基于Logistic回归和XGBoost算法构建急性膝关节周围多发损伤患者围手术期深静脉血栓形成风险的预测模型  

Prediction model of perioperative risk of deep venous thrombosis in patients with acute multiple knee joint injuries based on Logistic regression and XGBoost algorithm

作　　者：李军 刘霞 陈梓锋 冯星龙 王永胜 Li Jun;Liu Xia;Chen Zifeng;Feng Xinglong;Wang Yongsheng(Department of Orthopaedic Trauma,Guangzhou Panyu Central Hospital,Guangzhou 511400,China)

机构地区：[1]广州市番禺区中心医院创伤骨科,511400

出　　处：《国际外科学杂志》2021年第6期371-377,共7页International Journal of Surgery

基　　金：广州市番禺区科工商信局(2018年度)医疗卫生项目重大项目(2018-Z04-04)。

摘　　要：目的基于Logistic回归和XGBoost算法构建急性膝关节周围多发损伤患者围手术期深静脉血栓形成风险的预测模型,并比较预测性能。方法回顾性选取2017年1月—2020年6月于广州市番禺区中心医院创伤骨科治疗的急性膝关节周围多发损伤患者120例,以7∶3的比例随机分为训练集(n=84)和测试集(n=36),采用训练集数据构建Logistic回归和XGBoost算法预测模型,筛选急性膝关节周围多发损伤患者围手术期深静脉血栓形成的预测因素,采用测试集数据评价模型的预测效果。符合正态分布的计量资料以均数±标准差(Mean±SD)表示,组间比较采用独立样本t检验;非正态分布的计量资料以中位数(四分位间距)[M(P_(25),P_(75))]表示,组间比较采用独立样本Mann-Whitney U检验;计数资料组间比较采用χ^(2)检验。结果Logistic回归模型结果表明,年龄、合并高血压、合并冠心病、受伤至手术时间、术后1 d的D-二聚体、合并多发伤是急性膝关节周围多发损伤患者围手术期深静脉血栓形成的预测因素(P<0.05)。XGBoost算法模型重要特征评分结果中排前5位的为合并多发伤35分,受伤至手术时间28分,年龄24分,合并冠心病21分,术后1 d的D-二聚体16分。训练集中,Logistic回归模型的曲线下面积为0.805(95%CI:0.637~0.912),Hosmer and Lemeshow检验的χ^(2)=1.436,P=0.329;XGBoost算法模型的曲线下面积为0.847(95%CI:0.651~0.920),Hosmer and Lemeshow检验的χ^(2)=1.103,P=0.976。结论Logistic回归模型和XGBoost算法模型对急性膝关节周围多发损伤患者围手术期深静脉血栓形成的预测性能相当,且合并多发伤、受伤至手术时间、年龄、合并冠心病、术后1 d的D-二聚体可作为预测因子。Objective Based on Logistic regression and XGBoost algorithm,the prediction model of perioperative risk of deep venous thrombosis in patients with acute multiple knee joint injuries was constructed,and the prediction performance was compared.Methods A total of 120 patients with acute multiple injuries around the knee treated in the Department of Orthopaedic Trauma,Guangzhou Panyu District Central Hospital from January 2017 to June 2020 were retrospectively selected.According to the proportion of 7∶3,the patients were randomly divided into training set(n=84)and test set(n=36).The prediction models of Logistic regression and XGBoost algorithm were constructed by training set data,to screen the predictors of perioperative deep venous thrombosis in patients with acute multiple injury around knee joint,and the prediction effect of the model was evaluated by test set data.The measurement data conforming to the normal distribution were expressed as mean±standard deviation(Mean±SD),and the independent t-test was used for comparison between groups;the measurement data of non-normal distribution were expressed as the median(interquartile range)[M(P25,P75)],the independent sample Mann-Whitney U test was used for comparison between groups;the Chi-square test was used for comparison of enumeration data between groups.Results The results of Logistic regression model showed that age,hypertension,coronary heart disease,time from injury to operation,D-dimer at 1 day after operation and multiple injuries were predictive factors for perioperative deep venous thrombosis in patients with acute multiple injuries around the knee joint.The top five important feature scores of XGBoost algorithm model were combined multiple injuries(35 points),time from injury to operation(28 points),age(24 points),coronary heart disease(21 points)and D-dimer 1 day after operation(16 points).In the training set,the area under the curve of the Logistic regression model was 0.805(95%CI:0.637-0.912),andχ^(2)=1.436,P=0.329 for Hosmer and Lemeshow test.Th

关 键 词：预测 血栓形成 急性膝关节周围多发损伤 LOGISTIC回归 XGBoost算法 

分 类 号：R687.4[医药卫生—骨科学]