Medical Policy

This document addresses gene expression profiling for coronary artery disease (CAD).

Please refer to the document indicated below for information regarding testing for cardiovascular disease:

• CG-MED-57 Cardiac Stress Testing with Electrocardiogram

Investigational and Not Medically Necessary:

The use of gene expression profiling for coronary artery disease is considered investigational and not medically necessary.

Several studies have addressed the Corus^® CAD (CardioDx Inc., Palo Alto, CA), which is no longer commercially available as of 2019. Elashoff and colleagues (2011) described the initial validation study of the Corus CAD test. The researchers used a series of microarray and real-time polymerase chain reaction (RT-PCR) data sets to develop a blood-based gene expression algorithm for assessing obstructive CAD in non-diabetic subjects. The components of the algorithm were the expression levels of 23 genes, sex and age.

Two multicenter prospective clinical validation studies were published on the Corus CAD test, the PREDICT (Rosenberg, 2010) and COMPASS (Thomas, 2013). In 2012, Rosenberg and colleagues published data from the PREDICT trial indicating that the gene expression score (GES) developed for the Corus CAD test was significantly associated with composite primary endpoint of major adverse cardiovascular events and interventions over 12 months (p<0.001). At a score threshold of ≤ 15, the sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) were 86%, 41%, 90% and 33%, respectively. In the COMPASS study, the sensitivity, specificity, NPV and PPV of the GES were 89%, 52%, 96% and 24%, respectively. The researchers found that GES was significantly correlated with maximum percent stenosis (≥ 50). During the 6-month follow-up period, there were 28 adverse clinical events. The GES was significantly associated with major adverse cardiovascular events (MACE) and the likelihood of revascularization. At a score threshold of ≤ 15, the GES had a sensitivity of 96% and an NPV of 99%. The authors acknowledged that the study included potentially lower disease prevalence in the subjects due to the inclusion/exclusion criteria, and the lack of comparison of GES scores to other noninvasive imaging modalities.

Voros and colleagues (2014) used data from 610 individuals who participated in the PREDICT and COMPASS studies and found that there was a significant correlation between calcified plaque burden and the GES (r=0.50, p<0.001). In addition, the GES was significantly associated with maximum percent diameter stenosis (r=0.41, p<0.001).

Several studies have addressed the impact of the CORUS CAD test on patient management. McPherson and colleagues (2013) evaluated the impact of gene expression testing on disease management by a group of cardiology specialists. Participants (n=171) presenting with stable chest pain and related symptoms without a history of CAD were referred to six cardiologists for evaluation. In the prospective cohort of 88 participants, the cardiologist's diagnostic strategy was evaluated before and after GES testing. A total of 83 individuals were evaluable for study analysis, which included 57 (69%) women, mean age 53 ± 11 years, and mean GES 12.5 ± 9. Presenting symptoms were classified as typical angina, atypical angina, and noncardiac chest pain in 33%, 60%, and 7% of the participants (n=27, 50, and 6), respectively. This study found that individuals with low gene expression scores (≤ 15) were more likely to have a decrease in the intensity of diagnostic testing. Individuals with elevated GES were more likely to undergo additional testing for the evaluation of obstructive CAD. Limitations of this study include its small sample size and the evaluation of short-term (6 months) outcomes.

Herman and colleagues (2014) conducted a study (IMPACT-PCP) which assessed the impact of gene expression testing on clinical decision-making in individuals presenting to a primary care setting with symptoms of suspected CAD. The study was comprised of 261 consecutive stable, nonacute, nondiabetic participants presenting with typical and atypical symptoms of CAD. All of the participants underwent GES testing, with clinicians documenting their planned diagnostic strategy both prior to and after GES. After 30 days, a change in the diagnostic plan before and after GES testing was noted in 145 (58%) of the participants. A total of 93 (37%) of the participants had decreased intensity of testing versus the 52 (21%) which experienced an increase in the intensity of testing. In particular, among the 127 low-score Corus CAD individuals (51% of study participants), 60% (76/127) experienced decreased testing, and only 2% (3/127) experienced increased testing. The authors concluded that including the GES into the diagnostic workup demonstrated clinical utility above and beyond conventional clinical factors by optimizing the individual’s diagnostic evaluation. Limitations of the study include inclusion of individuals at low risk for CAD, short term follow-up and modest sample size.

Lapado and colleagues (2016) published an analysis of the PRESET Registry, with a focus on physician decision-making for individuals who had a high versus low score on the Corus CAD GES. The GES incorporated age- and sex-related factors and was known as the age/sex/gene expression score (ASGES). The analysis yielded that the referral rate to cardiology or advanced cardiac imaging was 10% (26/252) among individuals with low scores on the ASGES (≤ 15) compared with 44% (137/314) of individuals with elevated score (> 15) on the ASGES. The study was not able to specify the role that the Corus CAD played in the physician’s decision-making process and it did not compare physician decision-making with and without the ASGES. Another analysis of the same PRESET Registry data (Gul, 2019) focused on the 288 women enrolled in the registry. Physicians referred 9% (20/218) of women with a low ASGES (≤ 15) for additional testing compared with 44% (31/70) with an elevated ASGES (>15). After 1 year of follow-up, women with low ASGES scores had experienced fewer MACE than those with elevated ASGES (1.3% versus 4.2%, respectively). The difference between groups, however, was not statistically significant, p=0.16). As in the Lapado (2016) study described above, there was no comparison of physician decision-making with and without knowledge of an ASGES.

The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group found insufficient evidence to recommend the use of genomic profiling to assess cardiovascular risk and “discourages clinical use unless further evidence supports improved clinical outcomes” (EGAPP, 2010).

A 2019, Medicare Local Coverage Determination (LCD) stated the following on the Corus CAD test:

This is a non-coverage policy for the CORUS^® CAD test (CardioDX, Redwood City, CA). The test was previously covered by this contractor as a “rule out” test for stable non-diabetic patients presenting to a primary care physician with the new onset of symptoms suggestive of coronary artery disease (CAD)... Since initial coverage of the assay, the manufacturer has failed to demonstrate that testing resulted in improved patient outcomes or that testing changed physician management to result in improved patient outcomes

Kashyap (2018) conducted laboratory research on gene expression profiling for CAD. Their study identified genes and associated biological processes related to the severity of CAD, but did not appear to be linked to a commercially available test in the United States.

Heart disease is the leading cause of mortality in the United States, with more than 655,000 deaths per year. The most common type of heart disease is CAD, with nearly 366,000 deaths reported in 2017 (CDC, 2020). CAD is the narrowing or blockage of arteries that supply blood to the heart (coronary arteries). It is generally caused by atherosclerosis, the build-up of plaque, cholesterol and fatty deposits on the walls of the coronary arteries. Plaque build-up can reduce blood flow to the heart and, without this blood flow, the heart is unable to function properly. Angina, chest pain or discomfort, occurs when the heart muscle does not get sufficient blood. The symptoms of CAD may differ for men and women. Men commonly experience crushing chest pain whereas women are more likely to experience chest pain as pressure or tightness and have other symptoms such as shortness of breath, nausea and fatigue.

The diagnosis of CAD among individuals reporting signs or symptoms such as chest pain remains challenging. Clinical history-taking and physical examination are initial steps in assessment of these individuals. In addition, there are predictive tools that can help guide clinical decision-making. These include the Framingham risk score, QRisk algorithm and ASSIGN score which are all validated risk prediction tools (Ayerbe 2016). Laboratory tests include measurement of c-reactive protein levels and coronary artery calcium scores. Coronary angiography is considered the reference standard for diagnosing obstructive CAD, but it is invasive and potentially harmful. There is interest in additional non-invasive tests that can guide the diagnosis of obstructive CAD and lead to a reduction in the rate of adverse cardiac events.

Gene expression analysis was proposed as another method of assessing pre-test risk. An example of a gene expression profiling test is the Corus^® CAD test, which is no longer commercially available as of 2019. This was a peripheral blood test which integrated the expression levels of 23 genes known to play a role in the development of or response to atherosclerosis and combined data on the gene expression with the individual’s age and sex using a proprietary mathematical algorithm to generate a gene expression score (GES). Proponents of GES tests claimed that low GES scores could obviate the need for invasive testing in otherwise low or moderate risk patients. Research is ongoing on other approaches to GES profiling for cardiovascular disease.

Coronary angiography: A procedure that uses dye (contrast material) and x-rays to evaluate the extent of narrowing in coronary arteries.

Diamond-Forrester risk score: A classification tool used to estimate the pretest probability of coronary artery disease in patients with chest pain. The tool was developed in an outpatient setting.

Gene expression profiling: A laboratory test that measures the activity of multiple genes at once for diagnostic or prognostic purposes. The test result is often reported as a proprietary summary score.

Obstructive coronary artery disease: A reduction in blood flow to the heart muscle due to a narrowing of arteries that supply blood to the heart. The narrowing or blockage is caused by plaque that has built up in the arteries (atherosclerosis). Also known as ischemic heart disease.

The following codes for treatments and procedures applicable to this document are included below for informational purposes. Inclusion or exclusion of a procedure, diagnosis or device code(s) does not constitute or imply member coverage or provider reimbursement policy. Please refer to the member's contract benefits in effect at the time of service to determine coverage or non-coverage of these services as it applies to an individual member.

When services are Investigational and Not Medically Necessary:
For the following procedure code; or when the code describes a procedure indicated in the Position Statement section as investigational and not medically necessary.

Peer Reviewed Publications:

1. Ayerbe L, González E, Gallo V et al.Clinical assessment of patients with chest pain; a systematic review of predictive tools. BMC Cardiovasc Disord. 2016; 16:18.
2. Cagle SD Jr, Cooperstein N. Coronary Artery Disease: Diagnosis and Management. Prim Care. 2018; 45(1):45-61.
3. Elashoff MR, Wingrove JA, Beineke P, et al. Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients. BMC Med Genomics. 2011; 4:26.
4. Gul B, Lansky A, Budoff MJ et al The clinical utility of a precision medicine blood test incorporating age, sex, and gene expression for evaluating women with stable symptoms suggestive of obstructive coronary artery disease: Analysis from the PRESET Registry. J Womens Health (Larchmt). 2019; 28(5):728-735.
5. Herman L, Froelich J, Kanelos D, et al. Utility of a genomic-based, personalized medicine test in patients presenting with symptoms suggesting coronary artery disease. J Am Board Fam Med. 2014; 27(2):258-267.
6. Kashyap S, Kumar S, Agarwal V, et al. Gene expression profiling of coronary artery disease and its relation with different severities. J Genet. 2018; 97(4):853-867.
7. Ladapo JA, Budoff M, Sharp D et al. Clinical utility of a precision medicine test evaluating outpatients with suspected obstructive coronary artery disease. Am J Med. 2017; 130(4):482.
8. McPherson JA, Davis K, et al. The clinical utility of gene expression testing on the diagnostic evaluation of patients presenting to the cardiologist with symptoms of suspected obstructive coronary artery disease: results from the IMPACT (Investigation of a Molecular Personalized Coronary Gene Expression Test on Cardiology Practice Pattern) trial. Crit Path Cardiol. 2013; 12(2):37-42.
9. Rosenberg S, Elashoff MR, Beineke P, et al. Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients. Ann Intern Med. 2010; 153(7):425-434.
10. Rosenberg S, Elashoff MR, Lieu HD et al. PREDICT Investigators. Whole blood gene expression testing for coronary artery disease in nondiabetic patients: major adverse cardiovascular events and interventions in the PREDICT trial. J Cardiovasc Transl Res. 2012; 5(3):366-374.
11. Thomas GS, Voros S, McPherson JA, et al. A blood-based gene expression test for obstructive coronary artery disease tested in symptomatic nondiabetic patients referred for myocardial perfusion imaging the COMPASS study. Circ Cardiovasc Genet. 2013; 6(2):154-162.
12. Voros S, Elashoff MR, Wingrove JA et al. A peripheral blood gene expression score is associated with atherosclerotic plaque burden and stenosis by cardiovascular CT-angiography: results from the PREDICT and COMPASS studies. Atherosclerosis. 2014; 233(1):284-290.

Government Agency, Medical Society, and Other Authoritative Publications:

1. Centers for Disease Control and Prevention (CDC). Heart disease facts. Last updated October 2022. Available at: https://www.cdc.gov/heartdisease/facts.htm. Accessed on November 30, 2022.
2. Centers for Medicare and Medicaid Services (CMS). Local Coverage Determination (LCD): MolDX: Corus® CAD Assay (L37787). Available at: https://www.cms.gov/medicare-coverage-database/details/lcd-details.aspx?LCDId=37787&ContrId=228&ver=6&ContrVer=2&CntrctrSelected=228*2&Cntrctr=228&name=&DocType=2|4&LCntrctr=228*2&bc=AgACAAQAAAAA&. Accessed on November 30, 2022.
3. Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group. Recommendations from the EGAPP Working Group: genomic profiling to assess cardiovascular risk to improve cardiovascular health. Genet Med. 2010; 12(12):839-843.

CardioDx
Corus CAD

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