1
|
Feng Q, Zhao Y, Wang H, Zhao J, Wang X, Shi J. 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. Front Cardiovasc Med 2022; 9:857922. [PMID: 36035940 PMCID: PMC9403046 DOI: 10.3389/fcvm.2022.857922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeAs 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 methodsTotally, 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.ResultsWithin 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.ConclusionIn-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.
Collapse
Affiliation(s)
- Qiang Feng
- Department of Cardiology, Handan Central Hospital, Handan, China
- *Correspondence: Qiang Feng,
| | - Ying Zhao
- Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Haiyan Wang
- Department of Cardiology, Handan Central Hospital, Handan, China
| | - Jiayu Zhao
- Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xun Wang
- Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jianping Shi
- Department of Cardiology, Handan Central Hospital, Handan, China
| |
Collapse
|