基于LASSO回归对冠心病相关血脂指标的筛选

目的:利用LASSO回归分析筛选出与冠心病密切相关的血脂指标。方法:选取2013年5月至2015年11月在济宁医学院附属医院心内科住院并诊断为冠心病的患者3 062例的临床资料进行回顾性分析。按照冠状动脉造影结果分为冠心病组( n＝2 427)和对照组( n＝635)。统计分析用R语言。建立冠心...

Screening of lipid parameters in coronary artery disease based on LASSO regression

Objective Using lasso regression analysis to screen out the blood lipid indexes closely related to coronary heart disease Methods The clinical data of 3062 patients with coronary heart disease who were hospitalized in the Department of Cardiology,Affiliated Hospital of Jining Medical College from May 2013 to November 2015 were retrospectively analyzed.They were divided into control group(n=2427)and coronary angiography group(n=635).R language was used for statistical analysis.Multiple logistic regression models were established for indicators of blood lipid related to CAD,and their multicollinearity severity was assessed.LASSO regression was used to screen out the representative lipid parameters in the CAD prediction model.Results A total of 3062 patients were enrolled,including 2427 patients in coronary heart disease group and 635 patients in control group.The inclusion of lipid parameters into multiple logistic regression model leads to serious multicollinearity.Stepwise regression can only partially reduce multicollinearity severity,while LASSO regression model significantly reduces multicollinearity severity.Low density lipoprotein cholesterol(LDL-C),high density lipoprotein cholesterol(HDL-C)and non-high density lipoprotein cholesterol(non-HDL-C)were found to be the representative lipid indexes for predicting coronary heart disease by LASSO regression analysis.Conclusion LASSO regression has advantages in processing multicollinearity data.LASSO regression showed that LDL-C,HDL-C and non-HDL-C were representative lipid indicators for predicting coronary heart disease..