A 50-year-old non-smoking, nondiabetic man with good diet and exercise habits comes to your office. He has family history of premature coronary heart disease in his father. Today his blood pressure (BP) is 139/85, his high-density lipoprotein (HDL) cholesterol is 50 mg/dL, and his low-density lipoprotein (LDL) cholesterol is 150 mg/dL. How would you select the best preventive strategy for this patient according to latest evidence and guidelines?

The first step in the evaluation of asymptomatic primary prevention patients is to evaluate their risk of cardiovascular disease (CVD). In this example, we can use the Pooled Cohort Equation (PCE), which estimates the risk of atherosclerotic CVD (ASCVD)—inclusive of myocardial infarction and stroke—in the coming 10 years. Using the PCE, the 2013 American College of Cardiology (ACC) and American Heart Association (AHA) prevention guidelines categorize primary prevention patients without possible familial hypercholesterolemia and without diabetes into three "statin decision" groups (Figure 1).¹

Figure 1: The ACC/AHA Guideline on Lipid-Lowering Therapy

This patient's 10-year ASCVD risk using the PCE is 4.6%. Would this seemingly low-risk patient, who falls into the "statins not recommended" group, benefit from further evaluation?

In fact, all patients under the 2013 ACC/AHA rubric should undergo a risk discussion before deciding on statin therapy (Figure 1). This patient with a family history of premature coronary heart disease may benefit from a further discussion about the possible initiation of statin therapy. The discussion should include the potential for ASCVD risk reduction, management of non-lipid risk factors, and promotion of a heart-healthy lifestyle. This discussion should also include the potential benefits, harms, and drug interactions of therapies including statins as the first-line treatment, emphasizing risk-based treatment decisions; if there is uncertainty about the therapeutic decision, clinicians should mention the possibility of further testing options to improve the risk estimate, encouraging the patient to ask questions and express values and/or preferences.² Coronary artery calcium (CAC) scoring, as well as high-sensitivity C-reactive protein and ankle-brachial index, are examples of tests that the ACC/AHA proposed further risk stratification and guiding treatment options.

Given the patient's family history, he is dissatisfied with a risk-factor-only approach to risk prediction. He desires to learn more about CAC scoring.

CAC, performed using non-contrast cardiac-gated computed tomography (CT), can be carried out in 10-15 minutes on any modern CT scanner. It is associated with about 1-2 mSv of radiation, equivalent to approximately 2 bilateral mammograms, and usually costs <$150. In a recent systematic review and meta-analysis, Gupta et al. showed that presence of CAC appears to motivate an improved diet, increased exercise, and the initiation of and adherence to preventive therapies.³

There are now many comparative effectiveness studies of CAC to other tests regarding risk prediction. In 2012, before the latest ACC/AHA guidelines were published, Yeboah et al. showed that addition of CAC increased area under the receiver operating characteristic curve of the Framingham risk score from 0.623 to 0.784 in intermediate-risk patients, with CAC showing superior discrimination and risk reclassification compared with ankle-brachial index and high-sensitivity C-reactive protein.⁴ In two more recent studies by Yeboah et al., CAC demonstrated the strongest ability of all ACC/AHA guideline-endorsed tests to improve to the PCE,⁵ showing the lowest number needed to screen for identifying an individual with baseline <7.5% risk to be reclassified as statin eligible with an abnormal test result.⁶

Outcome studies on the absence of CAC (CAC = 0) extended the knowledge about risk prediction of CAC. Blaha et al. showed that CAC = 0 was the strongest negative risk factor among 13 supposed reassuring factors in MESA (Multi-Ethnic Study of Atherosclerosis).⁷ Pursnani et al. showed that CAC = 0 was associated with a low CVD rate (1.6%) in statin-eligible participants.⁸ Mortensen et al. in the BioImage Study: A Clinical Study of Burden of Atherosclerotic Disease in an At-Risk Population demonstrated that the CAC = 0 could down-classify risk of elderly participants who are statin eligible to statin ineligible, perhaps sparing a significant proportion of the elderly population from taking statin.⁹

The 2013 ACC/AHA guideline writers have acknowledged the new accumulating evidence base for CAC. Dr. Donald Lloyd-Jones, writer on the risk prediction guidelines, wrote "Now, in 2017, we probably have enough evidence to change the paradigm in a way that will incorporate disease screening, likely using CAC since it has the best evidence base. ACC and AHA are looking to convene a new risk assessment guideline in part to address this."¹⁰

The patient is impressed by the data supporting CAC but is not interested in population-based studies. The patient wants to know if CAC results are actionable and if they help personalize his own therapy decisions.

The strong risk prediction has prompted investigators to publish clinical decision algorithms using CAC. In a landmark paper, Nasir et al. categorized MESA participants according to their ASCVD risk score and presence of CAC. They found that 57% and 45% of the 5 to <7.5% and 7.5 and <20% ASCVD risk groups, respectively, would be expected to have CAC = 0 (and therefore be lower risk). Absence of CAC down-classified both of these groups, which were initially categorized by their ASCVD risk as "statins to be considered" and "statins recommended" to "statins not recommended" (Figure 2).¹¹ Nasir et al. found that CAC was not as helpful when ASCVD risk was >20%.

Figure 2: Implication of CAC on Statin Therapy¹¹

Based on these findings, in 2017 the Society of Cardiovascular Computed Tomography (SCCT) published new guidelines on the appropriate use of CAC scoring. Importantly, the SCCT recommended performing CAC scoring in select patients with an ASCVD risk between 5 and 20% in the context of shared decision-making. SCCT also suggested using CAC score in selected patients with ASCVD risk <5%, especially in those with family history of premature coronary heart disease.¹²

The patient elects to have a CAC score, which is found to be 300 (95th percentile for age/sex/race). What does this information add to our knowledge about this patient?

In 2015, McClelland et al. published the MESA coronary heart disease risk score.¹² This score is the first score to incorporate the CAC score into its calculator. It is useful for determining coronary heart disease risk both before and after consideration of the CAC score. The C-statistic of the MESA coronary heart disease risk score improves from 0.75 to 0.8 when the CAC score is considered in the model. The MESA risk score has been validated in two external cohorts, and in addition to the web-based calculator, a useful app is now available for Android and iPhone.

The estimated 10-year coronary heart disease risk of this patient according to MESA risk score, before considering the CAC score, is 6.1%. However, after considering the patient's elevated CAC score, the 10-year risk is now 13.3%. You also demonstrate that his score would have been 2.9% if his CAC score were zero. The patient is amazed how different his risk is when considering the CAC score compared with relying on risk factors only and is now interested in aggressive therapy.

Beyond statins, the CAC score may also have implications for aspirin therapy. For example, Miedema et al. estimated the number needed to treat (NNT) for different CAC strata by applying an expected 18% relative reduction in coronary heart disease to baseline risk estimates. The authors set the number needed to harm of major bleeding to be 442 according to a meta-analysis. They found that participants with CAC ≥ 100 would probably benefit from aspirin therapy regardless of their traditional 10-year risk score. Conversely, participants with CAC zero would likely not benefit from aspirin (NNT = 2,036, and 808 if Framingham Risk Score were <10% and ≥10, respectively).¹⁴

McEvoy et al. demonstrated that the CAC score might guide the selection of patients for more intensive BP control (systolic BP < 120). This is important following the discrepant results of SPRINT (Systolic Blood Pressure Intervention Trial) and the HOPE-3 (Heart Outcomes Prevention Evaluation–3) trial. They estimated the 10-year NNT of an intensive BP goal by applying the relative risk reduction per systolic BP estimated by meta-analysis. In summary, they found that combination of CAC and ASCVD risk score might help to optimize systolic BP goals for each patient, especially among adult patients with ASCVD risk of 5-15% and pre-hypertension or mild hypertension.¹⁵

The CAC score likely also has a role in selecting LDL goals and therefore in the selection of statin intensity and/or use of non-statin therapies. There is an emerging expert opinion that patients with high CAC scores may benefit from the most aggressive LDL goals (i.e., <70) and, therefore, consideration of non-statin therapies like ezetimibe in addition to statins for optimal risk lowering.

Recently, Hong et al. examined the cost-effectiveness of CAC scoring among intermediate-risk patients, taking a societal perspective. They found in the base model that a "treat-all" primary prevention strategy has similar economic and clinical consequences to a CAC-guided patient selection (despite many fewer patients treated), thus supporting selective use of CAC in the setting of shared decision-making when the patient desires flexible—and personalized—treatment options.¹⁶

The above case is illustrative of the value of CAC over exclusive reliance on risk factors. Using the PCE only, the patient in this case may not qualify for any preventive pharmacotherapy. This would make sense if the patient had CAC = 0. However, we would argue that the patient with CAC > 300 would benefit not only from high-intensity statin, but also from a daily baby aspirin and the consideration of BP therapy and add-on non-statin therapy to achieve goals of systolic BP of about 120-130 and LDL < 70.

References

1. Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2014;63:2935-59.
2. Martin SS, Sperling LS, Blaha MJ, et al. Clinician-patient risk discussion for atherosclerotic cardiovascular disease prevention: importance to implementation of the 2013 ACC/AHA Guidelines. J Am Coll Cardiol 2015;65:1361-8.
3. Gupta A, Lau E, Varshney R, et al. The Identification of Calcified Coronary Plaque Is Associated With Initiation and Continuation of Pharmacological and Lifestyle Preventive Therapies: A Systematic Review and Meta-Analysis. JACC Cardiovasc Imaging 2017;10:833-42.
4. Yeboah J, McClelland RL, Polonsky TS, et al. Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. JAMA 2012;308:788-95.
5. Yeboah J, Young R, McClelland RL, et al. Utility of Nontraditional Risk Markers in Atherosclerotic Cardiovascular Disease Risk Assessment. J Am Coll Cardiol 2016;67:139-47.
6. Yeboah J, Polonsky TS, Young R, et al. Utility of Nontraditional Risk Markers in Individuals Ineligible for Statin Therapy According to the 2013 American College of Cardiology/American Heart Association Cholesterol Guidelines. Circulation 2015;132:916-22.
7. Blaha MJ, Cainzos-Achirica M, Greenland P, et al. Role of Coronary Artery Calcium Score of Zero and Other Negative Risk Markers for Cardiovascular Disease: The Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 2016;133:849–58.
8. Pursnani A, Massaro JM, D'Agostino RB Sr, O'Donnell CJ, Hoffmann U. Guideline-Based Statin Eligibility, Coronary Artery Calcification, and Cardiovascular Events. JAMA 2015;314:134-41.
9. Mortensen MB, Fuster V, Muntendam P, et al. A Simple Disease-Guided Approach to Personalize ACC/AHA-Recommended Statin Allocation in Elderly People: The BioImage Study. J Am Coll Cardiol 2016;68:881-91.
10. Sabatini, J. CT Slice: What's the Score With CCS? (Radiology Today website). April 2017. Accessed 7/19/2018. Available at: http://www.radiologytoday.net/archive/rt0417p06.shtml.
11. Nasir K, Bittencourt MS, Blaha MJ, et al. Implications of Coronary Artery Calcium Testing Among Statin Candidates According to American College of Cardiology/American Heart Association Cholesterol Management Guidelines: MESA (Multi-Ethnic Study of Atherosclerosis). J Am Coll Cardiol 2015;66:1657–68.
12. Hecht H, Blaha MJ, Berman DS, et al. Clinical indications for coronary artery calcium scoring in asymptomatic patients: Expert consensus statement from the Society of Cardiovascular Computed Tomography. J Cardiovasc Comput Tomogr 2017;11:157–68.
13. McClelland RL, Jorgensen NW, Budoff M, et al. 10-Year Coronary Heart Disease Risk Prediction Using Coronary Artery Calcium and Traditional Risk Factors: Derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) With Validation in the HNR (Heinz Nixdorf Recall) Study and the DHS (Dallas Heart Study). J Am Coll Cardiol 2015;66:1643–53.
14. Miedema MD, Duprez DA, Misialek JR, et al. Use of coronary artery calcium testing to guide aspirin utilization for primary prevention: estimates from the multi-ethnic study of atherosclerosis. Circ Cardiovasc Qual Outcomes 2014;7:453–60.
15. McEvoy JW, Martin SS, Dardari ZA, et al. Coronary Artery Calcium to Guide a Personalized Risk-Based Approach to Initiation and Intensification of Antihypertensive Therapy. Circulation 2017;135:153–65.
16. Hong JC, Blankstein R, Shaw LJ, et al. Implications of Coronary Artery Calcium Testing for Treatment Decisions Among Statin Candidates According to the ACC/AHA Cholesterol Management Guidelines: A Cost-Effectiveness Analysis. JACC Cardiovasc Imaging 2017;10:938–52.

Keywords: Diagnostic Imaging, Risk Factors, Cholesterol, LDL, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Cholesterol, HDL, C-Reactive Protein, Methyltestosterone, Blood Pressure, Ankle Brachial Index, Prehypertension, Aspirin, ROC Curve, Factor XIII, Research Personnel, Coronary Vessels, Hyperlipoproteinemia Type II, Risk Assessment, Atherosclerosis, Coronary Disease, Stroke, Primary Prevention, Myocardial Infarction, Tomography, X-Ray Computed, Diabetes Mellitus, Risk Reduction Behavior, Outcome Assessment, Health Care, Drug Interactions, Hypertension, Tomography

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