Risk Factors of Coronary Artery Disease in Secondary Prevention--Results from the AtheroGene--Study
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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; AtheroGene Investigators.
in: PLOS ONE, Jahrgang 10, Nr. 7, 2015, S. e0131434.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Risk Factors of Coronary Artery Disease in Secondary Prevention--Results from the AtheroGene--Study
AU - Zengin, Elvin
AU - Bickel, Christoph
AU - Schnabel, Renate B
AU - Zeller, Tanja
AU - Lackner, Karl-J
AU - Rupprecht, Hans-J
AU - Blankenberg, Stefan
AU - Westermann, Dirk
AU - AtheroGene Investigators
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Cohort Studies
KW - Coronary Artery Disease/epidemiology
KW - Female
KW - Follow-Up Studies
KW - Humans
KW - Male
KW - Middle Aged
KW - Proportional Hazards Models
KW - Regression Analysis
KW - Risk Factors
KW - Secondary Prevention
U2 - 10.1371/journal.pone.0131434
DO - 10.1371/journal.pone.0131434
M3 - SCORING: Journal article
C2 - 26154343
VL - 10
SP - e0131434
JO - PLOS ONE
JF - PLOS ONE
SN - 1932-6203
IS - 7
ER -
