AUTHOR=Xi Hongfei , Liu Jiasi , Xu Tao , Li Zhe , Mou Xuanting , Jin Yu , Xia Shudong 

TITLE=Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2023.1117915

DOI=10.3389/fcvm.2023.1117915

ISSN=2297-055X

ABSTRACT=Objective: To analyze the risk factors of in-stent restenosis (ISR) after first implantation of drug-eluting stent (DES) patients with coronary heart disease (CHD) and to establish a nomogram model to predict the risk of ISR.
Methods: This study retrospectively analyzed the clinical data of patients with CHD who underwent DES treatment for the first time in the Fourth Affiliated Hospital of Zhejiang University School of Medicine from January 2016 to June 2021. The patients were divided into an ISR group and a non-ISR (N-ISR) group according to the results of coronary angiography. The least absolute shrinkage and selection operator (LASSO) regression analysis was performed on the clinical variables to screen out the characteristic variables. Then we constructed the nomogram prediction model using univariate and conditional multivariate logistic regression analysis combined with the clinical variables selected in the LASSO regression analysis. Finally, the decision curve analysis, clinical impact curve, area under the receiver operating characteristic curve, and calibration curve were used to evaluate the clinical applicability, validity, discrimination, and consistency of the nomogram prediction model. And the ten-fold cross-validation and bootstrap validation were used for double validation of the prediction model.
Results: In this study, hypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen were all predictive factors for ISR. We successfully constructed a nomogram prediction model using these variables to quantify the risk of ISR. The AUC value of the nomogram prediction model was 0.806 (95%CI: 0.739-0.873), indicating that the model had a good discrimination ability for ISR. The high quality of the calibration plot of the model demonstrated the strong consistency of the model. And DCA curve and CIC curve also showed that the model had high clinical applicability and effectiveness.
Conclusions: Hypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen are important predictors of ISR. The nomogram prediction model can better identify the high-risk population of ISR and provide effective decision-making information for the follow-up intervention of the high-risk population.