Atherosclerotic Cardiovascular Risk Stratification in the BioImage Cohort
After the introduction of the Pooled Cohort Equation (PCE) from the American College of Cardiology (ACC)/American Heart Association (AHA) 2013 Guidelines on the Assessment of Cardiovascular Risk, there have been concerns for risk overestimation. Therefore, a strategy to improve the specificity that is, the ability to discern who truly will benefit most from lipid-lowering therapy is needed. Clinicians and patients both desire a more accurate assessment of an individual's atherosclerotic cardiovascular disease (ASCVD) risk, which is currently driven in large part by one's chronologic age. This investigative study utilized non invasive imaging (coronary artery calcium [CAC] and/or carotid plaque burden [CPB]) to address this conundrum by asking whether an imaging-based primary prevention strategy for an at-risk population could help customize preventative medication allocation in a more judicious manner.
The BioImage Study (A Clinical Study of Burden of Atherosclerotic Disease in an At-Risk Population) sought to identify imaging biomarkers that predict near-term (3-year) atherothrombotic events. This study enrolled 7,687 asymptomatic men 55 to 80 years of age and women 60 to 80 years of age who were members of the Humana Health System. From this initial number of participants, 6,102 subjects were enrolled in the imaging arm; all participants had no previous history of established cardiovascular disease (myocardial infarction [MI], stroke, angina, heart failure, or need for arterial revascularization); however, these patients still had risk factors for cardiovascular disease present (from the original population, 65% with hypertension, 57% with hyperlipidemia, 23% with diabetes).
From study participants, those without known atherosclerotic disease were selected and filtered to include those with ≥7.5% 10-year risk. This study deemed individuals statin ineligible if CAC or CPB was 0. In contrast, those with predicted 5 to <7.5% 10-year risk were classified as statin eligible if their CAC was ≥100 (or equivalent CPB of ≥300). This strategy was referred to as disease-guided reclassification.
A majority of participants (86%) qualified for ACC/AHA guideline directed statin therapy by achieving the threshold of ≥7.5% 10-year risk by PCE calculator. Out of this population, a significant amount had CAC/CPB scores of 0 (32%/23%, respectively). This population had very low event rates; e.g., cardiovascular disease (CVD) events were 0.8% for a CAC of 0 versus 4.3% in those with a CAC of ≥100. Those with a CPB of 0 vs ≥300 (threshold which matches the percentile for CAC=100) had CVD event rates of 1.1% vs 3.8%, respectively. For those with a CAC of 0 vs ≥100, coronary heart disease (CHD) events were 0.2% vs 3.0%, respectively. Lastly, for CPB of 0 vs ≥300 and CHD events, the former had an event rate of 0.5%, and the latter at 2.6%.
Using CAC to reclassify subjects, the specificity of CHD events improved by 22% (p <0.0001) without a significant loss in sensitivity. That is, CAC reduced potentially unnecessary treatment without compromising the identification of those who would go on to experience events. CPB improved specificity by 16% (p <0.0001) with a loss of sensitivity by 7%. The number needed to screen using CAC or CPB for either CHD or CVD events to identify one person with presence of absence of significant atherosclerosis (defined by CAC of ≥100 or CPB if ≥300) for each category of ASCVD risk (i.e., <5%, 5 to <7.5%, ≥7.5% to <15% and ≥15%) was ≤10 for all categories, e.g., to find those with a CAC of 0 who had ASCVD risk of 7.5 to 15% was 2.6 and 4.6 for those with ASCVD of ≥15%.
An approach utilizing non-invasive imaging in this study demonstrated an ability to reliably risk stratify older patients with a significant predicted ASCVD risk (≥7.5% 10-year risk by PCE calculator). Such a philosophy could be used to avoid low yield pharmacotherapy with a statin depending upon CAC/CPB scores. Use of such a strategy in light of the new ACC/AHA guidelines on cholesterol management can be taken into strong consideration by physicians seeking a more clinically objective algorithm when caring for patients with most elevations in predicted ASCVD risk.
The BioImage Study is a prospective observational cohort of men ages 55-80 and women ages 60-80 with cardiovascular risk factors, but without known ASCVD; women were well represented (56%), as were racial/ethnic minorities along with participants being recruited from two different geographical locations.1 Mortensen et al. utilized this cohort to replicate prior studies attempt to risk stratify participants with significant ASCVD risk (≥7.5%, as scored by the PCE) by CAC and CPB scores;2 this endeavor was undertaken as a way to potentially address the often discussed overestimated risk by the PCE, which would have a potential 13 million more Americans to undergo a clinician-patient risk discussion about starting a statin.3
The study by Mortensen et al. dovetails two relatively controversial topics that have yet to achieve consensus regarding their impact on long term cardiovascular outcomes: the ACC/AHA 2013 risk assessment guidelines for cholesterol management and the use of non invasive imaging (i.e., CAC and CPB) to correlate CHD/CVD events to plaque burden. The debate over the 2013 guidelines is well known and their potential shortcomings (i.e., overestimation of ASCVD risk by PCE calculator) have been repeatedly addressed.4-9 The aspect of the PCE that the authors highlight as driving the ASCVD risk score is age, a concern echoed by others as well.10
This study found that 86% of the BioImage cohort qualified as statin eligible due to 10-year ASCVD risk ≥7.5% (significantly higher than the estimated 49% of the general US adult population eligible for statin therapy under the new guidelines).3 The ASCVD risk estimate was ≥15% in 55% of patients; the minimal age for entry in this cohort was an age of 55 in men and 60 in women.
The authors demonstrated with increasing ASCVD risk scores that participants had concomitantly increasing plaque burden as determined by CAC and CPB scores. This subclinical atherosclerosis determined by CAC or CPB scores had a strong correlation with clinical events (i.e., CHD and CVD events); this held true even for those with diabetes. Not even a cardiovascular risk factor as powerful as diabetes diminished the CAC or CPB's ability to predict CVD/CHD events; the ability of CAC to predict low event rates in individuals with multiple traditional risk factors has been demonstrated in the MESA (Multi-Ethnic Study of Atherosclerosis) population as well.11 This is a thought provoking concept as it lends credence to the idea of 'de-risking' (downward adjustment of estimated ASCVD risk) those with apparent clinical risk due principally to increased age, but low plaque burden.
Turning attention to the feasibility of such an approach, a reasonable number needed to screen (NNS) of 2.6 and 4.5 was determined for those with ASCVD risk range of 7.5-15% and ≥15%, respectively, to find a participant with a CAC of 0. That means, for example, 39% of subjects with an ASCVD risk of 7.5-15% had a CAC of 0, a sizeable percentage of participants. However, one must be aware that this older population clearly has a higher ASCVD risk than the general population as aforementioned. Still, these are striking and intriguing numbers.
To many, the next appropriate endeavor would be to prospectively assess whether 'de-risking' (downgrading their estimated risk) such patients does not have a negative impact on long-term cardiovascular outcomes.12-13 With these findings, the authors of this study attempt to foster some harmony in application of the 2013 ACC/AHA guidelines as well as aid the individual patient striving to make an informed decision in a clinician-patient risk discussion.
One can highlight the inherent bias that exists with all such studies such as recruitment of those much more health conscious but also in the BioImage cohort: exclusion of the uninsured (all participants are Humana Health-Plan Members) as well the limitation in assessing claim-based risk assessment of factors such as smoking and sedentary lifestyle.1 The usage of lipid lowering medications in the different CAC score groups (i.e., 0 vs. 1-99 vs. ≥100) actually appeared to increase (27%, 35% and 40% respectively), while there was no such notable numerical trend in the CPB groups (45%, 34% and 38% in CPB scores 0, 1-299 and ≥300, respectively). Lastly, short term follow-up duration of this study is seemingly the main limitation.
In the end, employing noninvasive imaging such as CAC and CPB scores allowed for the improvement in the specificity of estimating cardiovascular risk without sacrificing sensitivity, when used sequentially with the PCE to estimate 10-year primary risk of ASCVD. What remains to be seen is if such an attempted refining of the 2013 ACC/AHA guidelines has a positive clinical and financial impact (e.g., minimizing polypharmacy/avoiding side effects), and how the medical community at large addresses the task of testing this seemingly robust hypothesis with clinical equipoise.
- Mutendam P, McCall C, Sanz J, Fuster V, High-Risk Plaque Initiative. The BioImage Study: novel approaches to risk assessment in the primary prevention of atherosclerotic cardiovascular disease—study design and objectives. Am Heart J 2010;160:49-57.
- Nasir K, Bittencourt MS, Blaha MJ, et al. Implications of coronary artery calcium testing among statin candidates according to the American College of Cardiology/American Heart Association cholesterol management guidelines: MESA (Multi-Ethnic Study of Atherosclerosis). J Am Coll Cardiol 2015;66:1657-68.
- Pencinan MJ, Navar-Boggan AM, D'Agostino RB, et al. Application of the new cholesterol guidelines to a population-based sample. N Engl J Med 2014;370:1422-31.
- Muntner P, Safford MM, Cushman M, Howard G. Comment on the reports of over-estimation of ASCVD risk using the 2013 AHA/ACC risk equation. Circulation 2014;129:266-7.
- Cook NR, Ridker PM. Further insight into the cardiovascular risk calculator: the roles of statins, revascularizations, and underascertainment in the Women's Health Study. JAMA Intern Med 2014;174:1964-71.
- Cook NR, Ridker PM. Response to comment on the reports of over-estimation of ASCVD risk using the 2013 AHA/ACC risk equation. Circulation 2014;129:268-9.
- Navar-Boggan AM, Peterson ED, D'Agostino RB, Pencina MJ, Sniderman AD. Using age- and sex-specific risk thresholds to guide statin therapy: one size may not fit all. J Am Coll Cardiol 2015;65:1633-9.
- Nissen SE. Prevention guidelines: bad process, pad outcome. JAMA Intern Med 2014;174:1972-3.
- DeFilippis AP, Young R, Carrubba CJ, et al. An analysis of calibration and discrimination among multiple cardiovascular risk scores in a modern multiethnic cohort. Ann Intern Med 2015;162:266-75.
- Karmali KN, Goff DC, Ning H, Lloyd-Jones DM. A systematic examination of the 2013 ACC/AHA pooled cohort risk assessment tool for atherosclerotic cardiovascular disease. J Am Coll Cardiol 2014;64:959-68.
- Silverman MG, Blaha MJ, Krumholz HM, et al. Impact of coronary artery calcium on coronary heart disease events in individuals at the extremes of traditional risk factor burden: the Multi-Ethnic Study of Atherosclerosis. Eur Heart J 2014;35:2232-41.
- McEvoy JW, Martin SS, Blaha MJ, et al. The case for and against a coronary artery calcium trial: means, motive, and opportunity. JACC Cardiovasc Imaging 2016;9:994-1002.
- Blumenthal RS, Hwang CW, Nasir K. Selective use of coronary artery calcium screening: worth the cost? J Am Coll Cardiol 2009;54:1268-70.
Clinical Topics: Diabetes and Cardiometabolic Disease, Dyslipidemia, Heart Failure and Cardiomyopathies, Prevention, Lipid Metabolism, Nonstatins, Acute Heart Failure, Heart Failure and Cardiac Biomarkers, Hypertension, Smoking
Keywords: Algorithms, Angina Pectoris, Atherosclerosis, Biological Markers, Cardiovascular Diseases, Cholesterol, Coronary Artery Disease, Diabetes Mellitus, Dyslipidemias, Heart Failure, Hyperlipidemias, Hypertension, Lipids, Myocardial Infarction, Polypharmacy, Primary Prevention, Risk Assessment, Risk Factors, Sedentary Lifestyle, Smoking, Stroke
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