Coronary age as a risk factor in the modified Framingham risk score

Clinical guidelines emphasize risk assessment as vital to patient selection for medical primary intervention. However, risk assessment methods are restricted in their ability to predict further coronary events. The most widely accepted tool in the United States is the Framingham risk score. In these equations age is a powerful risk factor. Although the extent of coronary atherosclerosis increases with age, there is large inter-individual variability in the rate of development and progression of this disease. This fact limits the utility of Framingham scoring when applied to individuals. Electron beam tomography (EBT), which measures coronary calcium, provides a non-invasive method for assessing coronary plaque burden, thus offering the possibility of providing a more accurate estimate of an individual's "arterial age" than from chronological age alone.

In this paper we discuss a new and simple method for incorporating the coronary calcium score (CCS) to modify the Framingham Risk Assessment (FRA). Using this method, a coronary artery calcium (CAC) age equivalent is generated that replaces chronological age in Framingham scoring.

Using a percentile table of CCS scores by age group and sex, individuals are matched to the age group whose calcium score most closely approximates their own individual score. The original 10-year absolute risk score of a 65-year old man with a CCS of 6 based on chronological age is 10%, whereas the modified absolute risk score based on CAC age equivalents is 2%.

Our approach of replacing chronological age with CAC age equivalents in the Framingham equations possesses simplicity of application combined with precision. Physicians can easily derive adjusted Framingham risk scores and prescribe intervention methods based on patients' ten-year risks. The adjusted ten-year risks are likely to be more accurate than unadjusted risks since they are based on coronary calcium score information. The modified FRA approach not only may increase the predicted risk for some patients, but also may decrease the predicted risk for others, making it a more precise adjustment than other methods.