已发表论文

空腹残余胆固醇对药物洗脱支架植入后支架内再狭窄的预后作用

 

Authors Luo Y, Cui S, Zhang C, Huang R, Zhao J, Su K, Luo D, Li Y

Received 19 November 2021

Accepted for publication 28 January 2022

Published 18 February 2022 Volume 2022:15 Pages 1733—1742

DOI https://doi.org/10.2147/IJGM.S348148

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Scott Fraser

Objective: In-stent restenosis (ISR) is regarded as a critical limiting factor in stenting for coronary heart disease (CHD). Recent research has shown that fasting residual cholesterol (RC) has been shown to have a substantial impact on coronary heart disease. Unfortunately, there have not been much data to bear out the relationship between RC and ISR. Then, the predictive value of RC for in-stent restenosis in patients with coronary heart disease was analyzed.
Patients and Methods: Aiming to explore the relationship between RC and ISR, we designed a retrospective study of patients with CHD after drug-eluting stent (DES) implantation, combining the data from a public database and selecting the best-fitting model by comparing the optical subset with least absolute shrinkage and selection operator (LASSO) regression.
Results: Analysis of the abovementioned two models showed that the optical subset optimal subset model, which was based on RC, creatine, history of diabetes, smoking, multi-vessel lesions (2 vessels or more lesions), peripheral vascular lesions (PAD), and blood uric acid, had a better fit (AUC = 0.68), and that RC was an independent risk factor for ISR in the abovementioned two models. Notwithstanding its limitation, this study does suggest that RC has good predictive value for ISR.
Conclusion: Remnant cholesterol is an independent risk factor for in-stent restenosis after percutaneous coronary intervention (PCI) and is a reliable predictor of ISR.
Keywords: in-stent restenosis, ISR, percutaneous coronary intervention, PCI, remnant cholesterol, RC, drug-eluting stents, DES, least absolute shrinkage and selection operator, LASSO