The Second Clinical Medical College of Zhejiang, Chinese Medical University, Hangzhou, Zhejiang, China.
Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, China.
Eur J Med Res. 2023 Aug 11;28(1):280. doi: 10.1186/s40001-023-01235-y.
New vertebral compression fractures (NVCFs) are common adverse events in percutaneous kyphoplasty (PKP). The present study aimed to investigate the risk factors for NVCFs in patients after PKP and to construct a nomogram for the prediction of the risk of re-fracture.
We retrospectively analyzed the medical records of patients after PKP surgery between January 2017 and December 2020. Patients were divided into an NVCF group (n = 225) and a control group (n = 94) based on the presence or absence of NVCFs, respectively, at follow-up within 2 years after surgery. Lasso regression was used to screen for risk factors for re-fracture. Based on the results, a Lasso-logistic regression model was developed, and its prediction performance was evaluated using receiver operating characteristic curves, calibration, and decision curve analysis. The model was visualized, and a nomogram was constructed.
A total of eight potential predictors were obtained from Lasso screening. Advanced age, low body mass index, low bone mineral density, lack of anti-osteoporosis treatment, low preoperative vertebral body height, vertebral body height recovery ≥ 2, cement leakage, and shape D (lack of simultaneous contact of bone cement with the upper and lower plates) were included in the logistic regression model.
A nomogram for predicting postoperative NVCF in PKP was developed and validated. This model can be used for rational assessment of the magnitude of the risk of developing NVCFs after PKP, and can help orthopedic surgeons make clinical decisions aimed at reducing the occurrence of NVCFs.
经皮椎体后凸成形术(PKP)后新发椎体压缩性骨折(NVCFs)是常见的不良事件。本研究旨在探讨 PKP 后患者发生 NVCFs 的危险因素,并构建预测再骨折风险的列线图。
我们回顾性分析了 2017 年 1 月至 2020 年 12 月期间接受 PKP 手术的患者的病历。根据术后 2 年内随访时是否存在 NVCFs,将患者分为 NVCF 组(n=225)和对照组(n=94)。使用 Lasso 回归筛选再骨折的危险因素。基于结果,建立了 Lasso-logistic 回归模型,并通过接受者操作特征曲线、校准和决策曲线分析评估其预测性能。模型可视化后,构建了一个列线图。
Lasso 筛选共获得 8 个潜在预测因子。高龄、低体重指数、低骨密度、缺乏抗骨质疏松治疗、术前椎体高度低、椎体高度恢复≥2、骨水泥渗漏和形状 D(骨水泥与上下板不能同时接触)被纳入逻辑回归模型。
开发并验证了预测 PKP 后 NVCF 的列线图。该模型可用于合理评估 PKP 后发生 NVCFs 的风险程度,并有助于骨科医生做出旨在降低 NVCFs 发生风险的临床决策。