This article provides an assessment of the downstream impact of coronary artery calcium scanning on the subsequent treatment patterns of non—high-risk patients.
To assess if coronary artery calcium (CAC) scans influence treatment
patterns as reflected by subsequent rates of cardiac imaging
and therapeutic interventions, and their effect on ischemic
Longitudinal observational study from January 1, 2005, through
August 31, 2011, using a large managed-care medical and pharmacy
Two cohorts were evaluated: CAC patients who received CAC
testing, and Reference patients, subject to preauthorization, who
were denied CAC scans. Patients were adults less than 65 years
old. Index date was CAC scan date for CAC and pre-authorization
request date for Reference. Patients were stratified into high-risk
and non—high-risk categories; outcomes were analyzed only for
non—high-risk where CAC scores could potentially modify risk
classification. Cardiac imaging, coronary revascularizations, and
pharmaceutical interventions were evaluated for 6 months post
index and adverse ischemic events were assessed using all available
The study included 2679 CAC and 1135 Reference patients.
Among non—high-risk patients, similar proportions of both
groups received an imaging test within 6 months (23.2% vs
= .5); revascularization rates and pharmaceutical
utilization were similar. Adverse events were rare. Age-sex
adjusted incidence rate ratio for adverse events was 1.1 (95%
CI, 0.36-3.38) among CAC relative to Reference. High-risk patients,
considered inappropriate for CAC testing, represented 20.2% and
23.5% of CAC and Reference, respectively (
Patients having CAC scans were not associated with fewer downstream
ischemic events nor with reduced subsequent imaging
and therapeutic interventions among non—high-risk patients.
Results also indicated inappropriate testing of high-risk patients.
Am J Manag Care. 2014;20(8):e330-e339
An estimated 33% of the adults in the United States
are affected by coronary artery disease (CAD),1,2
and along with such high prevalence have come
substantial and increasing rates of morbidity.1 While
relative mortality rates attributable to cardiovascular
disease (CVD) declined 33% in the United States from
1999 to 2009, disease burden remained high. CVD was
associated with 1 of every 3 United States deaths in 2009,
32% of the 2.4 million overall. Of 2009 deaths, coronary
heart disease alone caused approximately 1 in every 6, and
stroke 1 in every 19. The total direct and indirect cost
of CVD and stroke was estimated at $312.6 billion in
2009.3 These sizable and growing burdens have driven
efforts to evaluate and better understand cardiac risks in
asymptomatic patient populations.4-11
Coronary artery calcium (CAC) scanning, a screening
tool that detects subclinical coronary disease in asymptomatic
populations, is noninvasive; it can be performed in a few
minutes while the patient is fully dressed.4-9,12-14 A number of
older studies suggest that CAC scores are directly correlated
with coronary atherosclerosis, and may represent a marker
for plaque burden.15-19 CAC identifies with calcified plaque,20
and CAC score is considered a predictor of coronary death
and nonfatal myocardial infarction (MI).21 Furthermore,
some studies have reported high sensitivity (true positive
for the presence of coronary artery disease), greater accuracy
and reproducibility when CAC is measured with coronary
computed tomography (CT).15,18,19 CAC utilizes radiation,
and accumulating evidence about radiation exposure and
cancer risk remains a concern, especially in women and middle-
aged and younger persons.20,22-24 However, results from recent
studies suggest that advanced scanning tools may reduce
effective radiation doses.25,26
The primary policy concern regarding CAC scanning
is whether it provides—on its own, and/or when added to
existing tests—a better assessment of future risk of cardiac
events than do scoring methods such as the Framingham
Risk Score (FRS)14,27 and the National
Cholesterol Education Panel (NCEP)
Adult Treatment Panel (ATP) III
guidelines,28 and whether it leads to
improved outcomes for patients.
Anand et al reported, after following
510 asymptomatic patients for an
average of slightly more than 2 years,
that CAC scores were a better predictor
of ischemic events and related
short-term cardiovascular outcomes
than established measures of cardiovascular
risk factors such as the FRS.29
Similarly, based on data from the multi-center prospective
longitudinal trial, Multi-Ethnic Study of Atherosclerosis
(MESA), Detrano et al concluded that CAC scores
yielded better predictive information relative to the FRS.12
Using data from the MESA trial, Polonsky et al added
CAC scores to FRS risk factors to examine the prediction
of incident CHD (including soft events such as revascularizations).
The authors concluded that the addition of
CAC scores produced significant net reclassification improvement,
an indicator of the amount of adjustments between
risk categories.30 Similar reclassification rates were
observed in a study by Erbel et al that added CAC scores
to both the FRS and the NCEP ATP III scores when predicting
hard events (ie, nonfatal myocardial infarction
and coronary death).31
CAC testing, however, may not be useful for everyone.
For high-risk patients, CAC scoring does not produce any
improvement in event prediction, management, or outcomes.
5 The 2010 American College of Cardiology Foundation/
American Heart Association (ACCF/AHA)
Task Force on Practice Guidelines indicated that CAC
measurements were reasonable for the assessment of cardiovascular
risk among asymptomatic adults at intermediate
risk, defined as 10%to 20% risk of a cardiac event
within 10 years.6,12,14 The guidelines also indicated that the
measurement of CAC may be appropriate for patients at
low to intermediate risk, defined at 6% to 10% risk of a cardiac
event within 10 years.6,14,32 The guidelines indicated,
however, that CAC was not appropriate for individuals
at low (6% or lower) risk of experiencing a cardiac event
within 10 years.4,6,12,14
While CAC scans may potentially influence the reclassification
of risk for certain subgroups of asymptomatic
patients, the impact of CAC testing on treatment as evidenced
by subsequent imaging, therapeutic interventions,
and ischemic events in the real-world setting is still largely
unknown.33 There is some evidence that CAC scanning
may improve cardiac risk management,34 but in a statement
to AHA health professionals, Wexler et al pointed
out that the number of people with coronary calcium
could be 10 to 100 times greater than those who will ever
get heart disease, and that CAC may not always be actionable,
especially in those with no known risk factors.35
Furthermore, evidence is lacking as to whether screening
asymptomatic adults impacts morbidity or mortality from
The objective of this study was to explore if CAC testing
resulted in any downstream modifications in treatment
patterns and cardiac outcomes of non—high-risk
patients whose physicians ordered CAC scans and either
1) received them (the CAC group), or 2) were denied them
for insurance reasons, citing noncovered services (the
Reference group, or References).
Data Source and Study Design
This longitudinal observational study utilized medical
and pharmacy claims within the HealthCore Integrated
Research Database (HIRD) to identify patients who received
a CAC procedure between January 1, 2005, and
August 31, 2011. Patient records maintained by AIM
Specialty Health, a specialty services company that manages
radiology benefits, were used to identify patients who
were denied the procedure during that same time frame.
The HIRD represents a clinically rich and geographically
diverse repository of longitudinal claims data for 45 million
lives covered by about 14 health insurance plans in
the Northeast, Midwest, South, and West regions of the
United States. Researchers had access only to a limited
data set, and the acquisition and handling of patient data
complied with all applicable state and federal privacy regulations
including the Health Insurance Portability and
Accountability Act. No IRB approval was required for
this nonexperimental study, which utilized data containing
no personal patient identifiers.
Two population groupings were included in the study.
The CAC group consisted of patients not requiring preauthorization
for a CAC scan (Current Procedural Terminology
[CPT] codes 0144T or 75571) and receiving the
procedure in an outpatient setting between January 1,
2006, and April 30, 2011. The index date for CAC patients
was defined as the date of the CAC scan. The Reference
group comprised patients whose physicians requested a
CAC scan but were denied in the pre-authorization process,
administered by AIM Specialty Health, because the
procedure was not covered in the patients’ health plan
benefits. Reference patients did not have a CPT code for
a CAC scan during the study period, and were identified
via pre-authorization records maintained by AIM Specialty
Health. Given the limited clinical information on
cardiovascular risks in claims data, the selected reference
population was the best comparator group to the CAC
group. Physicians recommended CAC testing for patients
in both groups. The patients in 1 group were denied the
procedure because of restrictions in their health plan coverage,
not because of their clinical characteristics. The
index date for Reference patients was defined as the prior
approval request date for the CAC procedure.
Both CAC and Reference patients were further subcategorized
into high-risk and non—high-risk groups on the
basis of pre existing comorbidities identified within their
medical claims. Patients were classified as high risk if they
had at least 1 diagnosis of diabetes, myocardial infarction
(MI), angina pectoris, ischemic heart disease other
than MI and angina, peripheral artery disease, thrombotic
stroke/transient ischemic attack, congestive heart
failure, or cerebrovascular disease during the 12 months
prior to the index date. This risk classification methodology
was based on conditions included in the NCEP ATP
III guidelines,28 plus heart failure, an approach that was
described in detail in prior studies.36,37
For inclusion in the analysis, both CAC and Reference
patients were required to be between the ages of 18
and 64 years at the index date. To evaluate pre existing
cardiac risk, all patients were required to have at least 12
months of continuous health plan eligibility prior to the
index date. In addition, at least 6 months of continuous
health plan eligibility post index was required to perform
the analysis of downstream utilization. No minimum post
index continuous eligibility was required for the assess
ment of adverse ischemic events. All patients identified as
high risk were excluded from downstream utilization and
outcome measures, as CAC testing would not be considered
“screening” in these patients.
Downstream Utilization and Outcome Measures
Downstream utilization and outcomes were analyzed
only for patients in the non—high-risk group because prior
evidence suggested that the results of CAC tests could potentially
influence a reclassification of the risk and, therefore,
treatment pathway of patients may be altered only in
the non—high-risk group.5,6 Among the measures of interest
for the non—high-risk patients were downstream cardiac
imaging tests, coronary revascularizations, and pharmaceutical
interventions in the 6 months following the index
date. Cardiac imaging tests of interest included stress
echocardiography, myocardial nuclear imaging, cardiac
magnetic resonance imaging, diagnostic cardiac catheterization,
cardiac positron emission tomography, and coronary
CT angiography. The revascularizations evaluated included
coronary artery bypass surgery (CABG) and percutaneous
coronary intervention (PCI). All tests and interventions
were identified via CPT and
International Classification of
Disease, Ninth Revision, Clinical Modification
procedure codes in patients’ medical claims. Cardiac pharmaceutical
interventions included angiotensin-convertingenzyme
(ACE) inhibitors or angiotensin receptor blockers
ARBs), beta-blockers, calcium channel blockers, diuretics,
nitrates, and statins identified from National Drug Code/
General Product Identifier (NDC/GPI) coding from pharmacy
Also of interest was the average total cost per month
for cardiac pharmaceutical interventions. This was calculated
as the sum of the total costs (plan-paid plus patient
out-of-pocket costs) for cardiac pharmaceutical interventions
divided by the months of prescription eligibility.
Monthly costs were analyzed as a proxy for medication
Adverse ischemic events of interest, which were identified
diagnostic codes in medical claims for
hospital inpatient stays lasting at least 3 days but no longer
than 180 days, included acute myocardial infarction (410.
x0 and 410.x1), ischemic stroke (433.x1 and 434.x1), and
unstable angina pectoris (411.1x) after the index date.
All comparisons between patients in the CAC and Reference
groups were conducted with
or Fisher exact tests
for categorical variables and with 2-sample
tests for continuous
demographic variables. Wilcoxon ranked-sum test
was applied for cost variables. Comparisons of ischemic
adverse events during follow-up between the CAC and
Reference groups were conducted with Cox proportionalhazards
models. All statistical analyses were performed with
SAS version 9.2 (SAS Institute Inc, Cary, North Carolina).
A statistical significance level of .05 was utilized.
Demographic Characteristics of Study Population
The total study population (N = 3814) included 2679
patients in the CAC cohort and 1135 in the control (Reference)
group. Patients in the CAC and control groups
were comparable in age (mean 52.7 years) and gender
(40% female) distribution
. Patients in the highrisk
category accounted for 20.2% and 23.5% of the CAC
and Reference groups, respectively
05). The Reference
group had a greater pre existing comorbidity burden
(mean Charlson Comorbidity Index [CCI] score 0.47) vs
the CAC group (mean CCI score 0.40),
the difference between the groups may not be clinically
significant. Approximately three-fourths of the patients
in each cohort had CCI scores of zero, indicating that no
relevant comorbidities were present.
Post Index Interventions
Among the patients categorized as non—high-risk, similar
proportions in both the CAC and Reference groups received
at least 1 subsequent (not CAC) imaging test within 6
months post index (23.2% vs 23.8
= .5). Overall, the proportions
were comparable between the 2 groups, regardless
of the type of imaging test (
). The rates of therapeutic
interventions (revascularizations) were similar between the
groups: CAC (0.3%) and Reference (0.65%) for CABG
.20), and 3.29% and 4.43% for PCI .15), respectively.
Medication utilization was comparable for the 2 groups,
with slightly more than one-third of the patients on statins
.86), and approximately one-fifth receiving ACE inhibitors
or ARBs in each group
.61). Similar monthly mean
(±SD) total costs were associated with the pharmaceutical
therapeutic interventions between 2 groups ($31.50 [±$52.79]
for CAC versus $29.95 [±$49.43] for Reference,
Adverse Cardiac Events
The median follow-up periods were 689 days for CAC
and 501 days for Reference. Adverse events were rare in
both groups during these relatively short follow-up periods
(0.85% in CAC vs 0.79% in Reference). The propor
tions of adverse cardiac events were comparable for CAC
and Reference patients regardless of event type (
). The age-sex adjusted incidence rate ratio for adverse
events was 1.1 (95% CI, 0.36-3.38) among non—high-risk
patients in the CAC versus Reference cohorts (
This study demonstrated no significant difference in
the rates of subsequent cardiac imaging utilization and
therapeutic interventions, nor in the incidence of ischemic
events, between the CAC and Reference cohorts.
Slightly less than one-fourth of the patients in the CAC
and Reference groups received a cardiac imaging test during
the 6-month follow-up period, and the proportions
remained comparable regardless of test type. The 2 groups
had similar rates of revascularizations (PCI and CABG),
as well as similar utilization patterns and costs for drug
interventions, indicating no notable change in treatment
patterns post CAC testing. During the follow-up period,
ischemic adverse events were rare in both the CAC and
The FRS—a multivariate statistical model incorporating
age, gender, smoking status, blood pressure, cholesterol,
and diabetes, among other risk factors—has been used
successfully to evaluate the risk of coronary events among
people without a diagnosis of heart disease.38 Relative to
the FRS, CAC score has been suggested as an approach
that could enhance the prediction of risk in this population.
5,21,38-40 In this study, the association between CAC
scanning and subsequent cardiac imaging, revascularization,
pharmaceutical-based cardiac interventionsm, and
adverse ischemic events was assessed in a real-world setting
among managed care patients. To facilitate comparison,
the study included a substantive control cohort and
confined the analysis to non—high-risk patients, among
whom the impact of CAC measurements had the potential
to be most evident.5,6
The results suggest that CAC testing did not substantially
impact referrals for additional screening, therapeutic
interventions, drug utilization, and costs during
6 months of follow-up. Similarly, CAC testing did not
change the rate of downstream adverse events during a
median follow-up of 689 and 501 days for CAC and Reference
patients, respectively. Another important finding
was that 1 in 5 CAC tests in the CAC group was
performed on high-risk patients, and would be deemed
inappropriate per ACCF/AHA guidelines.5 This is
probably a conservative estimate because not all factors
that could result in a high-risk classification were
necessarily captured with the claims-based identification
criteria used in this study. This could have resulted in
an understatement of the proportion of CAC scans that
Evidence from prior studies has demonstrated that
CAC testing alone or in combination may predict cardiac
events better than available scoring systems such as the
FRS and the NCEP ATP III guidelines.12,29-31 Yet it does
not appear that improved predictive ability influences
clinical decision making. One possible explanation might
be that the marginal improvements associated with CAC
over existing algorithms were not sufficient to persuade
providers to modify treatment patterns for asymptomatic
The study by Polonsky et al showed only a modest
improvement in the predictive accuracy (as measured by
area under the receiver operating characteristic [ROC]
curve) when CAC scores were added to FRS scores.30
The result for FRS score alone was 0.76 (95% CI, 0.72-
0.79), which increased to 0.81 (95% CI, 0.78-0.84)
<.001) following the addition of CAC to scores.30 An
improvement of such modest size might not have clinical
consequences. In addition, most of the reclassifications
were from moderate to low risk, which might also
not carry clinical importance.30 In another study, after
adding CAC scores to FRS and NCEP ATP III scores,
Erbel et al showed that the areas under the ROC curve
improved from 0.681 to 0.749
<.003) and from 0.653 to
= .0001), respectively.31 While this represents better
improvement following the addition of CAC scores
relative to results from the Polonsky et al study, it was
still in the modest range.30,31
The current study relied on secondary (administrative
claims) data, from which it was not possible to access
patients’ actual CAC scores. Based on prior studies12,29-31
with CAC scans, however, it would seem reasonable
to expect the risk status of some of the patients in the
CAC cohort to change based on their CAC scores. The
evidence in this study, however, did not indicate notable
modifications in how patients utilized healthcare resources
nor in how providers managed their patients’ pharmaceutical
interventions and cardiac procedures post scan.
This finding is consistent with the prevailing view that
no beneficial evidence is available on how CAC testing
Nonetheless, earlier studies and AHA scientific statements42
have suggested a role for CAC as an independent
predictor of cardiovascular events. The findings of our
study have implications for the management of treatment
in light of concerted efforts by payers to comprehend how
procedures such as CAC testing may be integrated into
their offerings in an effort to improve health outcomes.43
The results of diagnostic procedures may influence changes
or improvements in treatment patterns. When treatment
patterns remain largely unchanged regardless of test
results, as was observed with CAC scans in this study,
however, questions about the value of the test may have
As is typical of claims-based data analyses, which have
inherent limitations including the lack of clinical indicators
on disease severity, the results of this study should
be interpreted with caution. This consideration could
have directly affected the identification of high-risk patients
(and consequently, non—high-risk patients), which
was accomplished exclusively through the presence of
specific pre existing diagnoses within medical claims. It
is not inconceivable that that some patients may have
been misclassified despite this solid methodological approach—
although we believe that the portion would
likely be miniscule. Furthermore, any misclassification
was more likely in the low-risk population versuss the
high-risk. Part of the reason for this is that if low- and
intermediate-risk patients had cardiac events prior to the
initiation of the health plan coverage captured in our database,
such patients would truly belong in the high-risk
category. Nonetheless, the study findings were unlikely to
be affected because they focused on treatment pattern differences
between these 2 populations.
A broader limitation was the inability to identify and
account for any patients in the control group who were
denied the CAC scan as a noncovered benefit but still received
the test via self-pay or other financial arrangements.
Finally, although the median follow-up durations—689
days for CAC and 501 days for Reference—were substantially
longer than the follow-up periods for the therapeutic
interventions, they may still not represent sufficient
time in which to observe a comprehensive range of adverse
events following the establishment of patients’ risk
status with CAC procedures, as was observed in earlier
studies and suggested by the FRS 10-year mortality risk for
We recognized that short follow-up times would present
a challenge when this study was initiated with administrative
claims as the primary data source. Enrollees
often change health plans as they move from one job to
another, so it was anticipated that within claims, the researchable
follow-up periods could well be of shorter duration.
Still, events and risks within a shorter time frame
for a general managed care population would be an important
addition to the field, and could fill an important
gap because incidences over a shorter follow-up duration
have only been reported for specific populations, such as
patients with diabetes, or breast cancer. As a result, the
findings in this study, while limited to the impact of CAC
scans on treatment patterns in the short term rather than
long term, may address an important gap. We believe that
future studies using longer follow-up periods would contribute
important information on the long-term impact of
CAC scans on subsequent treatment patterns.
Finally, although this study only included working,
commercially insured subjects, differences in socioeconomic
circumstances could have influenced the type
and quality of healthcare insurance they accessed. Such
differences were likely minimized, however, because all
coverage was employer-determined. In this study, the
procedure was not covered by employers who concurred
with the health plan’s position that CAC did not satisfy
its medical necessity criteria. Also included, however,
were patients from self-insured employer groups with independent
medical policies that allowed CAC scans as a
covered benefit. Such extended coverage could sometimes
be reflected by the business and financial circumstances
of employers; neither that nor the socioeconomic status
of individual patients was determinable from our study
data. This could have introduced a bias, although one of
miniscule significance given that all patients were working
and were receiving employer-provided health insurance.
In this study, non—high-risk patients having CAC
scans were not associated with fewer ischemic events nor
reduced use of additional imaging tests. While this study
was designed to evaluate the non—high-risk category in
which the predictive value of CAC was expected to be
greatest, almost no difference was seen in additional testing,
cardiac interventions, drug utilization, and adverse
event rates between the CAC and Reference groups. In
addition, a substantial number of high-risk patients inappropriately
received CAC scans, which provided no additional
predictive value, exposed them unnecessarily to
radiation harm, and increased their healthcare costs. This
will undoubtedly have policy implications for payers and
providers. Additional studies in real-world settings over
longer durations could help to further elucidate how or if
CAC testing modifies treatment patterns post scan.
Bernard B. Tulsi, MSc, provided writing and other editorial support
for this manuscript.
Author Affiliations: Healthcore, Inc, Wilmington, DE (WCC, GS, JB);
WellPoint, Inc, Los Angeles, CA (BK); AIM Specialty Health, Chicago, IL
(TP); and University of California, San Francisco (RR).
Funding Source: This study was internally funded by WellPoint, Inc.
Author Disclosures: WCC, GS, JB, and BBT disclose that they are employees
of HealthCore, a research subsidiary of WellPoint; BK discloses
that he is an employee of WellPoint; TP discloses that he is an employee
of AIM Specialty Health; and RR discloses that she is an employee of
Authorship Information: Concept and design (WC, GS, JB, BK); acquisition
of data (JB); analysis and interpretation of data (WC, GS, JB,
BK, RR, TP); drafting of the manuscript (WC); critical revision of the
manuscript for important intellectual content (WC, GS, JB, BK, RR, TP);
statistical analysis (WC); provision of study materials or patients (JB); obtaining
funding (JB); administrative, technical, or logistic support (GS);
and supervision (JB, RR, JP).
Address correspondence to: Winnie Chia-hsuan Chi, MS, Senior Research
Analyst, HealthCore, Inc, 800 Delaware Ave, 5th Fl, Wilmington,
DE 19801-1366. E-mail: firstname.lastname@example.org.
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