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

被引:34
作者
Li, Wenle [1 ,2 ]
Wang, Jiaming [3 ]
Liu, Wencai [4 ]
Xu, Chan [2 ,5 ]
Li, Wanying [2 ]
Zhang, Kai [1 ,2 ]
Su, Shibin [6 ]
Li, Rong [7 ]
Hu, Zhaohui [8 ]
Liu, Qiang [1 ]
Lu, Ruogu [9 ]
Yin, Chengliang [10 ]
机构
[1] Xianyang Cent Hosp, Dept Orthoped, Xianyang, Peoples R China
[2] Xianyang Cent Hosp, Clin Med Res Ctr, Xianyang, Peoples R China
[3] Harbin Med Univ, Affiliated Hosp 1, Dept Orthoped, Harbin, Peoples R China
[4] Nanchang Univ, Affiliated Hosp 1, Dept Orthopaed Surg, Nanchang, Jiangxi, Peoples R China
[5] Xianyang Cent Hosp, Dept Dermatol, Xianyang, Peoples R China
[6] Xiamen Bank, Xiamen, Peoples R China
[7] Shaanxi Univ Tradit Chinese Med, Affiliated Hosp 1, Xianyang, Peoples R China
[8] Liuzhou Peoples Hosp, Dept Spine Surg, Liuzhou, Peoples R China
[9] Univ Southampton, Dept Elect & Comp Sci, Southampton, Hants, England
[10] Macau Univ Sci & Technol, Fac Med, Macau, Peoples R China
关键词
percutaneous vertebroplasty; bone cement leakage; machine learning algorithms; prediction model; web calculator; VERTEBRAL COMPRESSION FRACTURES; FRAMEWORK; NETWORK; RISK;
D O I
10.3389/fpubh.2021.812023
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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. Furthermore, Heatmap revealed the relative proportions of the 15 clinical variables. In bone cement leakage prediction, the AUC of the six ML algorithms ranged from 0.633 to 0.898, while the RF model had an AUC of 0.898 and was used as the best performing ML Web calculator (https://share.streamlit.io/liuwencai0/pvp_leakage/main/pvp_leakage) was developed to estimate the risk of bone cement leakage that each patient undergoing vertebroplasty.Conclusion: It achieved a good prediction for the occurrence of bone cement leakage with our ML model. The Web calculator concluded based on RF model can help orthopedist to make more individual and rational clinical strategies.
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页数:8
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