Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

作者：Wenle Li, Jiaming Wang, Wencai Liu, Chan Xu, Wanying Li, Kai Zhang, Shibin Su, Rong Li, Zhaohui Hu, Qiang Liu, Ruogu Lu, Chengliang Yin · 发表于：Frontiers in Public Health · 年份：2021 · DOI：10.3389/fpubh.2021.812023 · 被引用次数：53 · 研究领域：Spinal Fractures and Fixation Techniques、Medical Imaging and Analysis、Pelvic and Acetabular Injuries

Background: Bone cement leakage is a common complication of percutaneous vertebroplasty and it could be life-threatening to some extent. The aim of this study was to develop a machine learning model for predicting the risk of cement leakage in patients with osteoporotic vertebral compression fractures undergoing percutaneous vertebroplasty. Furthermore, we developed an online calculator for clinical application. Methods: This was a retrospective study including 385 patients, who had osteoporotic vertebral compression fracture disease and underwent surgery at the Department of Spine Surgery, Liuzhou People's Hospital from June 2016 to June 2018. Combing the patient's clinical characteristics variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision Tree (DT) and Multilayer perceptron (MLP), which could predict the risk of bone cement leakage. We tested the results with ten-fold cross-validation, which calculated the Area Under Curve (AUC) of the six models and selected the model with the highest AUC as the excellent performing model to build the web calculator. Results: The results showed that Injection volume of bone cement, Surgery time and Multiple vertebral fracture were all independent predictors of bone cement leakage by using multivariate logistic regression analysis in the 385 observation subjects. Further...