骨折患者行椎体成形术后1年内再发椎体骨折的影响因素，并建立风险预测模型。方法：将2020年至2022年期间于山东大学齐鲁医院因骨质疏松性椎体骨折接受椎体成形术治疗的患者纳入研究，并进一步将其分为训练队列和验证队列。利用单因素分析和基于Akaike信息准则(AIC)的逐步logistic回归法分析分析椎体成形术后1年内再发骨折的影响因素，建立风险预测模型并以列线图的形式将其可视化。我们通过Brier评分、受试者工作曲线(ROC)、校正曲线、临床决策曲线分析(DCA)和临床影响曲线(CIC)对模型的性能进行了评估和验证。结果：经排除后，共有780名患者入选。年龄、性别、吸烟史、用药情况、佩戴支具情况、日照情况、β胶原降解产物、总I型胶原氨基端延长肽、高密度脂蛋白胆固醇9个因素是骨质疏松性椎体骨折患者行椎体成形术后1年内再发椎体骨折的影响因素；预测模型验证结果显示：C指数分别为训练队列0.851，验证队列为0.820，Brier评分为0.090，证明模型的判别能力及准确度较高。临床决策分析和临床影响曲线结果表明，预测模型可为患者带来临床获益。结论：吸烟、不规范使用抗骨质疏松药物、术后佩戴支具时间不足、每日平均日照时间不足等可增加椎体成形术后1年内再发椎体骨折的风险，该模型可以较准确地预测骨质疏松椎体压缩性骨折患者行椎体成形术后1年内再发椎体骨折风险。

Abstract: Objective: To investigate the influencing factors of recurrent vertebral fracture within one year after vertebroplasty in patients with osteoporotic vertebral fracture, and to establish a predictive model. Methods: Patients who underwent vertebroplasty for osteoporotic vertebral fracture at Qilu Hospital of Shandong University between 2020 and 2022 were enrolled in the study and further divided into a training cohort and a validation cohort. Factors influencing the recurrent fracture within one year after vertebroplasty were analyzed using univariate analysis and stepwise logistic regression based on Akaike’s information criterion (AIC), and a risk prediction model was developed in the form of a column-line graph. We evaluated and validated the performance of the model by Brier scores, receiver operating characteristic (ROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC). Results: After exclusion, a total of 780 patients were enrolled. Nine factors namely, age, gender, smoking history, medication use, brace wear, sun exposure, β-cross, TP1NT, and HDL-C, were the influencing factors for recurrent vertebral fractures in osteoporotic vertebral fracture patients within one year of vertebroplasty. The results of the validation of the predictive model showed that the C-index was 0.851 for the training cohort and 0.820 for the validation cohort, and the Brier score was 0.090, which proved that the model's discriminative ability and accuracy were high. The results of clinical decision analysis and clinical impact curves showed that the predictive model could bring clinical benefits to patients. Conclusion: Smoking, unregulated use of anti-osteoporotic medications, insufficient postoperative support wearing time, and insufficient average daily sunlight exposure can increase the risk of recurrent vertebral fracture within one year after vertebroplasty, and the model can more accurately predict the risk of recurrent vertebral fracture within one year after vertebroplasty in patients with osteoporotic vertebral compression fractures.