Abstract
Background: Accurate estimation of coronary heart disease (CHD) risk is requisite for effective primary prevention of the disease. The Framingham Risk Score is the most commonly used method for estimating 10-year risk for CHD in asymptomatic individuals. Further noninvasive tests of atherosclerosis are widely available and may be added to enhance risk estimation. However, the ability to combine different test results explicitly in a quantitative way is limited, and a substantial gap remains in identification ofthose at high risk for future CHD.
Objectives: The aims of this paper are to present information about and examples of how to estimate 10-year risk of developing CHD with the Framingham Risk Score and todemonstrate how to combine two different test results with Bayes' theorem.
Method: Bayes' theorem of conditional probability is presented as a method by which to combine two different test results in a quantitative way to better identify high-risk asymptomatic individuals.
Discussion: Applying Bayes' theorem will help nurses to better estimate CHD risk, leading to optimal intervention plans. This method of refining risk estimation is especially useful for individuals who would fall into an intermediate-risk category based on the Framingham Risk Score.