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Jang-Whan Bae 1 Article
Biomarker for the Prediction of Major Adverse Cardiac Events in Patients with Non-ST-Segment Elevation Myocardial Infarction
Ho Sun Shon, Jang-Whan Bae, Kyoung Ok Kim, Eun Jong Cha, Kyung Ah Kim
Osong Public Health Res Perspect. 2017;8(4):237-246.   Published online August 31, 2017
DOI: https://doi.org/10.24171/j.phrp.2017.8.4.02
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  • 4 Citations
AbstractAbstract PDF

N-terminal pro-brain natriuretic peptide (NT-proBNP) is a well-known biomarker for the diagnosis and prognosis of heart failure, and is directly associated with myocardial dysfunction. We evaluated the prognostic value of NT-proBNP for major adverse cardiac events (MACEs) among patients with non-ST-segment elevation myocardial infarction (NSTEMI) from the Korea Acute Myocardial Infarction Registry during their mid-term follow-up period. In this paper, we analyzed NT-proBNP according to various MACE and level of NT-proBNP. We used multivariate logistic regression to determine the risk factors according to MACE type and NT-proBNP levels, and to identify the cutoff value for each MACE by using the receiver operating characteristic (ROC) curve. NT-proBNP was a significant variable among cardiac deaths (p = 0.016), myocardial infarction (p = 0.000), and coronary artery bypass grafting (CABG) (p = 0.000) in patients with MACE compared with those without MACE. Two-vessel coronary artery disease (CAD) (p = 0.037) and the maximum creatinine kinase (max-CK) (p = 0.031) produced significant results in repeat percutaneous coronary intervention. The area under the ROC curve was found to be statistically significant for cardiac death and CABG. NT-proBNP is a useful predictor for 12-month MACEs among patients with NSTEMI and in those with heart failure. We propose that a new index incorporating NT-proBNP, max-CK, and CAD vessel will be useful as a prognostic indicator of MACEs in the future.

Citations

Citations to this article as recorded by  
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  • Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework
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    Journal of Biomedical Informatics.2018; 88: 70.     CrossRef

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