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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

PURPOSE: As a second-generation drug-eluting stent, the restenosis risk factors of the everolimus-eluting stent (EES) lack sufficient evidence. Therefore, the study investigated the in-stent restenosis occurrence and its predictive factors among patients with coronary heart disease (CHD) who underwe...

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Autores principales: Feng, Qiang, Zhao, Ying, Wang, Haiyan, Zhao, Jiayu, Wang, Xun, Shi, Jianping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403046/
https://www.ncbi.nlm.nih.gov/pubmed/36035940
http://dx.doi.org/10.3389/fcvm.2022.857922
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author Feng, Qiang
Zhao, Ying
Wang, Haiyan
Zhao, Jiayu
Wang, Xun
Shi, Jianping
author_facet Feng, Qiang
Zhao, Ying
Wang, Haiyan
Zhao, Jiayu
Wang, Xun
Shi, Jianping
author_sort Feng, Qiang
collection PubMed
description PURPOSE: As a second-generation drug-eluting stent, the restenosis risk factors of the everolimus-eluting stent (EES) lack sufficient evidence. Therefore, the study investigated the in-stent restenosis occurrence and its predictive factors among patients with coronary heart disease (CHD) who underwent percutaneous coronary intervention (PCI) with EES. MATERIALS AND METHODS: Totally, 235 patients with CHD 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 of 8.5%. Diabetes mellitus, hypercholesteremia, hyperuricemia, fasting blood glucose, serum uric acid (SUA), high-sensitivity C-reactive protein (HsCRP), target lesions in the 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% CI: 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 patients with CHD who underwent post-PCI with EES.
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spelling pubmed-94030462022-08-26 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 Feng, Qiang Zhao, Ying Wang, Haiyan Zhao, Jiayu Wang, Xun Shi, Jianping Front Cardiovasc Med Cardiovascular Medicine PURPOSE: As a second-generation drug-eluting stent, the restenosis risk factors of the everolimus-eluting stent (EES) lack sufficient evidence. Therefore, the study investigated the in-stent restenosis occurrence and its predictive factors among patients with coronary heart disease (CHD) who underwent percutaneous coronary intervention (PCI) with EES. MATERIALS AND METHODS: Totally, 235 patients with CHD 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 of 8.5%. Diabetes mellitus, hypercholesteremia, hyperuricemia, fasting blood glucose, serum uric acid (SUA), high-sensitivity C-reactive protein (HsCRP), target lesions in the 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% CI: 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 patients with CHD who underwent post-PCI with EES. Frontiers Media S.A. 2022-08-11 /pmc/articles/PMC9403046/ /pubmed/36035940 http://dx.doi.org/10.3389/fcvm.2022.857922 Text en Copyright © 2022 Feng, Zhao, Wang, Zhao, Wang and Shi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Feng, Qiang
Zhao, Ying
Wang, Haiyan
Zhao, Jiayu
Wang, Xun
Shi, Jianping
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
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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403046/
https://www.ncbi.nlm.nih.gov/pubmed/36035940
http://dx.doi.org/10.3389/fcvm.2022.857922
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