title: A gluco-metabolic risk index with cardiovascular risk stratification potential in patients with coronary artery disease.
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.
authors.affiliation: Department of Medicine, Karolinska Institute, Solna, Stockholm, Sweden.
authors.forename: Matteo
authors.initials: M
authors.lastname: Anselmino
authors.forename: Klas
authors.initials: K
authors.lastname: Malmberg
authors.forename: Lars
authors.initials: L
authors.lastname: Rydén
authors.forename: John
authors.initials: J
authors.lastname: Ohrvik
mesh_terms: D000367:Age Factors; D000368:Aged; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism*; D008076:Cholesterol, HDL / Q000097:blood; D003324:Coronary Artery Disease / Q000097:blood* / Q000150:complications / Q000401:mortality; D018572:Disease-Free Survival; D005215:Fasting / Q000097:blood; D005260:Female; D005951:Glucose Tolerance Test; D006801:Humans; D053208:Kaplan-Meier Estimate; D016015:Logistic Models; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood* / Q000150:complications / Q000401:mortality; D008875:Middle Aged; D009203:Myocardial Infarction / Q000097:blood / Q000209:etiology* / Q000401:mortality; D016571:Neural Networks, Computer; D011379:Prognosis; D016016:Proportional Hazards Models; D018570:Risk Assessment; D012307:Risk Factors; D020521:Stroke / Q000097:blood / Q000209:etiology* / Q000401:mortality; D013997:Time Factors
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D008076:Cholesterol, HDL