Does CAC Testing Alter Downstream Treatment Patterns for Cardiovascular Disease?

Objectives

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

events downstream.

Study Design

Longitudinal observational study from January 1, 2005, through

August 31, 2011, using a large managed-care medical and pharmacy

claims database.

Methods

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

follow-up time.

Results

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

23.8%, respectively;

P

= .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 (

P

<.05).

Conclusions

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

• This is the first study to compare downstream differences post scan in the rates of additional diagnostic testing, therapeutic interventions, and ischemic events between patients who received coronary artery calcium (CAC) scans, and controls, who were denied CAC scans because their health plans did not cover the procedure, within a large, real-world, managed-care population.
• The findings in this study, consistent with those of prior, largely clinical studies, indicated that there were no significant differences in treatment patterns or ischemic events during the post scan, follow-up period.
• While questions about the value of CAC scans persist, they are still being ordered for asymptomatic patients, raising policy questions that may be resolved by additional studies in larger patient populations and for longer durations.

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).1^5,18,19 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

CAD.

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).

METHODS

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.

Study Population

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

Inclusion/Exclusion Criteria

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

(

ICD-9-CM

)

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

claims.

(

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

utilization rate.

Adverse ischemic events of interest, which were identified

from

ICD-9-CM

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.

Statistical Analysis

All comparisons between patients in the CAC and Reference

groups were conducted with

or Fisher exact tests

for categorical variables and with 2-sample

t

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.

χ²

RESULTS

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

Table 1

. Patients in the highrisk

category accounted for 20.2% and 23.5% of the CAC

and Reference groups, respectively

(P <.

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),

P

<.05), although

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

%, P

= .5). Overall, the proportions

were comparable between the 2 groups, regardless

of the type of imaging test (

Table 2

). The rates of therapeutic

interventions (revascularizations) were similar between the

groups: CAC (0.3%) and Reference (0.65%) for CABG

(P

=

(P =

.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

(P =

.86), and approximately one-fifth receiving ACE inhibitors

or ARBs in each group

(P =

.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,

P

= .65).

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 (

Table

3

). 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 (

Figure

).

DISCUSSION

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

Reference groups.

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

were inappropriate.

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

patients.

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)

(P

<.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

(P

<.003) and from 0.653 to

0.755

(P

= .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 studies^12,29-31

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

influences treatment.⁴¹

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

merit.

Limitations

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

cardiovascular diseases.^12,34,44

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.

CONCLUSIONS

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.

Acknowledgments

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

UCSF.

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: wchi@healthcore.com.

1. Mensah GA, Brown DW. An overview of cardiovascular disease

burden in the United States.

Health Aff (Millwood).

2007;26(1):

38-48.

2. Thom T, Haase N, Rosamond W, et al; American Heart Association

Statistics Committee and Stroke Statistics Subcommittee.

Heart disease and stroke statistics--2006 update: a report from

the American Heart Association Statistics Committee and Stroke

Statistics Subcommittee.

Circulation.

2006;113(6):e85-e151.

3. Go AS, Mozaffarian D, Roger VL, et al; American Heart Association

Statistics Committee and Stroke Statistics Subcommittee.

Heart disease and stroke statistics--2013 update: a report from the

American Heart Association.

Circulation.

2013(1);127:e6-e245.

4. Budoff MJ, Nasir K, McClelland RL, et al. Coronary calcium

predicts events better with absolute calcium scores than age-sexrace/

ethnicity percentiles: MESA (Multi-Ethnic Study of Atherosclerosis).

J Am Coll Cardiol.

2009;53(4):345-352.

5. Detrano RC, Wong ND, Doherty TM, et al. Coronary calcium

does not accurately predict near-term future coronary events in

high-risk adults.

Circulation.

1999;99(20):2633-2638.

6. Greenland P, Alpert JS, Beller GA, et al; 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(25):e50-e103.

7. Hoffmann U, Truong QA, Schoenfeld DA, et al; ROMICAT-II Investigators.

Coronary CT angiography versus standard evaluation in

acute chest pain.

N Engl J Med.

2012;367(4):299-308.

8. Latif MA, Budoff MJ. Coronary artery calcium scanning: a useful

tool for refining heart failure risk prediction? Future Cardiol.

2013;

9(1):1-3.

9. McEvoy JW, Blaha MJ, Nasir K, Blumenthal RS, Jones SR.

Potential use of coronary artery calcium progression to guide the

management of patients at risk for coronary artery disease events.

Curr Treat Options Cardiovasc Med.

2012;14(1):69-80.

10. Verrilli D, Welch HG. The impact of diagnostic testing on therapeutic

interventions.

JAMA.

1996;275(15):1189-1191.

11. Redberg RF, Shaw LJ. A review of electron beam computed

tomography: implications for coronary artery disease screening.

Prev Cardiol.

2002;5(2):71-78.

12. Detrano R, Guerci AD, Carr JJ, et al. Coronary calcium as a

predictor of coronary events in four racial or ethnic groups.

N

Engl J Med.

2008;358(13):1336-1345.

13. Goel R, Garg P, Achenbach S, et al. Coronary artery calcification

and coronary atherosclerotic disease.

Cardiol Clin.

2012;30(1):

19-47.

14. Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC. Coronary

artery calcium score combined with Framingham score for

risk prediction in asymptomatic individuals.

JAMA.

2004;291(2):

210-215.

15. Georgiou D, Budoff MJ, Kaufer E, Kennedy JM, Lu B, Brundage

BH. Screening patients with chest pain in the emergency

department using electron beam tomography: a follow-up study.

J Am Coll Cardiol.

2001;38(1):105-110.

16. Janowitz WR, Agatston AS, Kaplan G, Viamonte M Jr. Differences

in prevalence and extent of coronary artery calcium detected

by ultrafast computed tomography in asymptomatic men

and women.

Am J Cardiol.

1993;72(3):247-254.

17. Laudon DA, Behrenbeck TR, Wood CM, et al. Computed

tomographic coronary artery calcium assessment for evaluating

chest pain in the emergency department: long-term outcome of a

prospective blind study.

Mayo Clin Proc.

2010;85(4):314-322.

18. Simons DB, Schwartz RS, Edwards WD, Sheedy PF, Breen JF,

Rumberger JA. Noninvasive definition of anatomic coronary artery

disease by ultrafast computed tomographic scanning: a quantitative

pathologic comparison study.

J Am Coll Cardiol.

1992;

20(5):1118-1126.

19. Stanford W, Thompson BH, Weiss RM. Coronary artery calcification:

clinical significance and current methods of detection.

AJR

Am J Roentgenol.

1993;161(6):1139-1146.

20. Budoff MJ, Achenbach S, Blumenthal RS, et al; American Heart

Association Committee on Cardiac Imaging; Council on Clinical

Cardiology. Assessment of coronary artery disease by cardiac

computed tomography: a scientific statement from the American

Heart Association Committee on Cardiovascular Imaging and Intervention,

Council on Cardiovascular Radiology and Intervention,

and Committee on Cardiac Imaging, Council on Clinical Cardiology.

Circulation.

2006;114(16):1761-1791.

21. Arad Y, Spadaro LA, Goodman K, Newstein D, Guerci AD.

Prediction of coronary events with electron beam computed

tomography.

J Am Coll Cardiol.

2000;36(4):1253-1260.

22. Bluemke DA, Achenbach S, Budoff M, et al. Noninvasive

coronary artery imaging: magnetic resonance angiography and

multidetector computed tomography angiography: a scientific

statement from the American Heart Association Committee on

Cardiovascular Imaging and Intervention of the Council on Cardiovascular

Radiology and Intervention, and the Councils on Clinical

Cardiology and Cardiovascular Disease in the Young.

Circulation.

2008;118(5):586-606.

23. Gerber TC, Carr JJ, Arai AE, et al. Ionizing radiation in cardiac

imaging: a science advisory from the American Heart Association

Committee on Cardiac Imaging of the Council on Clinical Cardiology

and Committee on Cardiovascular Imaging and Intervention

of the Council on Cardiovascular Radiology and Intervention.

Circulation.

2009;119(7):1056-1065.

24. Kramer CM, Budoff MJ, Fayad ZA, et al; American College of

Cardiology Foundation; American Heart Association; American

College of Physicians Task Force on Clinical Competence and Training.

ACCF/AHA 2007 Clinical Competence statement on vascular

imaging with computed tomography and magnetic resonance:

a report of the American College of Cardiology Foundation/

American Heart Association/American College of Physicians Task

Force on Clinical Competence and Training.

J Am Coll Cardiol.

2007;50(11):1097-1114.

25. Chinnaiyan KM, Boura JA, Depetris A, et al; Advanced Cardiovascular

Imaging Consortium Coinvestigators. Progressive

radiation dose reduction from coronary computed tomography

angiography in a statewide collaborative quality improvement

program: results from the Advanced Cardiovascular Imaging Consortium.

Circ Cardiovasc Imaging.

2013;6(5):646-654.

26. Raff GL, Chinnaiyan KM, Share DA, et al; Advanced Cardiovascular

Imaging Consortium Co-Investigators. Radiation dose

from cardiac computed tomography before and after implementation

of radiation dose-reduction techniques.

JAMA.

2009;301(22):

2340-2348.

27. Lakoski SG, Greenland P, Wong ND, et al. Coronary artery calcium

scores and risk for cardiovascular events in women classified

as “low risk” based on Framingham risk score: the multi-ethnic

Arch Intern Med.

study of atherosclerosis (MESA). 2007;167(22):

2437-2442.

28. National Cholesterol Education Program (NCEP) Expert Panel

on Detection, Evaluation, and Treatment of High Blood Cholesterol

in Adults (Adult Treatment Panel III). Third Report of the National

Cholesterol Education Program (NCEP) Expert Panel on Detection,

Evaluation, and Treatment of High Blood Cholesterol in Adults

(Adult Treatment Panel III) final report.

Circulation.

2002;106(25):

3143-3421.

29. Anand DV, Lim E, Hopkins D, et al. Risk stratification in uncomplicated

type 2 diabetes: prospective evaluation of the combined

use of coronary artery calcium imaging and selective myocardial

perfusion scintigraphy.

Eur Heart J.

2006;27(6):713-721.

30. Polonsky TS, McClelland RL, Jorgensen NW, et al. Coronary

artery calcium score and risk classification for coronary heart

disease prediction.

JAMA.

2010;303(16):1610-1616.

31. Erbel R, Möhlenkamp S, Moebus S, et al; 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(17):1397-1406.

32. Taylor AJ, Bindeman J, Feuerstein I, Cao F, Brazaitis M,

O‘Malley PG. Coronary calcium independently predicts incident

premature coronary heart disease over measured cardiovascular

risk factors: mean three-year outcomes in the Prospective Army

Coronary Calcium (PACC) project.

J Am Coll Cardiol.

2005;46(5):

807-814.

33. Bonow RO. Clinical practice. should coronary calcium screening

be used in cardiovascular prevention strategies?

N Engl J

Med.

2009;361(10):990-997.

34. Rozanski A, Gransar H, Shaw LJ, et al. 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(15):1622-1632.

35. Wexler L, Brundage B, Crouse J, et al. Coronary artery calcification:

pathophysiology, epidemiology, imaging methods, and

clinical implications. a statement for health professionals from the

American Heart Association Writing Group.

Circulation.

1996;94:

1175-1192.

36. Cziraky MJ, Tan H, Bullano MF, Yu J, Schiebinger R, Willey VJ.

Impact of optimal lipid value achievement between 2005 and 2009

in patients with mixed dyslipidaemia on cardiovascular event

rates.

Int J Clin Pract.

2011;65(4):425-435.

37. Kamat SA, Bullano MF, Chang CL, Gandhi SK, Cziraky MJ.

Adherence to single-pill combination versus multiple-pill combination

lipid-modifying therapy among patients with mixed

dyslipidemia in a managed care population.

Curr Med Res Opin.

2011;27(5):961-968.

38. 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(18):1837-1847.

39. Kondos GT, Hoff JA, Sevrukov A, et al. Electron-beam

tomography coronary artery calcium and cardiac events: a

37-month follow-up of 5635 initially asymptomatic low- to

intermediate-risk adults.

Circulation.

2003;107(20):2571-2576.

40. O'Rourke RA, Brundage BH, Froelicher VF, et al. American College

of Cardiology/American Heart Association Expert Consensus

document on electron-beam computed tomography for the

diagnosis and prognosis of coronary artery disease.

Circulation.

2000;102(1):126-140.

41. Ebell MH. Should family physicians use coronary artery calcium

scores to screen for coronary artery disease? no: screening

is unproven, expensive, and potentially harmful.

Am Fam Physician.

2012;86(5):405-406.

42. Greenland P, Bonow RO, Brundage BH, et al; American College

of Cardiology Foundation Clinical Expert Consensus Task

Force (ACCF/AHA Writing Committee to Update the 2000 Expert

Consensus Document on Electron Beam Computed Tomography);

Society of Atherosclerosis Imaging and Prevention; Society of

Cardiovascular Computed Tomography. ACCF/AHA 2007 clinical

expert consensus document on coronary artery calcium scoring

by computed tomography in global cardiovascular risk assessment

and in evaluation of patients with chest pain.

J Am Coll

Cardiol.

2007;49(3):378-402.

43. WellPoint Medical Policy. Available at http://www.anthem.com/

medicalpolicies/policies/mp_pw_a050531.htm. Accessed August

26, 2014.

44. Polonsky TS, Lloyd-Jones DM. Coronary artery calcium testing:

dos and don’ts.

Cardiol Clin.

2012;30(1):49-55.

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