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A gluco-metabolic risk index with cardiovascular risk stratification potential in patients with coronary artery disease

Department of Medicine, Karolinska Institute, Solna, Stockholm, Sweden

Klas Malmberg

Department of Medicine, Karolinska Institute, Solna, Stockholm, Sweden

Lars Rydén

Department of Medicine, Karolinska Institute, Solna, Stockholm, Sweden

John Öhrvik

Department of Medicine, Karolinska Institute, Solna, Stockholm, Sweden, John.Ohrvik{at}ki.se

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.

Key Words: artificial neural network • classification • coronary artery disease • cross-validation • diabetes mellitus • event-free survival • oral glucose tolerance test • ordinal logistic regression

Diabetes and Vascular Disease Research, Vol. 6, No. 2, 62-70 (2009)
DOI: 10.1177/1479164109336052


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