Screening of lipid parameters in coronary artery disease based on LASSO regression
10.3760/cma.j.cn101721-20200807-00060
- VernacularTitle:基于LASSO回归对冠心病相关血脂指标的筛选
- Author:
Shaohui ZHANG
;
Qiang SU
;
Yongliang ZHAO
;
Jun ZHUO
;
Lixin LIU
;
Guoliang YANG
;
Xueying CHEN
;
Wen DAI
- From:
Clinical Medicine of China
2021;37(2):148-153
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Using lasso regression analysis to screen out the blood lipid indexes closely related to coronary heart diseaseMethods:The clinical data of 3 062 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=2 427) 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 3 062 patients were enrolled, including 2 427 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..
