Mathematical Modeling of Coronary Artery Disease (CAD): Analysis Reveals HbA1c and Total Cholesterol to be Significant Risk Predictors

Abstract

The increasing prevalence of CAD (Coronary Artery Disease) calls for early detection of risk factors and effective clinical management. The predictive potential of commonly estimated clinical variables on CAD incidence was assessed using mathematical modeling and analysis. A random sample of 50 patients with CAD and a control group of 50 subjects without CAD were drawn from a cardiac specialty hospital in Chennai, India during 2011-2012 (mean age = 50.2 years, SD = 11.2 years). Medical data included age, gender, height, weight, body mass index, presence/absence of hypertension, systolic blood pressure, diastolic blood pressure, presence/absence of diabetes mellitus, fasting blood sugar, post-prandial blood sugar, HbA1c, total cholesterol, family history of CAD. Mathematical modeling using discriminant analysis was performed to understand significant contributors leading to CAD. The discriminant analysis resulted in a mathematical model using parameters, HbA1c and cholesterol. The model was found to be statistically significant and this was demonstrated by computing the F value. HbA1c and total cholesterol were found to be significant in predicting the occurrence of CAD.