AUTHOR=Feng Qiang , Zhao Ying , Wang Haiyan , Zhao Jiayu , Wang Xun , Shi Jianping 

TITLE=A predictive model involving serum uric acid, C-reactive protein, diabetes, hypercholesteremia, multiple lesions for restenosis risk in everolimus-eluting stent-treated coronary heart disease patients

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.857922

DOI=10.3389/fcvm.2022.857922

ISSN=2297-055X

ABSTRACT=Purpose: As a second-generation drug-eluting stent, the restenosis risk factors of everolimus-eluting stent (EES) lack sufficient evidence. Therefore, the study investigated the in-stent restenosis occurrence and its predictive factors in coronary heart disease (CHD) patients who underwent percutaneous coronary intervention (PCI) with EES.
Methods: Totally, 235 CHD patients who underwent PCI with EES were included. At 1 year post PCI with EES (or earlier if clinically indicated), coronary angiography was performed to evaluate the in-stent restenosis status.
Results: Within 1 year post operation, 20 patients developed in-stent restenosis while 215 patients did not develop in-stent restenosis, resulting in a 1-year in-stent restenosis rate 8.5%. Diabetes mellitus, hypercholesteremia, hyperuricemia, fasting blood-glucose, serum uric acid (SUA), high-sensitivity C-reactive protein (HsCRP), target lesions at left circumflex artery, patients with two target lesions, length of target lesions and length of stent positively correlated with in-stent restenosis risk, while high-density lipoprotein cholesterol negatively associated with in-stent restenosis risk. Notably, diabetes mellitus, hypercholesteremia, SUA, HsCRP levels, and patients with two target lesions were independent predictive factors for in-stent restenosis risk by multivariate logistic regression analysis. Then, the in-stent restenosis risk prediction model was established based on these independent predictive factors, which exhibited an excellent value in predicting in-stent restenosis risk (area under the curve: 0.863; 95% confidence interval: 0.779-0.848) by receiver operating characteristic analysis.
Conclusion: In-stent restenosis risk prediction model, consisting of diabetes mellitus, hypercholesteremia, SUA, HsCRP, and patients with two target lesions, may predict in-stent restenosis risk in CHD patients post PCI with EES.