How Helpful Are Coronary Artery Calcium Scores?
Abstract & Commentary
By Harold L. Karpman, MD, FACC, FACP, Clinical Professor of Medicine, UCLA School of Medicine. Dr. Karpman reports no financial relationship to this field of study.
Synopsis: The addition of CACS to a prediction model based upon the traditional risk factors significantly improved the classification of risk and helped to place more individuals in their appropriate risk categories.
Source: Polonsky TS, et al. Coronary artery calcium score and risk classification for coronary heart disease prediction. JAMA 2010;303:1610-1616.
Large prospective studies have demonstrated that coronary artery calcium scores (CACS) measured by computed tomography are of significant value in risk prediction of future cardiovascular events.1-4 In fact, a recent publication evaluated a cohort of individuals without known cardiovascular disease (CVD) and determined that a CACS > 300 was associated with a hazard ratio for future CVD events of nearly 10-fold,4 and including CACS in a prediction model based on traditional risk factors significantly improved the overall ability to predict future CVD events.
Polonsky and his colleagues evaluated the extent to which adding CACS to a model based on traditional risk factors correctly reclassified participants in the Multi-Ethnic Study of Atherosclerosis (MESA)4 in terms of risk of future CVD events. This population-based cohort of individuals without known CVD totaled 6814 participants who were classified into two models. Model 1 analyzed the standard risk factors including age, sex, tobacco use, systolic blood pressure, antihypertensive medication use, total and high-density lipoprotein cholesterol measurements, and race/ethnicity, whereas model 2 used these standard risk factors plus CACS. The CACS permitted the investigators to reclassify an additional 23% of those subjects who subsequently experienced cardiovascular events to the high-risk category and an additional 13% without events to the low-risk category based upon their history and their CACS. Therefore, the addition of CACS to the prediction model based on traditional risk factors significantly improved the classification of risk and helped in placing more individuals in their correct risk categories.
Commentary
The results of the Polonsky study clearly demonstrated that a significant improvement in the classification of risk for the prediction of CVD events in an asymptomatic population sample of men and women drawn from multiethnic groups occurred when CACS were added to the standard risk profile. The benefit appears to be substantially higher in the intermediate Framingham risk group; therefore, adding CACS to the standard risk factor profile for asymptomatic individuals at intermediate Framingham risk appears to be quite valuable.5 Because some concern has been raised about the safety and cost associated with the widespread use of CACS, some cardiologists have suggested that a CACS-guided strategy may actually cost more money and prevent fewer events than would occur by simply aggressively treating all patients at intermediate risk.6 In addition, only 4 of more than 3000 low-risk individuals were reclassified to high-risk, suggesting that CACS may not be an efficient screening tool among low-risk individuals. However, the individuals who were reclassified from high risk to low risk experienced an event rate that was higher than predicted by the model using the CACS and, although the absolute number of events was small, the data support the recommendation that patients who are at high risk should be treated vigorously regardless of their CACS; therefore, they probably should be placed in a category of patients who need not undergo CACS testing for additional risk assessment. Finally, it should be recognized that besides improving cardiovascular risk assessment, the CACS plays an important role in the appropriate lipid management of patients in certain clinical subgroups in a cost-effective manner.7 For example, in patients with the metabolic syndrome who do not qualify for statin therapy based strictly upon NCEP ATP III recommendations, application of CACS screening could significantly improve the approach to lipid-lowering therapy, thereby reducing the frequency of subsequent CVD events.
In summary, the use of CACS plus additional risk factors significantly enhances the ability to classify a multiethnic cohort of asymptomatic persons without known CVD into clinically accepted categories of risks for future CVD events. However, it is important to recognize that the overall value of CACS appears to be greater in the intermediate-risk patient group than it is in the low-risk and high-risk patient populations. Since the medical risk of CACS is so minimal, the results of this study provides encouragement for moving to the next stage of its evaluation, which obviously is in determining its value in predicting clinical outcomes.
References
1. Raggi P, et al. Coronary artery calcium to predict all-cause mortality in elderly men and women. J Am Coll Cardiol 2008;52:17-23.
2. Budoff MJ, et al. Long-term prognosis associated with coronary calcification: Observation from a registry of 25,253 patients. J Am Coll Cardiol 2007;49:1860-1870.
3. Greenland P, et al. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA 2004;291:210-215.
4. Detrano R, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med 2008;358:1336-1345.
5. Greenland P, et al. ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain. J Am Coll Cardiol 2007;49:378-402.
6. Diamond GA, et al. The things to come of SHAPE: Cost and effectiveness of cardiovascular prevention. Am J Cardiol 2007;99:1013-1015.
7. Ibebuogu UN, et al. Measures of coronary artery calcification and association with the metabolic syndrome and diabetes. J Cardiometab Syndr 2009;4:6-11.
The addition of CACS to a prediction model based upon the traditional risk factors significantly improved the classification of risk and helped to place more individuals in their appropriate risk categories.You have reached your article limit for the month. Subscribe now to access this article plus other member-only content.
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