基于十二井穴生物电阻抗值构建冠心病患者抗血栓治疗出血风险预测模型  

Construction of a Bleeding Risk Prediction Model for Patients with Coronary Heart Disease Using Antithrombotic Therapy:based on Bioelectrical Impedance of Twelve Jing-well Points

作　　者：高群 张华军[2] 张立晶[1] 王嵘[2] GAO Qun;ZHANG Huajun;ZHANG Lijing;WANG Rong(Dongzhimen Hospital,Beijing University of Chinese Medicine,Beijing,100700;Chinese PLA General Hospital)

机构地区：[1]北京中医药大学东直门医院,北京100700 [2]中国人民解放军总医院

出　　处：《中医杂志》2023年第7期710-715,共6页Journal of Traditional Chinese Medicine

基　　金：国家重点研发计划(2016YFC1301400)。

摘　　要：目的 采用十二井穴生物电阻抗值构建冠心病患者抗血栓治疗出血风险预测模型并筛选模型最佳算法。方法 纳入经抗血栓治疗的冠心病患者1931例,入组时以患者自行报告出血事件作为阳性组,未报告出血事件为阴性组。分别检测两组患者十二井穴的电阻抗值并以此代表十二经脉电阻抗值,采用SHAP分析框架分别以LR、KNN、XGBoost三种机器学习算法进行拟合模型分析,根据SHAP分析结果选择三种算法中表现最优的模型进行下一步分析。分析最终选取的模型中各特征的权重与表现,取所有样本点的SHAP值计算均值并进行排序,以确定模型中权重最高即与出血事件关系最密切的经脉,同时基于同一模型对不同出血事件进行亚组分析。结果 1931例患者中阳性组176例,阴性组1755例。出血事件报告次数前三位分别是牙龈出血72次、皮下出血49次、鼻出血34次。两组患者的肺经、大肠经、胃经、心经、小肠经、肾经、心包经、三焦经、胆经井穴电阻抗值差异均有统计学意义(P<0.05)。三种算法中XGBoost算法表现最优,验证集曲线下面积(AUC)为0.728,测试集AUC为0.709,说明模型外部数据集中适用性较好,故选择该算法模型进行下一步分析。SHAP均值分析结果显示,排名前五位的小肠经(0.161)、大肠经(0.152)、肺经(0.088)、心包经(0.069)、心经(0.068)的电阻抗值差异在出血风险预测模型中的权重较高。对牙龈出血、皮下出血、鼻出血单一出血事件的亚组分析显示,在牙龈出血事件中大肠经SHAP均值(0.080)最高,在鼻出血事件中肺经SHAP均值(0.097)最高,在皮下出血事件中小肠经SHAP均值(0.162)最高。结论基于十二井穴即十二经脉生物电阻抗值可构建冠心病患者抗血栓治疗出血风险预测模型,且预测模型以XGBoost算法表现最优,可提示鼻出血与肺经、牙龈出血与大肠经、皮下出血与小肠经关系密切。Objective To construct a bleeding risk prediction model for patients with coronary heart disease(CHD)using antithrombotic therapy based on bioelectrical impedance of twelve jing-well points,and to choose the best algorithm for the model.Methods A total of 1931 CHD patients accepted antithrombotic therapy were included,and they were divided into the exposure group where bleeding events were reported at the time of enrollment,and the control goup where there is no self-reported bleeding events.The electrical impedance of the twelve jing-well points were detected,which represented the electrical impedance of the twelve channels.The SHAP analysis framework was used to analyze the fit models with three machine learning algorithms,that is LR,KNN,and XGBoost.The model with the best performance among the three algorithms was chosen for further analysis.The weight and performance of each feature in the finally selected model were analyzed,and the average SHAP values of all sample points were calculated and sorted to determine the channels with the highest weight in the model,that were the most closely related ones to the bleeding events;at the same time,subgroup analysis was performed for different bleeding events based on the same model.Results Among the 1931 patients,there were 176 cases in the exposure group and 1755 cases in the control group.The top three bleeding events were gum bleeding(72 times),subcutaneous bleeding(49 times),and epistaxis(34 times).There were statistically significant differences in the electrical impedance of the jing-well points of lung,large intestine,stomach,heart,small intestine,kidney,pericardium,sanjiao(三焦),and gallbladder channels between the two groups(P<0.05).The XGBoost algorithm performed best among the three algorithms;the area under the curve(AUC)of the validation set was 0.728,and the AUC of the test set was 0.709,indicating good applicability of the external data set,and therefore XGBoost algorithm was selected for further analysis.The average SHAP values showed that the diff

关 键 词：冠心病 抗血栓治疗 出血事件 井穴 生物电阻抗 风险预测模型 

分 类 号：R246.1[医药卫生—针灸推拿学]