Construction and validation of a prediction model for coronary artery stenosis based on LASSO regression
10.12007/j.issn.0258-4646.2025.02.008
- VernacularTitle:基于LASSO回归的塔城地区人群冠状动脉狭窄程度预测模型构建及验证
- Author:
Yikang XU
1
;
Lei LIU
;
Limin LIU
;
Jingru MA
;
Jiayu WANG
;
Jun MA
;
Ziyi ZHEN
Author Information
1. 沈阳医学院附属第二医院心内科,沈阳 110001
- Publication Type:Journal Article
- Keywords:
degree of coronary artery stenosis;
nomogram;
prediction model;
LASSO regression
- From:
Journal of China Medical University
2025;54(2):139-143,149
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the risk factors for moderate-to-severe coronary artery stenosis in the population of Tacheng,Xinjiang Uygur Autonomous Region,and to construct and verify a nomogram prediction model for the degree of coronary artery ste-nosis.Methods We retrospectively selected 629 patients who were hospitalized in the Cardiovascular Department of Tacheng Peo-ple's Hospital from January 2021 to June 2023.Using R language software,the sociodemographic data,disease-related data,and va-rious laboratory indicators of the 629 patients were included in the initial screening of risk factors for use in the LASSO regression analysis using a random number table method.The 629 patients were divided into a training group(n=440)and a validation group(n=189)in a 7:3 ratio.Data from the training group were used for model construction,with the degree of coronary artery stenosis as the dependent variable,and the variables selected by LASSO regression as independent variables in the logistic regression model.The validation group was used for model validation.Based on the results of the logistic regression analysis,a visual nomogram for predicting the degree of co-ronary artery stenosis was constructed using R language software.The discriminability,calibration,and clinical utility of the model were evaluated using the area under the receiver operating characteristic curve(AUC),a calibration curve,and decision curve analysis(DCA).Results Age,non-Han ethnicity,hypertension,hyperlipidemia,and a history of cerebrovascular disease were risk factors for mode-rate-to-severe coronary artery stenosis and were included in the risk prediction model.The AUC of the training group and the validation group were 0.905(95％CI:0.790-0.863)and 0.864(95％CI:0.744-0.861),respectively.The predicted values of the calibration curve were consistent with the actual values(Brier scores of the training and validation group:0.03 and 0.14,respectively).The predictive per-formance of the model was good,and the DC A results indicated that the model had net clinical benefits.Conclusion The risk prediction model for coronary artery stenosis in the population of the Tacheng area constructed in this study has good predictive performance and can provide a simple,feasible,economical,and easy-to-promote evaluation tool for medical personnel to screen patients with moderate-to-se-vere coronary artery stenosis.
