Combined Non-invasive Scan and Biomarkers to Identify Independent Risk Factors in Patients with Mild Coronary Stenosis

Overview

Journal J Thorac Dis

Publisher AME Publishing Company

Specialty Pulmonary Medicine

Date 2020 Apr 11

PMID 32274085

Authors

Wei Luo

Ru Zhao

Yanqiu Song

Hui Zhao

Weijun Ma

Yanzhe Ma

Hongliang Cong

Affiliations

Soon will be listed here.

Abstract

Background: Independent risk factors for major adverse cardiovascular events (MACEs) in patients with mild coronary stenosis are uncertain. This study aims to investigate predictive biomarkers for MACEs in patients with mild coronary stenosis.

Methods: Totally 381 patients with mild coronary stenosis were included and MACE incidences were recorded through a 24-month follow-up and 91 patients with unfavorable plaques characteristic are detected by CCTA. One unfavorable characteristic was recorded for 1 point and they were divided into three groups: high-risk group (HR, score =0), intermediate-risk group (IR, score =1) and low-risk group (LR, score/2). Specific blood biomarker measurements of high-sensitivity C-reactive protein (hs-CRP), matrix metallopeptidase 9 (MMP-9), and myeloperoxidase (MPO) were taken simultaneously.

Results: The mean age, hs-CRP and MPO levels in the HR and IR group were significantly higher than that in the LR group. A considerably higher level of MMP-9 showed in the HR group compared to the LR group. The incidence rates of MACE were remarkably higher in HR group than LR group and IR group. Kaplan-Meier survival analysis demonstrated that the cumulative event-free survival rate of HR was significantly higher than that in LR and IR group and there was no significant difference between LR and IR group. The univariate COX regression analysis indicated that the age, hs-CRP, MPO, and unfavorable plaque scores ≥2 were independent risk factors for MACEs.

Conclusions: High MPO levels were strongly correlated with MACEs in patients with mild coronary stenosis. Although confirmation is needed from larger trials, MPO could be a promising clinical tool to improve the risk stratification in patients with mild coronary stenosis and suggest strategies for the individualized prevention programs.

References

Nabel E, Braunwald E . A tale of coronary artery disease and myocardial infarction. N Engl J Med. 2012; 366(1):54-63. DOI: 10.1056/NEJMra1112570. View

Nadjiri J, Hausleiter J, Jahnichen C, Will A, Hendrich E, Martinoff S . Incremental prognostic value of quantitative plaque assessment in coronary CT angiography during 5 years of follow up. J Cardiovasc Comput Tomogr. 2016; 10(2):97-104. DOI: 10.1016/j.jcct.2016.01.007. View

Roger V . Epidemiology of myocardial infarction. Med Clin North Am. 2007; 91(4):537-52. PMC: 2537993. DOI: 10.1016/j.mcna.2007.03.007. View

Baldus S, Heeschen C, Meinertz T, Zeiher A, Eiserich J, Munzel T . Myeloperoxidase serum levels predict risk in patients with acute coronary syndromes. Circulation. 2003; 108(12):1440-5. DOI: 10.1161/01.CIR.0000090690.67322.51. View

Pijls N, Sels J . Functional measurement of coronary stenosis. J Am Coll Cardiol. 2012; 59(12):1045-57. DOI: 10.1016/j.jacc.2011.09.077. View

Takata K, Imaizumi S, Zhang B, Miura S, Saku K . Stabilization of high-risk plaques. Cardiovasc Diagn Ther. 2016; 6(4):304-21. PMC: 4960066. DOI: 10.21037/cdt.2015.10.03. View

Puchner S, Liu T, Mayrhofer T, Truong Q, Lee H, Fleg J . High-risk plaque detected on coronary CT angiography predicts acute coronary syndromes independent of significant stenosis in acute chest pain: results from the ROMICAT-II trial. J Am Coll Cardiol. 2014; 64(7):684-92. PMC: 4135448. DOI: 10.1016/j.jacc.2014.05.039. View

Nichols M, Townsend N, Scarborough P, Rayner M . Cardiovascular disease in Europe 2014: epidemiological update. Eur Heart J. 2014; 35(42):2950-9. DOI: 10.1093/eurheartj/ehu299. View

Motoyama S, Ito H, Sarai M, Kondo T, Kawai H, Nagahara Y . Plaque Characterization by Coronary Computed Tomography Angiography and the Likelihood of Acute Coronary Events in Mid-Term Follow-Up. J Am Coll Cardiol. 2015; 66(4):337-46. DOI: 10.1016/j.jacc.2015.05.069. View

10.

Fong S, Few L, See Too W, Khoo B, Ibrahim N, Yahaya S . Systemic and coronary levels of CRP, MPO, sCD40L and PlGF in patients with coronary artery disease. BMC Res Notes. 2015; 8:679. PMC: 4650203. DOI: 10.1186/s13104-015-1677-8. View

11.

Otsuka K, Fukuda S, Tanaka A, Nakanishi K, Taguchi H, Yoshikawa J . Napkin-ring sign on coronary CT angiography for the prediction of acute coronary syndrome. JACC Cardiovasc Imaging. 2013; 6(4):448-57. DOI: 10.1016/j.jcmg.2012.09.016. View

12.

Min J, Leipsic J, Pencina M, Berman D, Koo B, Van Mieghem C . Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA. 2012; 308(12):1237-45. PMC: 4281479. DOI: 10.1001/2012.jama.11274. View

13.

Peng C, Xia Y, Gao Z, Xu P, Wang X, Xian Z . Hemodynamics analysis of the serial stenotic coronary arteries. Biomed Eng Online. 2017; 16(1):127. PMC: 5679505. DOI: 10.1186/s12938-017-0413-0. View

14.

Liu T, Maurovich-Horvat P, Mayrhofer T, Puchner S, Lu M, Ghemigian K . Quantitative coronary plaque analysis predicts high-risk plaque morphology on coronary computed tomography angiography: results from the ROMICAT II trial. Int J Cardiovasc Imaging. 2017; 34(2):311-319. DOI: 10.1007/s10554-017-1228-6. View

15.

Lee J, Choi G, Koo B, Hwang D, Park J, Zhang J . Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics. JACC Cardiovasc Imaging. 2018; 12(6):1032-1043. DOI: 10.1016/j.jcmg.2018.01.023. View

16.

Mozaffarian D, Benjamin E, Go A, Arnett D, Blaha M, Cushman M . Executive Summary: Heart Disease and Stroke Statistics--2016 Update: A Report From the American Heart Association. Circulation. 2016; 133(4):447-54. DOI: 10.1161/CIR.0000000000000366. View

17.

Symons R, Choi Y, Cork T, Ahlman M, Mallek M, Bluemke D . Optimized energy of spectral coronary CT angiography for coronary plaque detection and quantification. J Cardiovasc Comput Tomogr. 2018; 12(2):108-114. DOI: 10.1016/j.jcct.2018.01.006. View

18.

Dey D, Gaur S, Ovrehus K, Slomka P, Betancur J, Goeller M . Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol. 2018; 28(6):2655-2664. PMC: 5940537. DOI: 10.1007/s00330-017-5223-z. View

19.

Chen W, Gao R, Liu L, Zhu M, Wang W, Wang Y . China cardiovascular diseases report 2015: a summary. J Geriatr Cardiol. 2017; 14(1):1-10. PMC: 5329726. DOI: 10.11909/j.issn.1671-5411.2017.01.012. View

20.

Ozaki Y, Okumura M, Ismail T, Motoyama S, Naruse H, Hattori K . Coronary CT angiographic characteristics of culprit lesions in acute coronary syndromes not related to plaque rupture as defined by optical coherence tomography and angioscopy. Eur Heart J. 2011; 32(22):2814-23. DOI: 10.1093/eurheartj/ehr189. View