摘要
[目的]构建基于逻辑回归和XGBoost算法的全膝关节置换术(total knee arthroplasty,TKA)围手术期深静脉血栓形成风险(deep vein thrombosis,DVT)的预测模型。[方法]回顾性分析2017年12月—2021年10月于安徽医科大学附属安徽省立医院骨科接受TKA手术治疗的3711例患者的临床资料,构建逻辑回归和XGBoost算法预测模型,筛选围手术期出现DVT的预测因素,并比较两者的预测效能。[结果]3711例患者中,TKA术后共有889例患者发生DVT,总发病率23.96%。单项因素比较表明,与非DVT组相比,DVT组年龄显著更大(P<0.05),低分子肝素和X因子抑制剂使用的比率显著更高(P<0.05),术后6 h早期抗凝比率显著更低(P<0.05)、术前准备时间显著更短(P<0.05)、术后住院时间显著更长(P<0.05),手术时间显著更长(P<0.05),术前D-二聚体含量显著更高(P<0.05),术前凝血酶原活动度更低(P<0.05)、术后血磷含量更高(P<0.05)、术后尿素氮肌酐比值更高(P<0.05),差异均有统计学意义。逻辑回归表明:术后尿素氮肌酐比值升高(OR=1.576,P<0.05)、术后住院时间长(OR=1.393,P<0.05)、高龄(OR=1.214,P<0.05)、术后血磷升高(OR=1.160,P=0.05)、术前D-二聚体升高(OR=1.058,P=0.05)是发生DVT的危险因素。XGBoost模型显示年龄、术后住院时间、术后D-二聚体水平、血清尿素氮/肌酐比值、使用低分子肝素是重要的特征向量。两种预测模型ROC分析的AUC分别为0.709和0.840。[结论]XGBoost模型对于TKA围手术期DVT事件具有良好的预测能力,患者年龄、术后住院时间、术后D-二聚体含量、血清尿素氮/肌酐比值、使用低分子肝素是潜在的重要预测指标。
[Objective]To establish the prediction models of deep vein thrombosis(DVT)after total knee arthroplasty(TKA)based on the logic regression and the extreme gradient boosting(XGBoost).[Methods]A retrospective study was conducted on 3711 patients who received TKA in the Department of Orthopaedics,Anhui Provincial Hospital from December 2017 to October 2021.The prediction models for DVT after TKA were established based on the factors related the DVT by logical regression and XGBoost algorithm respectively,which were compared in term of prediction efficiency.[Results]Of 3711 patients,889 patients proved DVT after TKA,with a total incidence of 23.96%.In term of univariate comparison,the DVT group proved significantly older(P<0.05),higher ratio of low-grade heparin and X factor inhibitors used(P<0.05),lower early anti-coagulation 6 hours after the operation(P<0.05),shorter preoperative preparation time(P<0.05),longer postoperative hospital stay(P<0.05),longer operation time(P<0.05),higher preoperative blood D-dimer(P<0.05),lower preoperative coagulation activity(P<0.05),higher level of postoperative blood phosphate(P<0.05)and higher postoperative ratio of urea nitrogen to creatinine(P<0.05)than the non-DVT group.As results of logical regression,higher postoperative ratio of urea nitrogen to creatinine(OR=1.576,P<0.05),longer postoperative hospital stay(OR=1.393,P<0.05),older age(OR=1.214,P<0.05),higher postoperative serum phosphorus(OR=1.160,P=0.05),higher preoperative D-dimer(OR=1.058,P=0.05)were risk factors for DVT events.In term of XGBoost model,the age,postoperative hospital stay,postoperative D-dimer,serum urea nitrogen/creatinine ratio,and use of low molecular weight heparin were important feature vectors.The AUC of ROC analysis of the two prediction models were of 0.709 and 0.840 respectively.[Conclusion]XGBoost model has a good ability to predict DVT events in the perioperative period of TKA.The age,postoperative hospital stay,postoperative D-dimer,serum urea nitrogen/creatinine ratio,and low molecular weight heparin used are potential important predictors.
作者
徐泽
张贤祚
张林林
黄威
周伟
朱晨
尹宗生
XU Ze;ZHANG Xianzuo;ZHANG Lin-lin;HUANG Wei;ZHOU Wei;ZHU Chen;YIN Zong-sheng(Department of Orthopaedics,Anhui Provincial Hospital,Hefei 230001,China;Department of Orthopaedics,The First Affiliated Hospital,China University of Science and Technology,Hefei 230001,China;Department of Orthopaedics,The First Affiliated Hospital,Anhui Medical University,Hefei 230022,China)
出处
《中国矫形外科杂志》
CAS
CSCD
北大核心
2022年第23期2123-2128,共6页
Orthopedic Journal of China
基金
安徽省自然科学基金项目(编号:2108085QH319)。
关键词
全膝关节置换术
深静脉血栓
预测模型
极端梯度提升
逻辑回归
total knee replacement
deep vein thrombosis
prediction model
extreme gradient boosting
logistic regression
