Cargando…
Risk Factors of Coronary Artery Disease in Secondary Prevention—Results from the AtheroGene—Study
BACKGROUND: Risk factors are important in cardiovascular (CV) medicine for risk stratification of patients. We aimed to compare the traditional risk factors to clinical variables for the prediction of secondary cardiovascular events. METHODS AND RESULTS: For this study, 3229 patients with known coro...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4496051/ https://www.ncbi.nlm.nih.gov/pubmed/26154343 http://dx.doi.org/10.1371/journal.pone.0131434 |
_version_ | 1782380336221519872 |
---|---|
author | Zengin, Elvin Bickel, Christoph Schnabel, Renate B. Zeller, Tanja Lackner, Karl-J. Rupprecht, Hans-J. Blankenberg, Stefan Westermann, Dirk |
author_facet | Zengin, Elvin Bickel, Christoph Schnabel, Renate B. Zeller, Tanja Lackner, Karl-J. Rupprecht, Hans-J. Blankenberg, Stefan Westermann, Dirk |
author_sort | Zengin, Elvin |
collection | PubMed |
description | BACKGROUND: Risk factors are important in cardiovascular (CV) medicine for risk stratification of patients. We aimed to compare the traditional risk factors to clinical variables for the prediction of secondary cardiovascular events. METHODS AND RESULTS: For this study, 3229 patients with known coronary artery disease (CAD) were included. We calculated whether the traditional risk factors, diabetes mellitus, increased LDL/HDL ratio, arterial hypertension and smoking alone and in combination with the clinical variables, ejection fraction, creatinine clearance, multi-vessel disease and CRP concentration predict the outcome cardiovascular death or non-fatal myocardial infarction (N = 432) during the mean follow-up time of 4.2 ± 2.0 years. In this cohort diabetes mellitus was the risk factor with the strongest influence regarding occurrence of secondary events (hazard ratio; HR:1.70, confidence interval; CI 95%: 1.36-2.11; P<0.0001), followed by LDL/HDL ratio and smoking. However, risk stratification is further improved by using additional clinical variables like ejection fraction (HR:3.30 CI 95%:2.51-4.33; P>0.0001) or calculated creatinine clearence (Cockroft-Gault formula) (HR:2.26 CI 95%:1.78-2.89; P<0.0001). Further ameliorating risk stratification from the clinical variables were CRP and multi-vessel disease. The most precise risk prediction was achieved when all clinical variables were added to the CV risk factors. CONCLUSION: Diabetes mellitus has the strongest influence to predict secondary cardiovascular events in patients with known CAD. Risk stratification can further be improved by adding CV risk factors and clinical variables together. Control of risk factors is of paramount importance in patients with known CAD, while clinical variables can further enhance prediction of events. |
format | Online Article Text |
id | pubmed-4496051 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44960512015-07-15 Risk Factors of Coronary Artery Disease in Secondary Prevention—Results from the AtheroGene—Study Zengin, Elvin Bickel, Christoph Schnabel, Renate B. Zeller, Tanja Lackner, Karl-J. Rupprecht, Hans-J. Blankenberg, Stefan Westermann, Dirk PLoS One Research Article BACKGROUND: Risk factors are important in cardiovascular (CV) medicine for risk stratification of patients. We aimed to compare the traditional risk factors to clinical variables for the prediction of secondary cardiovascular events. METHODS AND RESULTS: For this study, 3229 patients with known coronary artery disease (CAD) were included. We calculated whether the traditional risk factors, diabetes mellitus, increased LDL/HDL ratio, arterial hypertension and smoking alone and in combination with the clinical variables, ejection fraction, creatinine clearance, multi-vessel disease and CRP concentration predict the outcome cardiovascular death or non-fatal myocardial infarction (N = 432) during the mean follow-up time of 4.2 ± 2.0 years. In this cohort diabetes mellitus was the risk factor with the strongest influence regarding occurrence of secondary events (hazard ratio; HR:1.70, confidence interval; CI 95%: 1.36-2.11; P<0.0001), followed by LDL/HDL ratio and smoking. However, risk stratification is further improved by using additional clinical variables like ejection fraction (HR:3.30 CI 95%:2.51-4.33; P>0.0001) or calculated creatinine clearence (Cockroft-Gault formula) (HR:2.26 CI 95%:1.78-2.89; P<0.0001). Further ameliorating risk stratification from the clinical variables were CRP and multi-vessel disease. The most precise risk prediction was achieved when all clinical variables were added to the CV risk factors. CONCLUSION: Diabetes mellitus has the strongest influence to predict secondary cardiovascular events in patients with known CAD. Risk stratification can further be improved by adding CV risk factors and clinical variables together. Control of risk factors is of paramount importance in patients with known CAD, while clinical variables can further enhance prediction of events. Public Library of Science 2015-07-08 /pmc/articles/PMC4496051/ /pubmed/26154343 http://dx.doi.org/10.1371/journal.pone.0131434 Text en © 2015 Zengin et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Zengin, Elvin Bickel, Christoph Schnabel, Renate B. Zeller, Tanja Lackner, Karl-J. Rupprecht, Hans-J. Blankenberg, Stefan Westermann, Dirk Risk Factors of Coronary Artery Disease in Secondary Prevention—Results from the AtheroGene—Study |
title | Risk Factors of Coronary Artery Disease in Secondary Prevention—Results from the AtheroGene—Study |
title_full | Risk Factors of Coronary Artery Disease in Secondary Prevention—Results from the AtheroGene—Study |
title_fullStr | Risk Factors of Coronary Artery Disease in Secondary Prevention—Results from the AtheroGene—Study |
title_full_unstemmed | Risk Factors of Coronary Artery Disease in Secondary Prevention—Results from the AtheroGene—Study |
title_short | Risk Factors of Coronary Artery Disease in Secondary Prevention—Results from the AtheroGene—Study |
title_sort | risk factors of coronary artery disease in secondary prevention—results from the atherogene—study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4496051/ https://www.ncbi.nlm.nih.gov/pubmed/26154343 http://dx.doi.org/10.1371/journal.pone.0131434 |
work_keys_str_mv | AT zenginelvin riskfactorsofcoronaryarterydiseaseinsecondarypreventionresultsfromtheatherogenestudy AT bickelchristoph riskfactorsofcoronaryarterydiseaseinsecondarypreventionresultsfromtheatherogenestudy AT schnabelrenateb riskfactorsofcoronaryarterydiseaseinsecondarypreventionresultsfromtheatherogenestudy AT zellertanja riskfactorsofcoronaryarterydiseaseinsecondarypreventionresultsfromtheatherogenestudy AT lacknerkarlj riskfactorsofcoronaryarterydiseaseinsecondarypreventionresultsfromtheatherogenestudy AT rupprechthansj riskfactorsofcoronaryarterydiseaseinsecondarypreventionresultsfromtheatherogenestudy AT blankenbergstefan riskfactorsofcoronaryarterydiseaseinsecondarypreventionresultsfromtheatherogenestudy AT westermanndirk riskfactorsofcoronaryarterydiseaseinsecondarypreventionresultsfromtheatherogenestudy AT riskfactorsofcoronaryarterydiseaseinsecondarypreventionresultsfromtheatherogenestudy |