基于XGBoost模型预测下肢骨折患者术后深静脉血栓形成风险的临床研究  

Clinical study of XGBoost model-based prediction of postoperative deep vein thrombosis risk in patients with lower extremity fractures

作　　者：李陶陶 张雨露 马丽 宋田田 储雨晴 李莉 LI Tao-tao;ZHANG Yu-lu;MA Li;SONG Tian-tian;CHU Yu-qing;LI Li(Department of Orthopedics,Anhui Medical University Lu'an Hospital,Lu'an,Anhui,237000,China)

机构地区：[1]安徽医科大学附属六安医院骨科,六安237000

出　　处：《中国骨与关节杂志》2025年第4期337-342,共6页Chinese Journal of Bone and Joint

摘　　要：目的基于机器学习(machine learning,ML)模型和Shapley加性解释(Shapley additive explanations,SHAP)构建预测下肢骨折患者术后深静脉血栓形成(deep vein thrombosis,DVT)风险模型。方法选取我院2022年2月至2023年12月手术治疗的202例下肢骨折患者。通过Boruta算法筛选术后DVT重要特征变量。202例患者以3∶2比例分为训练集(n=121)和测试集(n=81)来构建和训练9种ML模型。使用接收者操作曲线(receiver operating features curve,ROC)评估9种ML模型预测性能。通过SHAP值附加解释ML模型并构建预测下肢骨折患者术后DVT风险模型。结果202例下肢骨折患者术后DVT发病率为20.3%。Boruta算法筛选出空腹血糖(fasting blood glucose,FBG)、红细胞(red blood cells,RBC)、血红蛋白(hemoglobin,Hb)、D-二聚体(D-dimer,D-D)、纤维蛋白原(fibrinogen,FIB)和吸烟是术后DVT重要特征变量。9种ML算法中,训练集和测试集的ROC证实极限梯度提升(extreme gradient boosting,XGBoost)模型预测术后DVT风险性能最高。基于SHAP值附加解释和可视化的XGBoost模型能以极高准确度预测DVT风险并生成在线应用程序。结论基于SHAP值解释的XGBoost模型能精准预测下肢骨折患者术后DVT风险。基于此开发的在线应用程序能够实时计算患者术后DVT风险。Objective A model for predicting the risk of postoperative deep vein thrombosis(DVT)in patients with lower extremity fractures was constructed based on machine learning(ML)models and Shapley’s additive interpretation(SHAP).Methods A total of 202 patients with lower extremity fractures who were surgically treated between February 2022 and December 2023 in our hospital were selected.Important characteristic variables of postoperative DVT were screened by Boruta’s algorithm.202 patients were divided into a training set(n=121)and a test set(n=81)in a 3:2 ratio to construct and train 9 ML models.The predictive performance of the 9 ML models was assessed using receiver operating curves(ROC).The ML models were additionally interpreted by SHAP values and constructed to predict the risk of postoperative DVT in patients with lower extremity fractures.Results The prevalence of postoperative DVT in 202 patients with lower limb fractures was 20.3%.Boruta algorithm screened for fasting blood glucose(FBG),red blood cells(RBC),haemoglobin(Hb),D-dimer(D-D),fibrinogen(FIB),and smoking were important characterizing variables for postoperative DVT.Among the nine ML algorithms,the ROC of the training and test sets confirmed that the extreme Gradient Boosting(XGBoost)model had the highest performance in predicting the risk of postoperative DVT.The XGBoost model based on additional interpretation and visualization of SHAP values predicted DVT risk with very high accuracy and generated an online application.Conclusions An XGBoost model based on the interpretation of SHAP values accurately predicts postoperative DVT risk in patients with lower limb fractures.An online application is developed to conveniently calculate patients’postoperativeDVT risk.

关 键 词：骨折 下肢 静脉血栓形成 机器学习 

分 类 号：R683.4[医药卫生—骨科学]