Proteomics has led to the discovery of several biomarkers within an individual’s bloodstream that can be used in the diagnostic process for disease. Identification of novel biomarkers have a significant impact in the area of public health, with the potential to replace existing diagnostic methods that are complicated, costly, and that pose considerable risk to the patient. Cardiac catheterization, the current diagnostic method for coronary artery disease, is such an invasive procedure. An over-abundance of negative test results leads to the inquiry whether exposing all symptomatic patients to the procedure is in a physician’s best interest.
A statistical analysis involving multivariate logistic regression and evaluation of predictive models identified a panel of biomarkers that can be used to classify patient with coronary artery disease and those with “normal” coronary arteries. This panel was used in conjunction with common clinical risk factors for heart disease to examine the added predictive power of the multi-marker panel when combined with clinical characteristics.
A four-marker panel consisting of OPN, IL1β, Apo-B100, and Fibrinogen were found to be statistically significant predictors of coronary artery disease in a predictive logistic model adjusting for clinical risk factors, diabetes status and smoking status. The ability to identify patients that did not have clinically relevant coronary disease based on currently used clinical risk factors increased greatly, from zero to approximately thirty percent of the patients, with the inclusion of the biomarker panel.
The use of a blood screening test for the diagnosis of coronary artery disease among symptomatic patients can limit the number of unnecessary cardiac catheterizations, reducing healthcare costs and patient risks associated with the invasive nature of the procedure. However, with such a test, there may be some discrimination error present, and the cost of misdiagnosing a patient with clinically relevant coronary artery disease needs to be weighed against the benefits of the test
