The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China.
The First People's Hospital of Chenzhou, Chenzhou, 423000, People's Republic of China.
BMC Surg. 2023 Mar 23;23(1):63. doi: 10.1186/s12893-023-01959-y.
In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery.
The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort.
The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116.
In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.
老年人胸腰椎骨质疏松性压缩性骨折（OVCFs）较为常见，骨折后常采用经皮椎体成形术（PVP）治疗。本研究采用机器学习（ML）方法协助临床医生预防 PVP 手术中骨水泥渗漏。
选取郴州市第一人民医院行单节段 PVP 治疗的 374 例胸腰椎 OVCF 患者的临床资料，其中骨水泥渗漏 150 例，无渗漏 224 例。采用 4 种 ML 方法筛选特征变量，并取交集生成预测模型，然后在验证队列中应用该预测模型。
采用 ML 方法筛选出 5 个因素建立列线图诊断模型，该模型的 AUC 为 0.646667，C 值为 0.647。校准曲线显示列线图预测值与实际概率之间存在一致性关系。在 91 个随机样本中，该列线图模型的 AUC 为 0.7555116。
本研究建立了单节段 PVP 手术骨水泥渗漏的预测模型，有助于医生降低手术风险，更好地实施手术。
