The primary objective of this study was to classify patients with CAD as regards their gluco-metabolic state by easily available clinical variables. A secondary objective was to explore if it was possible to identify CAD patients at a high cardiovascular risk due to metabolic perturbations. The 1,867 patients with CAD were gluco-metabolically classified by an OGTT. Among these, 990 patients had complete data regarding all components of the metabolic syndrome, BMI, HbA1c and medical history. Only FPG and HDL-c adjusting for age significantly impacted OGTT classification. Based on these variables, a neural network reached a cross-validated misclassification rate of 37.8% compared with OGTT. By this criterion, 1,283 patients with complete one-year follow-up concerning all-cause mortality, myocardial infarction and stroke (CVE) were divided into low- and high-risk groups within which CVE were, respectively, 5.1 and 9.4% (p=0.016).Adjusting for confounding variables the relative risk for a CVE based on the neural network was 2.06 (95% CI: 1.18-3.58) compared with 1.37 (95% CI: 0.79-2.36) for OGTT. Conclusions:The neural network, based on FPG, HDL-c and age, showed useful risk stratification capacities; it may, therefore, be of help when stratifying further risk of CVE in CAD patients
A gluco-metabolic risk index with cardiovascular risk stratification potential in patients with coronary artery disease
ANSELMINO, Matteo;
2009-01-01
Abstract
The primary objective of this study was to classify patients with CAD as regards their gluco-metabolic state by easily available clinical variables. A secondary objective was to explore if it was possible to identify CAD patients at a high cardiovascular risk due to metabolic perturbations. The 1,867 patients with CAD were gluco-metabolically classified by an OGTT. Among these, 990 patients had complete data regarding all components of the metabolic syndrome, BMI, HbA1c and medical history. Only FPG and HDL-c adjusting for age significantly impacted OGTT classification. Based on these variables, a neural network reached a cross-validated misclassification rate of 37.8% compared with OGTT. By this criterion, 1,283 patients with complete one-year follow-up concerning all-cause mortality, myocardial infarction and stroke (CVE) were divided into low- and high-risk groups within which CVE were, respectively, 5.1 and 9.4% (p=0.016).Adjusting for confounding variables the relative risk for a CVE based on the neural network was 2.06 (95% CI: 1.18-3.58) compared with 1.37 (95% CI: 0.79-2.36) for OGTT. Conclusions:The neural network, based on FPG, HDL-c and age, showed useful risk stratification capacities; it may, therefore, be of help when stratifying further risk of CVE in CAD patients
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