CACS used to improve classification of risk for prediction of coronary heart disease events

Use of a score based on the amount of calcium in coronary arteries in addition to traditional risk factors improved the classification of risk for prediction of coronary heart disease events, and placed more individuals in the most extreme risk categories, according to a study in the April 28 issue of JAMA.

The coronary artery calcium score (CACS; determined by use of computed tomography by measuring buildup of calcium in plaque on the walls of the arteries of the heart) has been shown in large prospective studies to be associated with the risk of future cardiovascular events. However, the extent to which adding CACS to traditional coronary heart disease (CHD) risk factors improves classification of risk is unclear, according to background information in the article.

Tamar S. Polonsky, M.D., of the Northwestern University Feinberg School of Medicine, Chicago, and colleagues conducted a study to determine whether adding CACS to a prediction model based on traditional risk factors improves classification of risk. CACS was measured by computed tomography (a type of imaging method) in 6,814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), a population-based cohort without known cardiovascular disease.

Recruitment of participants began in July 2000, with follow-up through May 2008. Five-year risk estimates for new CHD were categorized as 0 percent to less than 3 percent, 3 percent to less than 10 percent, and 10 percent or more using hazards models. Model 1 incorporated age, race/ethnicity, sex, tobacco use, antihypertensive medication use, systolic blood pressure and total and high-density lipoprotein cholesterol measurements. Model 2 used these risk factors plus CACS. The researchers calculated the net reclassification improvement using model 2 vs. model 1.

Among a final group of 5,878 individuals, there were 209 CHD events during a median (midpoint) follow-up of 5.8 years, of which 122 were major events (heart attack, death from CHD, or resuscitated cardiac arrest). The researchers found that model 2 resulted in significant improvements in risk prediction compared with model 1. In model 1, 69 percent of the cohort was classified in the highest or lowest risk categories compared with 77 percent in model 2. With the addition of CACS to the model, an additional 23 percent of those who experienced events were reclassified as high risk and an additional 13 percent of those who did not experience events were reclassified as low risk. Among intermediate-risk individuals, 16 percent were reclassified as high risk while 39 percent were classified as low risk.

"The results of this study demonstrate that when CACS is added to traditional risk factors, it results in a significant improvement in the classification of risk for the prediction of CHD events in an asymptomatic population-based sample of men and women drawn from 4 U.S. racial/ethnic groups," the authors write. "Incorporation of an individual's CACS leads to a more refined estimation of future risk of CHD events than traditional risk factors alone."

"The results provide encouragement for moving to the next stage of evaluation to assess the use of CACS on clinical outcomes."