The Agatston Coronary Artery Calcium Score in Statin Users: Recent Insights from the CAC Consortium and Pathways Forward

Jan 19, 2021   |  Rishi Trivedi, MD; Michael J Blaha, MD, MPH; Roger S. Blumenthal, MD, FACC; Miguel Cainzos-Achirica, MD

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Quick Takes

  • We discuss recent findings from a study by Osei et al. of 28,000 participants from the CAC Consortium.
  • In statins users, both the CAC Agatston score and CAC volume had prognostic utility for CHD and CVD risk, suggesting that CAC burden also predicts CHD and CVD risk in this group.
  • The association between CAC and outcomes in statin users was significantly attenuated compared to those in nonusers, however, the improvement in predictive value compared to risk factor models alone was similar in both groups.
  • Moving forward, we discuss approaches to further enhance preventive therapy allocation in statin users undergoing CAC testing.

Background

The coronary artery calcium (CAC) burden as reported with the Agatston score1 is strongly associated with adverse cardiovascular disease (CVD) events.2-6 CAC scoring using the Agatston method is an established guideline-endorsed adjunct to traditional risk factors for enhanced risk assessment to guide management in asymptomatic individuals.7-9

Most evidence supporting the use of CAC in risk prediction comes from cohorts with very low rates of statin use and has mostly been used to inform guideline recommendations on personalized statin therapy allocation.8,9 Statin therapy modestly accelerates calcification of plaques leading to more stable, lower-risk compositions and sometimes an acceleration of Agatston CAC score progression.10-16 The prognostic utility of CAC in statin users is not well characterized.

Osei et al. aimed at assessing the prognostic utility of CAC for predicting risk of coronary heart disease (CHD) and CVD events comparing non-users and users of statins at the time of the CAC scan.17

Study Design

Retrospective cohort analysis using data from the CAC Consortium focused on subjects with information on baseline statin use (n=28,025), with a mean follow-up of 11 years. Components of the score including volume, area, and density were quantified. 

Cox regression models were used to evaluate the associations between CAC scores with incident cardiac events controlling for traditional risk factors. AUC analyses were performed to assess whether CAC, volume, and density improved prediction beyond traditional risk factors. All analyses were stratified by baseline statin use.

Results

The statin user group (22%) was older with increased classic CVD risk factor burden. Statin users had higher baseline CAC scores (281 vs. 107) with larger mean CAC area and density. The associations between CAC burden and outcomes are summarized in Table 1. There was a statistically significant interaction by baseline statin use, resulting in weaker associations between Agatston CAC scores and both outcomes.

Table 1

  Not on statin therapy On statin therapy
HR (95% CI) Mortality Rate per 1000 person-years (95% CI) HR (95% CI) Mortality Rate per 1000 person-years (95% CI)
CHD Mortality CAC 0 Reference 0.1 (0.0, 0.2) Reference 0.3 (0.2, 0.7)
CAC 1-99 3.4 (1.5, 7.5) 0.3 (0.2, 0.5) 0.9 (0.3, 2.7) 0.5 (0.3, 0.9)
CAC 100-399 4.5 (1.9, 10.8) 0.5 (0.3, 0.9) 1.1 (0.4, 3.1) 0.8 (0.4, 1.5)
CAC>400 13.1 (5.6, 30.3) 1.9 (1.3, 2.7) 2.2 (0.8, 5.9) 2.5 (1.7, 3.7)
CVD Mortality CAC 0 Reference 0.3 (0.2, 0.4) Reference 0.6 (0.3, 1.1)
CAC 1-99 1.5 (1.2, 2.8) 0.8 (0.6, 1.0) 1.3 (0.6, 2.7) 1.1 (0.7, 1.7)
CAC 100-399 2.0 (1.2, 3.3) 1.2 (0.8, 1.6) 1.5 (0.7, 3.2) 1.7 (1.1, 2.6)
CAC>400 5.3 (3.3, 8.6) 4.0 (3.1, 5.0) 2.4 (1.2, 5.1) 3.9 (2.9, 5.3)
Table 1. Prognostic utility of CAC in statin users versus non-users.  Adapted from Osei et al. Atherosclerosis 2021;316:79-83.

CAC volume and Agatston CAC score depicted associations with CVD/CHD in both groups; however, density was not associated with cardiac outcomes in statin users. Inclusion of CAC volume improved CVD/CHD prediction significantly while calcium density provided minimal additional predictive value in statin users. The effect of CAC score on predictive value by AUROC are summarized in Table 2.

Table 2

    Not on statin therapy On statin therapy
AUROC p value AUROC p value
CHD Mortality Risk Factors Alone 0.67 -- 0.74 --
RF + Volume Score 0.71 0.003* 0.77 0.03*
RF + Density Score 0.67 0.89 0.75 0.29
RF + Volume + Density 0.71 0.008* 0.78 0.02*
CVD Mortality Risk Factors Alone 0.72 -- 0.72 --
RF + Volume Score 0.74 0.007* 0.75 0.01*
RF + Density Score 0.72 0.79 0.72 0.81
RF + Volume + Density 0.74 0.008* 0.75 0.01*
Table 2. Area under the receiver operating curve improvements based on CAC inclusion.  Adapted from Osei et al. Atherosclerosis 2021;316:79-83.

Discussion

The study by Osei et al. enriches our understanding about the role of CAC scores in statin users, a critical clinical and research question for preventive cardiology.18,19 Studies in cohorts with high rates of baseline statin use, e.g., populations with diabetes,20 familial hypercholesterolemia,21,22 or patients with chest pain undergoing coronary computed tomography angiography (CCTA)23 suggest that CAC also stratifies risk of future events in these settings. It was unclear, however, whether the associations with events would be as strong as those in statin-naïve populations.

The findings confirm that CAC does have prognostic value among statin users, although the association is attenuated. Complicating interpretation is the inclusion of only fatal events and the relatively elevated, but still low, mortality rate in statin users versus non-users with a zero CAC score. This difference in mortality rate blunts the association for statin users compared to nonusers; however, absolute increases in mortality rate are similar with increasing CAC score (Table 1).

The elevated mortality rate is likely due to higher burden of cardiovascular risk factors and indication bias, although the point estimates for CVD events were significantly lower in users compared to nonusers. Given the similar improvement in prognostic value compared to risk factor only models (Table 2) and similar increases in absolute mortality rates, the attenuation of HR does not limit the clinical utility of using CAC scores for risk stratification in statin users.17

A key mechanism underlying this phenomenon is that statins increase plaque density thereby paradoxically raising the Agatston CAC score—as density is upweighted. The population of statin users with high CAC scores therefore encompasses very high-risk individuals and individuals with highly stable plaques at relatively lower risk of events. The latter subgroup adds "noise" to the association, taking HRs closer to the null value.

Some considerations can help clinicians deal with this paradox. First, increases in Agatston CAC scores caused by statins are modest and therefore very elevated CAC scores, e.g., >400 or >1000, should still be interpreted as indicative of extensive atherosclerosis and trigger aggressive preventive pharmacologic management. Although these cutoffs in statin users have not been studied, it is reasonable to follow recommendations based on risk according to CAC score in nonusers. 

CAC >100 warrants discussion of aspirin therapy and intensive BP goals, while CAC >300 and especially >1000 warrants intensifying lipid lowering therapy to secondary prevention levels (LDL<70), and other relevant preventive medications — GLP1RAs if diabetes present, icosapent ethyl.24-30 Our group has published guidance on how a CAC-guided approach could be used for the allocation of various preventive pharmacotherapies in patients with diabetes.31

Second, most research on the allocation of add-on therapies guided by CAC implicitly assumed that baseline rather than follow-up CAC scores would inform such allocation, and that this would happen in mostly statin-naïve populations.26,28,29 Accordingly, in patients with an initial (first) CAC scan >100, it may be reasonable to initiate not only statins, but also other relevant preventive medications early on — without the need for repeat CAC scanning later in time.

In patients with an initial CAC <100 who only required statin therapy, a subsequent CAC score while on statin therapy may guide stepwise introduction of additional preventive medications. This paradigm maximizes early atherosclerotic cardiovascular disease (ASCVD) protection and prevents therapeutic inertia.

Finally, an alternative approach to overcome the density paradox could be to expand the use of alternative CAC scoring methods that focus on volume alone or better distinguish the protective role of densely calcified plaque.32 However, this is hampered by a current lack of reference values, limited supportive research, and validation; implementation limitations include software update requirements and standardization.

Conclusions

Total CAC and volume holds prognostic value in statin users; however, the effects of statin on plaque histology requires clinicians to interpret CAC scores while accounting for statin use. The Agatston CAC score stratifies CHD/CVD event risk in statin users. The associations are attenuated compared to those seen among statin-naïve patients; however, a CAC score provides similar risk prediction in conjunction with risk factor models regardless of statin use status. Moving forward, expanded use of enhanced CAC scoring systems and, most importantly, early initiation of multiple risk-reduction medications, may bypass the plaque density paradox and enhance primary ASCVD protection for patients.

References

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Clinical Topics: Diabetes and Cardiometabolic Disease, Dyslipidemia, Prevention, Lipid Metabolism, Nonstatins, Novel Agents, Primary Hyperlipidemia, Statins

Keywords: Dyslipidemias, Calcium, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Cardiovascular Diseases, Coronary Vessels, Hyperlipoproteinemia Type II, Secondary Prevention, Research Design, Reference Values, Prognosis, Retrospective Studies, Follow-Up Studies, Area Under Curve, Coronary Disease, Risk Factors, Risk Assessment, Diabetes Mellitus, Diabetes Mellitus, Risk Reduction Behavior, Chest Pain, Atherosclerosis, Cardiology, Reference Standards, Acceleration, Aspirin, Lipids, Resource Allocation


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