Controversies in
Imaging
Is There a Role for Coronary Artery Calcium
Scoring for Management of Asymptomatic
Patients at Risk for Coronary Artery Disease?
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Clinical Risk Scores Are Not Sufficient To Define Primary
Prevention Treatment Strategies Among Asymptomatic Patients
Michael J. Blaha, MD, MPH; Michael G. Silverman, MD; Matthew J. Budoff, MD
T
he central principle of primary prevention is that treatment decisions must be carefully matched to accurate estimates of risk. The currently accepted method for
determining coronary heart disease (CHD) risk among
asymptomatic individuals is through calculation of the risk
factor–based Framingham Risk Score (FRS).1 The FRS relies
predominantly on age, sex, and to a lesser degree the traditional modifiable CHD risk factors (smoking, blood pressure,
total cholesterol, high-density lipoprotein cholesterol, diabetes mellitus) to derive a statistical probability of developing
a myocardial infarction or CHD-related death in the ensuing 10 years. Although the FRS has proven to be a useful
tool, its overall predictive value in modern cohorts is modest
(C-statistic, ≈0.70–0.75).2 Risk factor profiles widely overlap in those with and without CHD events, with the FRS
failing to identify many truly high-risk individuals who are
likely to benefit from preventive therapy. For example, 75%
of younger patients presenting with ST-elevation myocardial infarction were considered low risk the day before their
event.3 The majority of all CHD events continue to occur in
patients considered either low or intermediate risk at baseline
FRS assessment.4
Improving the FRS by adding risk factors has proven difficult largely because of the fundamental limitations inherent to any risk factor–based approach. The most important
limitation is the reliance on 1-time measurements of a small
collection of routinely available risk factors taken at a relatively late stage in life (Figure 1A). This strategy may seem
fundamentally at odds with the pathophysiology of atherosclerosis. For example, we know from Mendelian randomization studies,5,6 cohorts of young patients,7,8 and autopsy
studies9,10 that CHD risk exposure begins early, varies in
intensity over the course of development and adulthood,
and includes many poorly accounted for genetic and environmental determinants.11 Common clinical measurements
may not capture this time-dependent risk exposure. For
example, routine clinical measurements of blood pressure
are notoriously variable and tell us little about the lifetime
exposure to hypertension and likelihood of end-organ damage.12 The emerging data suggest that CHD risk is likely
best expressed as a function of cumulative exposure to all
risk determinants over a lifetime.
A recent advance in risk prediction is the concept of lifetime risk. For example, there is now a 30-year risk calculator
from the Framingham Heart Study13 and a risk stratification
tool provided by the Cardiovascular Lifetime Risk Pooling
Project.14 However, these models capture only 1 element of
Response by Andersson and Vasan see p 408
From The Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Baltimore, MD (M.J. Blaha, M.G.S.); and Los Angeles Biomedical
Research Institute, Harbor-UCLA Medical Center, Torrance, CA (M.J. Budoff).
The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.
This article is Part II of a 2-part article. Part I appears on page 390.
Correspondence to Matthew Budoff, MD, David Geffen School of Medicine at UCLA, Los Angeles Biomedical Research Institute, 1124 W Carson St,
RB-2, Torrance, CA 90502. E-mail [email protected]
(Circ Cardiovasc Imaging. 2014;7:398-408.)
© 2014 American Heart Association, Inc.
Circ Cardiovasc Imaging is available at http://circimaging.ahajournals.org
398
DOI: 10.1161/CIRCIMAGING.113.000341
Blaha et al Biomarkers Are Not Enough for Risk Stratification 399
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Figure 1. Strategies for risk prediction. Traditional risk models (A) use 1-time measurements of risk factors taken later in life—in this
example at age 50—and project coronary heart disease (CHD) risk during the coming 10 years. Lifetime risk models (B) similarly use
1-time measurements of risk factors but instead project long-term CHD risk. Subclinical atherosclerosis (C), by directly visualizing the
accumulated burden of atherosclerosis, integrates lifetime risk exposure and may provide more accurate estimates of near-term and
long-term risk.
lifetime risk—the extension of risk forecasting further into the
future (Figure 1B). Although termed lifetime risk, these models offer no advance in the integration of time-dependent risk
exposure over one’s lifetime. Rather, they continue to emphasize 1-time measurement of traditional risk factors typically
collected in an older patient.
One strategy for improving risk prediction is through the
use of novel serum biomarkers. Serum biomarkers may offer
insight about a new pathophysiologic pathway (inflammation
with high-sensitivity C-reactive protein [hsCRP]) or may
indicate hemodynamic stress (pro-brain natriuretic peptide)
or end-organ myocardial damage (high-sensitivity troponin).
However, 1-time measurements of serum markers suffer
from lack of specificity, repeat-measurement variability, and
modest ability to reflect cumulative risk exposure. To date,
serum biomarkers have offered little improvement over the
FRS.15 Among ≈250 000 individuals from the Emerging Risk
Factors Collaboration, adding hsCRP to the FRS improved
risk classification by just 1.5%, with the authors estimating that ≈500 intermediate-risk people would need hsCRP
testing to prevent 1 cardiovascular disease (CVD) event during a 10-year period.16
An alternative strategy for improving risk prediction
involves the use of imaging to directly measure the accumulated burden of atherosclerosis. Atherosclerosis imaging
tests seek to personalize risk assessment by integrating the
cumulative interaction of early risk determinants (ie, genetics and epigenetics) with lifetime exposure to measured (ie,
blood pressure and serum cholesterol levels) and unmeasured
(ie, air pollution and secondhand smoke) risk factors and
directly displaying the resultant effect on the chosen vascular
bed in an individual patient (Figure 1C). In this way, atherosclerosis imaging tests may mitigate imprecision in quantifying early-­life risk exposures, especially those that accrue
before medical encounters, as well as our imperfect ability to
measure risk exposure severity in the clinic setting.17 Equally
importantly, direct visualization of the vascular bed allows
clinicians to identify individuals who, for unclear reasons, do
not develop atherosclerosis despite seemingly significant risk
factor exposure.
400 Circ Cardiovasc Imaging March 2014
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Serum biomarkers and imaging tests represent fundamentally
different types of risk information that require distinct terminology. Serum biomarkers are more similar to traditional risk factors,
signaling increased risk of developing a disease and attempting
to describe a distinct pathophysiologic process. Atherosclerosis
imaging tests are best considered disease scores, offering direct
measurement of the disease of interest and offering integration
rather than separation of distinct pathophysiologic mechanisms.
In this editorial review, we argue that traditional risk factors and serum biomarkers are insufficient for guiding modern risk-based treatment in primary prevention. To support
this hypothesis, we point to data demonstrating the following:
(1) marked heterogeneity between age/traditional risk factors
and atherosclerosis burden; (2) marked heterogeneity in event
rates within traditional risk strata, with the predominant driver
of event rates being atherosclerosis burden; and (3) inability
of serum biomarkers to produce clinically meaningful risk
reclassification beyond the FRS.
In our discussion, we use coronary artery calcium (CAC)
scoring from noncontrast cardiac computed tomography as
the current prototype of subclinical atherosclerosis imaging
in primary prevention. The use of CAC for risk stratification is currently supported in national guidelines by a class
IIa recommendation.
Heterogeneity Between Traditional
Risk and Atherosclerosis Burden
A fundamental observation is that atherosclerosis burden is
not an obligatory finding in older patients or those with many
risk factors.18–21 Likewise, young patients and those with 0 or
1 risk factor may have an increased burden of atherosclerosis
(Figure 2).Although there is a direct relationship between predicted FRS and the presence and severity of CAC, the distribution of CAC within FRS groups remains heterogeneous.22
The National Heart, Lung, and Blood Institute (NHLBI)funded population-based Multi-Ethnic Study of Atherosclerosis
(MESA), which enrolled apparently healthy adults with no
known CVD, is an optimal study to observe this heterogeneity. For example, when MESA participants are stratified by age
and risk factor burden, 2 of the most important components of
the FRS, a substantial amount of disagreement is noted with
the directly measured atherosclerosis burden.18,19 The number
needed to scan (NNS) to detect a CAC score=0 among older
individuals aged 75 to 84 is ≈5. Similarly, among individuals
with ≥3 risk factors, the NNS to detect a CAC score=0 is just
≈3. Nearly identical trends can be observed for increasing lowdensity lipoprotein levels, smoking status, and diabetes mellitus status. Among those traditionally classified as intermediate
to high risk based on age, conventional risk factor burden,
Figure 2. Heterogeneity in atherosclerosis across
traditional risk groups. The distribution of coronary
artery calcium (CAC) is heterogenous across
age and risk burden, 2 of the key components of
traditional risk scores. Adapted with permission
from Nasir et al20 and Tota-Maharaj et al.21
Authorization for this adaptation has been obtained
from both the owner of the copyright in the original
work and the owner of copyright in the translation
or adaptation.
Blaha et al Biomarkers Are Not Enough for Risk Stratification 401
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or calculated risk score, the NNS to detect a CAC score=0
remains <6. Among individuals with no modifiable risk factors, the NNS to identify 1 individual with CAC >100 is 9.
Again using data from MESA, Okwuosa et al22 have demonstrated that among individuals with 10-year FRS estimates
of 5% to 7.5%, 7.5% to 10%, 10% to 15%, and 15% to 20%,
the prevalence of CAC ≥100 was 18%, 25%, 33%, and 41%,
respectively, translating into an NNS of 5.5, 4, 3, and 2.5 to
detect a CAC score ≥100. At the same time, individuals with a
10-year FRS estimate of 10% to 15%, 15% to 20%, and >20%
had a prevalence of CAC=0 of 36%, 27%, and 17%, translating into a respective NNS of 2.8, 3.7, and 5.6 to detect a CAC
score=0. To put this in perspective, for a patient with an intermediate FRS of 10% to 15%, one is about as equally likely to
discover CAC=0 as an elevated burden of atherosclerosis as
indicated by CAC >100.
The Coronary Artery Risk Development in Young Adults
(CARDIA) study, a population-based cohort of young asymptomatic individuals aged 33 to 45 years, demonstrates this
conundrum in young patients. Given the importance of chronologic age in the FRS, it is difficult for patients aged <45 years
to be considered anything but low risk. Likewise, many argue
that CAC is a late finding in atherosclerosis and cannot be
used to identify young patients at risk. Despite this, 19% of
CARDIA participants with an FRS of 5% to 10% had some
CAC (NNS≈5), whereas 17% of these young individuals with
an FRS >10% already had a CAC score of ≥100 (NNS≈6).23
Effect of Heterogeneity on Risk Prediction
Even more important is the heterogeneity between traditional risk and observed events, with discrepant event rates
driven by the directly observed burden of atherosclerosis.
This concept was highlighted in an observational cohort of
≈44 000 asymptomatic individuals free of known CHD who
were referred for CAC testing and followed up for all-cause
mortality.20,21 Across age categories and risk factor burden,
there is a marked variation in mortality rate that is most
strongly associated with CAC burden (Figure 3). Of great
importance is that all individuals with a CAC score ≥100
have a substantially elevated mortality rate regardless of age
or risk factor burden.
The above mortality studies have been limited by self-­
reported risk factor status, potential referral bias, and lack of
CHD-specific mortality. However, strikingly similar results
have been observed in the population-based prospective
MESA cohort where risk factors were rigorously measured
and hard CHD events were adjudicated by committee.18,19
Again, the presence of increased atherosclerosis burden (CAC
≥100) was associated with a significant increase in event rate
across any given age group or risk factor burden status. This
Figure 3. Effect of coronary artery calcium (CAC)
on event rates within traditional risk factor strata.
Within traditional risk strata, as identified by age
group and risk factor burden, the quantity of CAC is
the primary driver of adverse events. Adapted with
permission from Nasir et al20 and Tota-Maharaj et al.21
Authorization for this adaptation has been obtained
from both the owner of the copyright in the original
work and the owner of copyright in the translation or
adaptation.
402 Circ Cardiovasc Imaging March 2014
important heterogeneity of events by CAC burden has also
been observed among low-risk women, across lipoprotein
cholesterol levels, and the traditional FRS categories.24,25
Statistical Risk Marker Validation
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Most biomarkers do not require advanced statistical testing
because they fail ≥1 of 2 important initial steps: (1) documentation of sufficient heterogeneity between traditional risk
estimates and values of the risk marker; and (2) documentation of substantial heterogeneity between traditional risk and
CHD events, with the disparity driven by the biomarker. However, as shown above, CAC fulfills both of these criteria. Furthermore, validation is then justified and provided by more
advanced statistical tests.
In MESA, the Rotterdam study, the Heinz Nixdorf
Recall Study, and the Early Identification of Subclinical
Atherosclerosis by Noninvasive Imaging Research (EISNER)
study, CAC has been shown to enhance overall risk discrimination (using the C-statistic) and to improve classification
of individuals into more appropriate risk groups for clinical
decision making (using net reclassification improvement).25–28
In the landmark article from MESA, Detrano et al29 noted
that individuals with a CAC score ≥100 had a multivariableadjusted hazard ratio of >7 for major coronary events and
a concomitant improvement in the C-statistic from 0.77 for
models using the individual traditional risk variables to 0.82
adding CAC. These results have been replicated in other
cohorts.26–28 Also from MESA, and again replicated in other
cohorts,26–28 Polonsky et al25 demonstrated a net reclassification improvement of 25% for the entire cohort and 54% for the
intermediate-risk group.
referred for clinical CAC scans, the mortality rate was just
0.5% at mean 5.6 years of follow-up.32 In 2 of the largest population-based cohort studies using baseline CAC assessment,
<1% of individuals had a hard CHD event during 5 years of
follow-up.33,34 Importantly, studies have shown that mortality
in young patients aged <45 years with CAC >100 is actually
higher than for older patients aged >75 years with CAC=0.23
Patients with 0 risk factors but CAC >100 have worse survival
than those with ≥3 risk factors but CAC=0.22
Identifying a low-risk primary population is extremely
important because it provides clinicians and policymakers a
rationale for using less healthcare resources in a population
of patients unlikely to receive net benefit from intervention.
Although CAC testing achieves this goal, the existing data
suggest that biomarkers (such as hsCRP) cannot reliably rule
out either atherosclerosis or future CVD events. Rather than
identifying those who do not need treatment, serum biomarkers are nearly exclusively added to the FRS to raise risk estimates and thus are inextricably tied to more treatment and
downstream cost.
The potential clinical implications associated with CAC=0
are broad and include more selective use of aspirin and statin
for primary prevention and more selective use of downstream
testing. For example, among individuals with nonoptimum
risk factors but CAC=0, we would recommend against the initiation or continuation of aspirin given the likelihood that the
risk of bleeding will outweigh the anticipated cardiovascular
benefit. About statin use, we would recommend treating those
with CAC=0 to less aggressive targets with low-dose statins
given that the side effects of statins seem to be dose dependent. Given the extremely low risk, there should be essentially
no downstream testing in the CAC=0 group.
The Imaging Hypothesis—the Power of CAC=0
A common feature of all anatomic imaging modalities is a
high sensitivity for detecting clinically important disease. The
imaging hypothesis states that given the high sensitivity of
imaging tests, the greatest value lies in the negative predictive value, determining who is at low risk for developing an
adverse outcome. The imaging hypothesis holds true for atherosclerosis imaging. In asymptomatic individuals, it is highly
unlikely for a patient to have clinically important coronary
artery disease and have an adverse event when CAC=0.30
The so-called power of 0 has been demonstrated in multiple
studies (Table 1). In a pooled analysis of 71 595 patients, the
overall mortality rate at 4.2 years when CAC=0 was 0.5%.31
Likewise, in a retrospective cohort study of 44 052 patients
CAC Versus hsCRP
hsCRP has been championed as one of the most promising
serum biomarkers. To assess the comparative effectiveness of
hsCRP, one must identify studies that directly compare hsCRP
and CAC in populations where these tests are likely to be used
in clinical practice.
Such analyses have been performed in MESA.35 In MESA
participants with normal low-density lipoprotein cholesterol
<130 who otherwise fit criteria for enrollment in the Justification
for the Use of Statins in Primary Prevention: An Intervention
Trial Evaluating Rosuvastatin (JUPITER) trial, the presence of
CAC was associated with a 4.3-fold increase in CHD events,
whereas hsCRP ≥2 mg/L was not associated with either CHD
Table 1. Outcome Studies Demonstrating Risk of CAC Score of 0
Study (Study Type )
Study Size Number With CAC=0 Study With CAC=0, % Follow-Up Time, y
Outcome
Events (Event Rate)
Sarwar et al (pooled analysis )
71 595
29 312
41
4.2
CVD events
154 (0.5%)
Blaha et al32 (retrospective cohort)
44 052
19 898
45
5.6
All-cause mortality
104 (0.5%)
MESA33 (prospective cohort)
6809
3414
50
4.1
CHD events, hard
10 (0.3%)
Heinz Nixdorf Recall (prospective cohort)
4129
1322
32
5.0
CHD events, hard
11 (0.8%)
31
34
CAC indicates coronary artery calcium; CHD, coronary heart disease; CVD, cardiovascular disease; and MESA, Multi-Ethnic Study of Atherosclerosis.
Blaha et al Biomarkers Are Not Enough for Risk Stratification 403
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or CVD after multivariable adjustment. In the Heinz Nixdorf
Recall Study (mean FRS, 11%), hsCRP values of 1 to 3 mg/L
did not predict events compared with hsCRP <1 mg/L after
multivariable adjustment. Although hsCRP >3 mg/L was associated with a modest 82% increased CHD risk in this study, CAC
>400 was associated with a hazard ratio of 5.9 for CHD events.36
Furthermore, adding CAC to the FRS + hsCRP improved risk
discrimination, whereas adding hsCRP to the FRS + CAC had
no effect on risk discrimination, suggesting that not only does
CAC provide a greater improvement in risk discrimination but
adding hsCRP on top of CAC adds little benefit.
Proponents of hsCRP argue that the true value of this test
lies in its ability to selectively identify patients who will
receive benefit from statin therapy. However, the often-quoted
JUPITER trial is not a biomarker trial and thus cannot substantiate this claim. For example, JUPITER exclusively enrolled
patients with high hsCRP ≥2 mg/L, and it is unknown whether
patients with low hsCRP would have received a similar benefit
from rosuvastatin. Subsequent analyses from JUPITER and
other trials have shown no evidence of effect modification
by hsCRP level.37,38 In fact, statins display remarkably consistent relative risk reduction in nearly all patient groups. In
the absence of evidence that the relative benefit of statins is
dependent on hsCRP status, the question becomes one of the
predicting absolute benefits.
In this light, Blaha et al35 further demonstrated the potential treatment implications of using CAC to guide statin use
in clinical practice. In MESA participants fitting criteria for
the JUPITER trial (older age, low-density lipoprotein cholesterol <130 mg/dL, and hsCRP ≥2 mg/L), ≈50% had CAC=0.
These individuals had a low event rate and thus had a highly
unfavorable estimated 5-year number needed to treat (NNT)
of 549 to prevent 1 CHD event with rosuvastatin treatment. In
contrast, most CHD events (74%) occurred in the small group
of JUPITER-eligible patients with CAC>100; treatment in this
group would be associated with a highly favorable 5-year NNT
of 24. The authors argue that focusing treatment of the subset
of individuals with measurable atherosclerosis might represent
a more appropriate allocation of resources and might reduce
healthcare cost compared with traditional risk factor or hsCRPbased approaches.
The potential for improved absolute risk reduction (and thus
NNT) when using CAC to guide preventive therapy was further highlighted in a double-blind randomized controlled trial,
whereby patients with CAC scores >80th percentile for age
and sex were randomized to either atorvastatin 20 mg or placebo. In the subset with baseline calcium scores >400 (47% of
the study population), treatment reduced the incidence of all
CVD events by 42% (8.7% versus 15%; P=0.046; estimated
5-year NNT ≈14). In this study, C-reactive protein did not predict events independently of CAC.39
CAC and hsCRP in Intermediate-Risk Patients
Intermediate-risk patients represent the ideal target population
for improved risk discrimination because treatment decisions
in this subgroup are frequently uncertain. Using data from
MESA, Yeboah et al40 performed a comparison of several risk
markers in the broad intermediate-risk category. Although
CAC, family history of CHD, ankle-brachial index, and
hsCRP were each independently associated with increased
risk for incident CHD, carotid intima–media thickness and
brachial flow-mediated dilation were not associated. The corresponding net reclassification improvement statistics were
as follows: CAC=0.659, family history=0.16, ankle-brachial
index=0.036, hsCRP=0.079, carotid intima–media thickness=0.102, and brachial flow-mediated dilation=0.024.
We used the above MESA data to extend the comparison of CAC with hsCRP in the intermediate-risk population
(Figure 4). Approximately 91% of all CHD events occurred
in intermediate-risk individuals with CAC >0. In contrast, the
majority of events occurred in patients with hsCRP <2 mg/L.
As can be seen in Figure 4, focusing treatment with generic
statins on patients with measurable atherosclerosis may potentially maximize the absolute benefit from statin therapy.
CAC Versus Other Serum Biomarkers
Although many serum biomarkers display modest associations with CHD events, studies of these markers commonly
have small incremental effect sizes with substantial evidence
of positive reporting and publication bias. Few serum biomarkers have shown sufficient heterogeneity with traditional
risk models to demonstrate even modest incremental value
over the FRS. For example, a recent study from the Rotterdam cohort evaluated the predictive ability of several novel
risk markers (Table 2).26 Although several biomarkers were
noted to have associations with increased CHD events, only
CAC and possibly N-terminal pro-brain natriuretic peptide
were noted to have a clinically meaningful effect on risk discrimination and reclassification compared with risk prediction
based on conventional risk factors alone.
Multiple Serum Biomarker Approach
Given the limited benefit seen with individual serum biomarkers, researchers have investigated the use of combined
biomarker scores. Previous cohort analyses have yielded generally unfavorable or mixed results on the ability for multiple
biomarker scores to enhance risk prediction, and previous biomarker scores have not been directly compared with measures
of subclinical atherosclerosis.41,42 However, Rana et al28 from
EISNER recently compared CAC with a multiple biomarker
score for the ability to further enhance CVD risk prediction.
The multiple biomarker score was associated with a negligible
nonsignificant improvement in risk prediction, whereas the
addition of CAC was associated with a substantial improvement in risk discrimination and reclassification as noted by
a net reclassification improvement of 35% and an improvement in C-statistic from 0.73 to 0.84 (Table 2).28 Notably, the
addition of CAC to the FRS plus biomarker score resulted in
improved risk discrimination, whereas the addition of biomarkers to FRS plus CAC had no effect on risk discrimination.
404 Circ Cardiovasc Imaging March 2014
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Figure 4. Distribution of coronary artery calcium (CAC), high-sensitivity C-reactive protein (hsCRP), and hard coronary heart disease
(CHD) events in intermediate-risk participants from MESA. Although CAC is evenly distributed in intermediate-risk individuals in MESA,
the majority of hard CHD events occur in those with elevated CAC. This provides a rationale for directing primary prevention therapy at
selected individuals in whom the majority of events occur. In contrast, the majority of events occurred in individuals with low hsCRP,
arguing against selected treatment based on hsCRP. Using the relative event reduction (35%) associated with generic statins, one can
see that the 5-year number needed to treat (NNT) is favorable when CAC is high and unfavorable when CAC is low, whereas hsCRP does
not provide strong rationale for targeting particular groups.
Limitations of Subclinical
Atherosclerosis Testing
What are the barriers to more widespread use of subclinical
atherosclerosis testing? Several potential roadblocks must be
considered, including those specific to CAC (No. 1 and No. 2)
and those applicable to all future tests (No. 3 and No. 4).
Table 2. Change in C-Statistic and NRI in the Rotterdam and
EISNER Studies
Study
Change in C-Statistic*
NRI*
0.00–0.02
0.4%–7.6%
0.05
19.3%
Rotterdam
26
Biomarkers
CAC
EISNER
28
Biomarker panel
0.02
4.0%
CAC
0.11
35.0%
CAC indicates coronary artery calcium; EISNER, Early Identification of
Subclinical Atherosclerosis by Noninvasive Imaging Research; and NRI, net
reclassification improvement.
*Added to the Framingham Risk Score.
1.Exposure to radiation: The radiation exposure for a
CAC scan is roughly 1 mSv, similar to that of a bilateral mammogram, and equivalent to ≈120 extra days of
background environmental radiation exposure. The potential benefits of preventing CHD morbidity and mortality, and deferring costly medical care when CAC=0,
must be weighed against the potential for a small increase in lifetime cancer risk. Given the acceptance of
frequent mammography for a disease responsible for
a fraction of the total mortality attributable to CHD,
selective CAC testing would seem to pose an acceptable risk.
2.Effect of incidental findings: Incidental findings with
CAC are common, although the effect of these is less
clear. MacHaalany et al43 noted that among ≈1000 individuals who underwent CAC testing, 42% had incidental
findings of some sort, whereas just 1.2% had clinically
significant findings.43 The value of pursuing most findings is unclear because there is no difference in noncardiac death (including cancer death) between individuals
with and without incidental findings. Furthermore, research on the necessity of reporting all incidental findings is needed.
Blaha et al Biomarkers Are Not Enough for Risk Stratification 405
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3.Increased cost of downstream medical testing: The use
of any new test may potentially lead to increased downstream healthcare use, so-called layered testing. This is a
legitimate concern with all novel serum biomarkers and
new imaging tests. Importantly, the EISNER randomized control trial demonstrated that there is no difference in the rate of downstream testing, procedures, or
resource use between individuals who did and did not
undergo CAC testing.44 In fact, resources seem to be directed selectively at those at highest risk, which is the
goal of primary prevention. For example, among individuals with CAC=0, there was lower downstream medical spending than those randomized to no scan. More
research is clearly needed in this area.
4.Lack of evidence for improved outcomes: It is critical to note that no clinical trial has shown improved
outcomes solely as a result of enhanced risk prediction. This applies not only to traditional risk scores but
to new iterations such as the Reynolds Risk Score as
well as serum biomarkers and atherosclerosis imaging
tests. There must be a mandate across all risk prediction strategies for true biomarker-driven study designs.
However, obtaining funding for these studies has proven difficult.
Atherosclerosis Imaging—Challenging
Existing Risk and Treatment Paradigms
New risk markers must sufficiently move the risk needle to
be expected to have any effect on clinical management of
patients. For serum biomarkers, the consistent lack of effect
on risk discrimination and reclassification, either individually
or combined as a score, limits their value in helping guide
treatment decisions for primary prevention. On the contrary,
measurement of subclinical atherosclerosis by CAC testing
seems to consistently lead to marked improvements in risk
discrimination and reclassification. At the present time, only
subclinical atherosclerosis imaging with CAC has the potential to truly tailor primary preventive strategies to individual
patients based on personalized risk assessment, and these
technologies are still rapidly improving.
There are tremendous public health implications in our
choice of risk assessment strategies. No interventions in
primary prevention are free of cost or risk. Commonly, our
decision to treat with an aspirin or a statin is a lifelong commitment with absolutely no guidelines for guiding the safe
withdrawal of such a risk-reducing therapy. As a result, the
bar for instituting treatment in primary prevention must be set
high. From a public health perspective, stakeholders will note
that atherosclerosis imaging may help both those in whom
the risk–benefit equation is favorable and also those in whom
there be possibly a net harm of therapy (eg, CAC=0).
To illustrate this point, we use the controversial example
of aspirin therapy for primary prevention in type 2 diabetes
mellitus. Particularly in this patient population, aspirin is
associated with definite harm (bleeding) and only marginal
benefit, with absolute benefit relying heavily on underlying absolute risk.45 Contrary to the current dogma that all
patients with diabetes mellitus are high risk, data from
MESA suggest a heterogeneous distribution of CAC and
Figure 5. Diabetes mellitus—a coronary heart disease (CHD) risk equivalent or risk nonequivalent? Although diabetes mellitus is
commonly considered a CHD risk equivalent, the risk is, in fact, heterogenous and is driven by the burden of subclinical atherosclerosis.
CAC indicates coronary artery calcium. Adapted with permission from Malik et al.46 Authorization for this adaptation has been obtained
from both the owner of the copyright in the original work and the owner of copyright in the translation or adaptation.
406 Circ Cardiovasc Imaging March 2014
hard CHD events (Figure 5).46 The majority of hard CHD
events occur in the small portion of people with elevated
CAC >100. Focusing aspirin treatment on those individuals
with a high burden of CAC might optimize the NNT while
minimizing the exposure of lower-risk individuals to potential harm and little anticipated benefit.47 In fact, a recent
analysis showed that among patients with diabetes mellitus
at intermediate risk in whom guidelines state aspirin might
be considered, CAC seems to identify those most likely to
benefit.48 The use of CAC for further risk stratification in
asymptomatic adults with diabetes mellitus ≥40 years of
age is currently supported by national guidelines (class IIa
recommendation).49
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Summary
Although risk factors have proven to be useful therapeutic targets, they are poor predictors of risk. Traditional risk scores
are moderately successful in predicting future CHD events and
can be a starting place for general risk categorization. However, there is substantial heterogeneity between traditional risk
and actual atherosclerosis burden, with event rates predominantly driven by burden of atherosclerosis. Serum biomarkers
have yet to show any clinically significant incremental value
to the FRS and even when combined cannot match the predictive value of atherosclerosis imaging.
As clinicians, are we willing to base therapy decisions
on risk models that lack optimum-achievable accuracy and
limit personalization? The decision to treat a patient in primary prevention must be a careful one because the benefit of
therapy in an asymptomatic patient must clearly outweigh
the potential risk. CAC, in particular, provides a personalized assessment of risk and may identify patients who will
be expected to derive the most, and the least, net absolute
benefit from treatment. Emerging evidence hints that CAC
may also promote long-term adherence to aspirin, exercise,
diet, and statin therapy.50 When potentially lifelong treatment decisions are on the line, clinicians must arm their
patients with the most accurate risk prediction tools, and
subclinical atherosclerosis testing with CAC is, at the present time, superior to any combination of risk factors and
serum biomarkers.
Disclosures
Dr Budoff has received a research grant from GE Healthcare. The
other authors report no conflicts.
References
1.Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H,
Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–1847.
2. Cook NR, Paynter NP, Eaton CB, Manson JE, Martin LW, Robinson
JG, Rossouw JE, Wassertheil-Smoller S, Ridker PM. Comparison of the
Framingham and Reynolds Risk scores for global cardiovascular risk
prediction in the multiethnic Women’s Health Initiative. Circulation.
2012;125:1748–1756, S1.
3. Akosah KO, Schaper A, Cogbill C, Schoenfeld P. Preventing myocardial infarction in the young adult in the first place: how do the National
Cholesterol Education Panel III guidelines perform? J Am Coll Cardiol.
2003;41:1475–1479.
4. Cooney MT, Dudina A, Whincup P, Capewell S, Menotti A, Jousilahti P,
Njølstad I, Oganov R, Thomsen T, Tverdal A, Wedel H, Wilhelmsen L,
Graham I; SCORE Investigators. Re-evaluating the Rose approach: comparative benefits of the population and high-risk preventive strategies.
Eur J Cardiovasc Prev Rehabil. 2009;16:541–549.
5. Linsel-Nitschke P, Götz A, Erdmann J, Braenne I, Braund P, Hengstenberg
C, Stark K, Fischer M, Schreiber S, El Mokhtari NE, Schaefer A,
Schrezenmeir J, Schrezenmeier J, Rubin D, Hinney A, Reinehr T, Roth
C, Ortlepp J, Hanrath P, Hall AS, Mangino M, Lieb W, Lamina C, Heid
IM, Doering A, Gieger C, Peters A, Meitinger T, Wichmann HE, König
IR, Ziegler A, Kronenberg F, Samani NJ, Schunkert H; Wellcome Trust
Case Control Consortium (WTCCC); Cardiogenics Consortium. Lifelong
reduction of LDL-cholesterol related to a common variant in the LDLreceptor gene decreases the risk of coronary artery disease—a Mendelian
Randomisation study. PLoS One. 2008;3:e2986.
6. Ference BA, Yoo W, Alesh I, Mahajan N, Mirowska KK, Mewada A, Kahn
J, Afonso L, Williams KA Sr, Flack JM. Effect of long-term exposure to
lower low-density lipoprotein cholesterol beginning early in life on the
risk of coronary heart disease: a Mendelian randomization analysis. J Am
Coll Cardiol. 2012;60:2631–2639.
7. Juonala M, Magnussen CG, Berenson GS, Venn A, Burns TL, Sabin MA,
Srinivasan SR, Daniels SR, Davis PH, Chen W, Sun C, Cheung M, Viikari
JS, Dwyer T, Raitakari OT. Childhood adiposity, adult adiposity, and cardiovascular risk factors. N Engl J Med. 2011;365:1876–1885.
8. Magnussen CG, Koskinen J, Chen W, Thomson R, Schmidt MD, Srinivasan
SR, Kivimäki M, Mattsson N, Kähönen M, Laitinen T, Taittonen L,
Rönnemaa T, Viikari JS, Berenson GS, Juonala M, Raitakari OT. Pediatric
metabolic syndrome predicts adulthood metabolic syndrome, subclinical
atherosclerosis, and type 2 diabetes mellitus but is no better than body
mass index alone: the Bogalusa Heart Study and the Cardiovascular Risk
in Young Finns Study. Circulation. 2010;122:1604–1611.
9.Berenson GS, Srinivasan SR, Bao W, Newman WP III, Tracy RE,
Wattigney WA. Association between multiple cardiovascular risk factors
and atherosclerosis in children and young adults. The Bogalusa Heart
Study. N Engl J Med. 1998;338:1650–1656.
10. Webber BJ, Seguin PG, Burnett DG, Clark LL, Otto JL. Prevalence of
and risk factors for autopsy-determined atherosclerosis among US service
members, 2001-2011. JAMA. 2012;308:2577–2583.
11. Smith EN, Chen W, Kähönen M, Kettunen J, Lehtimäki T, Peltonen L,
Raitakari OT, Salem RM, Schork NJ, Shaw M, Srinivasan SR, Topol EJ,
Viikari JS, Berenson GS, Murray SS. Longitudinal genome-wide association of cardiovascular disease risk factors in the Bogalusa heart study.
PLoS Genet. 2010;6:e1001094.
12.Rothwell PM. Limitations of the usual blood-pressure hypothesis and
importance of variability, instability, and episodic hypertension. Lancet.
2010;375:938–948.
13.Pencina MJ, D’Agostino RB Sr, Larson MG, Massaro JM, Vasan RS.
Predicting the 30-year risk of cardiovascular disease: the Framingham
Heart Study. Circulation. 2009;119:3078–3084.
14. Berry JD, Dyer A, Cai X, Garside DB, Ning H, Thomas A, Greenland P,
Van Horn L, Tracy RP, Lloyd-Jones DM. Lifetime risks of cardiovascular
disease. N Engl J Med. 2012;366:321–329.
15.Melander O, Newton-Cheh C, Almgren P, Hedblad B, Berglund G,
Engström G, Persson M, Smith JG, Magnusson M, Christensson A, Struck
J, Morgenthaler NG, Bergmann A, Pencina MJ, Wang TJ. Novel and conventional biomarkers for prediction of incident cardiovascular events in
the community. JAMA. 2009;302:49–57.
16.Emerging Risk Factors Collaboration. C-reactive protein, fibrinogen, and
cardiovascular disease prediction. N Engl J Med. 2012;367:1310–1320.
17.Erbel R, Budoff M. Improvement of cardiovascular risk prediction using
coronary imaging: subclinical atherosclerosis: the memory of lifetime risk
factor exposure. Eur Heart J. 2012;33:1201–1213.
18.Tota-Maharaj R, Blaha MJ, Blankstein R, Shaw LJ, Blumenthal RS,
Budoff MJ, Nasir K. Coronary artery calcium predicts coronary heart
disease events even at the extremes of age: the Multi-Ethnic Study of
Atherosclerosis. Circulation. 2011;124:A9868.
19.Silverman MG, Blaha MJ, Budoff MJ, Blankstein R, Sibley CT,
Blumenthal RS, Nasir K. Impact of coronary artery calcium on coronary
heart disease events in individuals at the extremes of traditional risk factor
Blaha et al Biomarkers Are Not Enough for Risk Stratification 407
Downloaded from http://circimaging.ahajournals.org/ by guest on June 18, 2017
burden: the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation.
2011;124:A14517.
20.Nasir K, Rubin J, Blaha MJ, Shaw LJ, Blankstein R, Rivera JJ, Khan
AN, Berman D, Raggi P, Callister T, Rumberger JA, Min J, Jones SR,
Blumenthal RS, Budoff MJ. Interplay of coronary artery calcification and
traditional risk factors for the prediction of all-cause mortality in asymptomatic individuals. Circ Cardiovasc Imaging. 2012;5:467–473.
21.Tota-Maharaj R, Blaha MJ, McEvoy JW, Blumenthal RS, Muse ED,
Budoff MJ, Shaw LJ, Berman DS, Rana JS, Rumberger J, Callister T,
Rivera J, Agatston A, Nasir K. Coronary artery calcium for the prediction
of mortality in young adults <45 years old and elderly adults >75 years
old. Eur Heart J. 2012;33:2955–2962.
22.Okwuosa TM, Greenland P, Ning H, Liu K, Bild DE, Burke GL, Eng
J, Lloyd-Jones DM. Distribution of coronary artery calcium scores by
Framingham 10-year risk strata in the MESA (Multi-Ethnic Study of
Atherosclerosis) potential implications for coronary risk assessment. J Am
Coll Cardiol. 2011;57:1838–1845.
23.Okwuosa TM, Greenland P, Ning H, Liu K, Lloyd-Jones DM. Yield of
screening for coronary artery calcium in early middle-age adults based
on the 10-year Framingham Risk Score: the CARDIA study. JACC
Cardiovasc Imaging. 2012;5:923–930.
24.Lakoski SG, Greenland P, Wong ND, Schreiner PJ, Herrington DM,
Kronmal RA, Liu K, Blumenthal RS. Coronary artery calcium scores and
risk for cardiovascular events in women classified as “low risk” based
on Framingham risk score: the Multi-Ethnic Study of Atherosclerosis
(MESA). Arch Intern Med. 2007;167:2437–2442.
25. Martin SS, Blaha MJ, Blankstein R, Agatston A, Rivera JJ, Virani SS,
Ouyang P, Jones SR, Blumenthal RS, Budoff MJ, Nasir K. Dyslipidemia,
coronary artery calcium, and incident atherosclerotic cardiovascular
disease: implications for statin therapy from the Multi-Ethnic Study of
Atherosclerosis. Circulation. 2014;129:77–86.
26.Kavousi M, Elias-Smale S, Rutten JH, Leening MJ, Vliegenthart R,
Verwoert GC, Krestin GP, Oudkerk M, de Maat MP, Leebeek FW,
Mattace-Raso FU, Lindemans J, Hofman A, Steyerberg EW, van der Lugt
A, van den Meiracker AH, Witteman JC. Evaluation of newer risk markers
for coronary heart disease risk classification: a cohort study. Ann Intern
Med. 2012;156:438–444.
27.Möhlenkamp S, Lehmann N, Moebus S, Schmermund A, Dragano N,
Stang A, Siegrist J, Mann K, Jöckel KH, Erbel R; Heinz Nixdorf Recall
Study Investigators. Quantification of coronary atherosclerosis and inflammation to predict coronary events and all-cause mortality. J Am Coll
Cardiol. 2011;57:1455–1464.
28. Rana JS, Gransar H, Wong ND, Shaw L, Pencina M, Nasir K, Rozanski
A, Hayes SW, Thomson LE, Friedman JD, Min JK, Berman DS.
Comparative value of coronary artery calcium and multiple blood biomarkers for prognostication of cardiovascular events. Am J Cardiol.
2012;109:1449–1453.
29. Detrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, Liu K,
Shea S, Szklo M, Bluemke DA, O’Leary DH, Tracy R, Watson K, Wong
ND, Kronmal RA. Coronary calcium as a predictor of coronary events in
four racial or ethnic groups. N Engl J Med. 2008;358:1336–1345.
30. Blaha MJ, Blumenthal RS, Budoff MJ, Nasir K. Understanding the utility
of zero coronary calcium as a prognostic test: a Bayesian approach. Circ
Cardiovasc Qual Outcomes. 2011;4:253–256.
31. Sarwar A, Shaw LJ, Shapiro MD, Blankstein R, Hoffmann U, Hoffman
U, Cury RC, Abbara S, Brady TJ, Budoff MJ, Blumenthal RS, Nasir K.
Diagnostic and prognostic value of absence of coronary artery calcification. JACC Cardiovasc Imaging. 2009;2:675–688.
32.Blaha M, Budoff MJ, Shaw LJ, Khosa F, Rumberger JA, Berman D,
Callister T, Raggi P, Blumenthal RS, Nasir K. Absence of coronary artery calcification and all-cause mortality. JACC Cardiovasc Imaging.
2009;2:692–700.
33. Budoff MJ, McClelland RL, Nasir K, Greenland P, Kronmal RA, Kondos
GT, Shea S, Lima JA, Blumenthal RS. Cardiovascular events with absent or
minimal coronary calcification: the Multi-Ethnic Study of Atherosclerosis
(MESA). Am Heart J. 2009;158:554–561.
34.Erbel R, Möhlenkamp S, Moebus S, Schmermund A, Lehmann N, Stang
A, Dragano N, Grönemeyer D, Seibel R, Kälsch H, Bröcker-Preuss M,
Mann K, Siegrist J, Jöckel KH; Heinz Nixdorf Recall Study Investigative
Group. Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary
atherosclerosis: the Heinz Nixdorf Recall study. J Am Coll Cardiol.
2010;56:1397–1406.
35. Blaha MJ, Budoff MJ, DeFilippis AP, Blankstein R, Rivera JJ, Agatston
A, O’Leary DH, Lima J, Blumenthal RS, Nasir K. Associations between
C-reactive protein, coronary artery calcium, and cardiovascular events:
implications for the JUPITER population from MESA, a population-based
cohort study. Lancet. 2011;378:684–692.
36.Möhlenkamp S, Lehmann N, Moebus S, Schmermund A, Dragano N,
Stang A, Siegrist J, Mann K, Jöckel KH, Erbel R; Heinz Nixdorf Recall
Study Investigators. Quantification of coronary atherosclerosis and inflammation to predict coronary events and all-cause mortality. J Am Coll
Cardiol. 2011;57:1455–1464.
37. Ridker PM, MacFadyen J, Libby P, Glynn RJ. Relation of baseline highsensitivity C-reactive protein level to cardiovascular outcomes with rosuvastatin in the Justification for Use of statins in Prevention: an Intervention Trial
Evaluating Rosuvastatin (JUPITER). Am J Cardiol. 2010;106:204–209.
38. Heart Protection Study Collaborative Group. C-reactive protein concentration and the vascular benefits of statin therapy: an analysis of 20 536
patients in the Heart Protection Study. Lancet. 2011;377:469–476.
39. Arad Y, Spadaro LA, Roth M, Newstein D, Guerci AD. Treatment of asymptomatic adults with elevated coronary calcium scores with atorvastatin, vitamin C, and vitamin E: the St. Francis Heart Study randomized
clinical trial. J Am Coll Cardiol. 2005;46:166–172.
40. Yeboah J, McClelland RL, Polonsky TS, Burke GL, Sibley CT, O’Leary
D, Carr JJ, Goff DC, Greenland P, Herrington DM. Comparison of novel risk markers for improvement in cardiovascular risk assessment in
intermediate-­risk individuals. JAMA. 2012;308:788–795.
41.Wang TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Cheh C,
Jacques PF, Rifai N, Selhub J, Robins SJ, Benjamin EJ, D’Agostino RB,
Vasan RS. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006;355:2631–2639.
42. Zethelius B, Berglund L, Sundström J, Ingelsson E, Basu S, Larsson A,
Venge P, Arnlöv J. Use of multiple biomarkers to improve the prediction
of death from cardiovascular causes. N Engl J Med. 2008;358:2107–2116.
43. Machaalany J, Yam Y, Ruddy TD, Abraham A, Chen L, Beanlands RS,
Chow BJ. Potential clinical and economic consequences of noncardiac
incidental findings on cardiac computed tomography. J Am Coll Cardiol.
2009;54:1533–1541.
44. Rozanski A, Gransar H, Shaw LJ, Kim J, Miranda-Peats L, Wong ND,
Rana JS, Orakzai R, Hayes SW, Friedman JD, Thomson LE, Polk D, Min J,
Budoff MJ, Berman DS. Impact of coronary artery calcium scanning on coronary risk factors and downstream testing the EISNER (Early Identification
of Subclinical Atherosclerosis by Noninvasive Imaging Research) prospective randomized trial. J Am Coll Cardiol. 2011;57:1622–1632.
45. Pignone M, Alberts MJ, Colwell JA, Cushman M, Inzucchi SE, Mukherjee
D, Rosenson RS, Williams CD, Wilson PW, Kirkman MS. Aspirin for
primary prevention of cardiovascular events in people with diabetes: a
position statement of the American Diabetes Association, a scientific statement of the American Heart Association, and an expert consensus document of the American College of Cardiology Foundation. Circulation.
2010;121:2694–2701.
46.Malik S, Budoff MJ, Katz R, Blumenthal RS, Bertoni AG, Nasir K,
Szklo M, Barr RG, Wong ND. Impact of subclinical atherosclerosis on
cardiovascular disease events in individuals with metabolic syndrome
and diabetes: the multi-ethnic study of atherosclerosis. Diabetes Care.
2011;34:2285–2290.
47.Silverman MG, Blaha MJ, Budoff MJ, Blankstein R, Blumenthal RS,
Krumholz HM, Rivera JJ, Agatston A, Wong ND, Shea S, McEvoy J,
Nasir K. Coronary artery calcium and cardiovascular events in diabetes:
implications for primary prevention therapies: the Multi-Ethnic Study of
Atherosclerosis (MESA). Circulation. 2012;126:A13133.
48.Silverman MG, Blaha MJ, Budoff MJ, Rivera JJ, Raggi P, Shaw LJ,
Berman D, Callister T, Rumberger JA, Rana JS, Blumenthal RS, Nasir K.
Potential implications of coronary artery calcium testing for guiding aspirin use among asymptomatic individuals with diabetes. Diabetes Care.
2012;35:624–626.
49.Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA,
Foster E, Hlatky MA, Hodgson JM, Kushner FG, Lauer MS, Shaw LJ, Smith
SC Jr, Taylor AJ, Weintraub WS, Wenger NK, Jacobs AK, Smith SC Jr,
Anderson JL, Albert N, Buller CE, Creager MA, Ettinger SM, Guyton RA,
Halperin JL, Hochman JS, Kushner FG, Nishimura R, Ohman EM, Page RL,
408 Circ Cardiovasc Imaging March 2014
Stevenson WG, Tarkington LG, Yancy CW; American College of Cardiology
Foundation; American Heart Association. 2010 ACCF/AHA guideline for
assessment of cardiovascular risk in asymptomatic adults: a report of the
American College of Cardiology Foundation/American Heart Association
Task Force on Practice Guidelines. J Am Coll Cardiol. 2010;56:e50–e103.
50.Nasir K, McClelland RL, Blumenthal RS, Goff DC Jr, Hoffmann U,
Psaty BM, Greenland P, Kronmal RA, Budoff MJ. Coronary artery calcium in relation to initiation and continuation of cardiovascular preventive medications: the Multi-Ethnic Study of Atherosclerosis (MESA). Circ
Cardiovasc Qual Outcomes. 2010;3:228–235.
Response to Blaha et al
Charlotte Andersson, MD, PhD; Ramachandran S. Vasan, MD
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Dr Blaha et al emphasize that a zero coronary artery calcium (CAC) score identifies individuals with a high risk factor burden
who do not develop CVD, citing studies (comparing individuals aged >75 years with those aged <45 years) that are limited
by referral bias. Other population-based studies indicate that individuals with a high Framingham risk score but CAC score
of 0 have a greater risk of CVD than individuals with a low Framingham risk score but a high CAC score. Although CAC
scoring improves risk reclassification, there is no evidence that such reclassification reduces morbidity/mortality or lowers
healthcare expenditure. Overall, it may be unethical to ignore elevated risk factors in patients with a 0 CAC score because of
potential atherosclerotic and nonatherosclerotic (target organ damage) sequel in the absence of long-term data demonstrating lack of adverse outcomes in this group. The specificity of the CAC score for obstructive coronary disease is limited,
and it does not correlate with vulnerable plaque burden. The typical thin-capped fibroatheroma with an inflamed cap and
a necrotic lipid-rich core that antedates acute coronary syndrome is not heavily calcified; it shows micro- or spotty calcification that is beyond the resolution capability of current scanners. In contrast, heavily calcified plaques are often stable.
Furthermore, the calcification extent in vulnerable plaques does not increase with age, whereas the CAC score does. Finally,
both CAC scoring and biomarkers yield complementary information on CVD risk because acute coronary syndrome arises
from the conjoint interactions of local plaque vulnerability with humoral factors, and the latter (unlike CAC) can be targeted
pharmacologically.
Is There a Role for Coronary Artery Calcium Scoring for Management of Asymptomatic
Patients at Risk for Coronary Artery Disease?: Clinical Risk Scores Are Not Sufficient To
Define Primary Prevention Treatment Strategies Among Asymptomatic Patients
Michael J. Blaha, Michael G. Silverman and Matthew J. Budoff
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Circ Cardiovasc Imaging. 2014;7:398-408
doi: 10.1161/CIRCIMAGING.113.000341
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