Coverage Policy Manual
Policy #: 2011049
Category: Laboratory
Initiated: June 2011
Last Review: June 2018
  Genetic Test: Coronary Artery Disease, Testing to Predict Risk (Corus CAD™)

Description:
The expression levels of various genes in circulating white blood cell or whole blood samples have been reported to discriminate between cases of obstructive coronary artery disease (CAD) and healthy controls. Multiplex gene expression testing can be combined with other risk factors to predict the likelihood of obstructive CAD in patients who present with chest pain or other suggestive symptoms, or in asymptomatic patients who are at high risk of CAD.
 
Heart disease is the leading cause of mortality in the U.S. and together with cerebrovascular disease accounted for 31% of deaths in 2007 (Xu, 2007). Individuals with signs and symptoms of obstructive coronary artery disease (CAD), the result of a chronic inflammatory process that ultimately results in progressive luminal narrowing and acute coronary syndromes, may be evaluated with a variety of tests according to prior risk. Coronary angiography is the gold standard for diagnosing obstructive CAD, but it is invasive and associated with a low but finite risk of harm. Thus, coronary angiography is recommended for patients at a high prior risk of CAD according to history, physical findings, electrocardiogram, and biomarkers of cardiac injury (Anderson, 2007). For patients initially assessed at low to intermediate risk, observation and noninvasive diagnostic methods, which may include imaging methods such as coronary computed tomographic angiography, may be recommended. Nevertheless, even noninvasive imaging methods have potential risks of exposure to radiation and contrast material. In addition, coronary angiography has a relatively low yield despite risk stratification recommendations. In one study of nearly 400,000 patients without known CAD undergoing elective coronary angiography, approximately 38% were positive for obstructive CAD (using the CAD definition, stenosis of 50% or more of the diameter of the left main coronary artery or stenosis of 70% or more of the diameter of a major epicardial or branch vessel that was more than 2.0 mm in diameter; result was 41% if using the broader definition, stenosis of 50% or more in any coronary vessel) (Patel, 2010). Thus, methods of improving patient risk prediction prior to diagnostic testing are needed.
 
A CAD classifier has been developed based on the expression levels, in whole blood samples, of 23 genes plus patient age and sex. This information is combined in an algorithm to produce a score from 1 to 40, with higher values associated with a higher likelihood of obstructive CAD. The test is marketed as Corus CAD™ (CardioDx, Inc.). The intended population is stable, nondiabetic patients suspected of CAD either because of symptoms, a high-risk history, or a recent positive or inconclusive test result by conventional methods.
 
Regulatory Status
The Corus CAD™ test is not a manufactured test kit and has not been reviewed by the U.S. Food and Drug Administration (FDA). Rather, it is a laboratory-developed test (LDT), offered by the Clinical Laboratory Improvement Act (CLIA)-licensed CardioDx Commercial Laboratory.
 
Coding
 
81493: Coronary artery disease, mRNA, gene expression profiling by real-time RT-PCR of 23 genes, utilizing whole peripheral blood, algorithm reported as a risk score
 
Other similar tests would be reported with the following unlisted CPT code:
 
81599: Unlisted multianalyte assay with algorithmic analysis.

Policy/
Coverage:
Gene expression testing to predict coronary artery disease does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness in improving health outcomes.  This testing is currently being studied in clinical trials.
 
For contracts without primary coverage criteria, gene expression testing to predict coronary artery disease is considered investigational.  Investigational services are specific contract exclusions in most member benefit certificates of coverage.
 

Rationale:
Assay Development
In an initial proof-of-principle study, Wingrove et al. evaluated 27 cases with and 14 controls without angiographically defined coronary artery disease (CAD) for expression of genes that differed significantly between the 2 groups, selecting 50 genes (Wingrove, 2008). To that the authors added 56 genes selected from relevant literature reports and evaluated expression of these 106 genes in an independent set of 63 cases and 32 controls, resulting in the selection of 14 genes that independently and significantly discriminated between groups in multivariable analysis. The significance of 11 of these 14 genes was replicated in a third set of 86 cases and 21 controls. Expression of the 14 genes was proportional to maximal coronary artery stenosis in the combined cohort of 215 patients. Limitations of this study included variable source of RNA for different cohorts (whole blood vs. separated whole blood leukocytes), small sample sizes in conjunction with large numbers of genes investigated and no apparent correction for multiple tests in significance testing, and modest discrimination between groups.
 
Final test development is described by Elashoff et al (Elashoff, 2011). The authors conducted 2 successive case-control gene expression discovery studies using samples from independent cohorts. Cases were angiographically defined as 75% or greater maximum stenosis in one major vessel, or 50% or greater in two vessels, and controls defined as less than 25% stenosis in all major vessels. Of clinical factors, diabetes had the most significant effect on gene expression; in the first case-control study (n=195), the expression of 42 genes was found to significantly (p<0.05) discriminate between cases and controls in nondiabetic patients and of 12 genes in diabetic patients, with no overlap. As a result, the second case-control study (n=198) and final development of the assay was limited to nondiabetic patients. The final selection of variables consisted of the expression of 20 CAD-associated genes and 3 normalization genes plus terms for age and sex, all incorporated into an algorithm that results in an obstructive CAD score ranging from 1–40. Receiver operating characteristic (ROC) analysis in the second case-control study resulted in an area under the curve (AUC) for CAD of 0.77 (95% confidence interval [CI]: 0.73-0.81).
 
Assay Validation
The finalized assay was validated in a prospective multicenter trial in which blood samples were collected from nondiabetic patients (n=526) with a clinical indication for coronary angiography but no known previous myocardial infarction (MI), revascularization, or obstructive CAD (available online at: clinicaltrials.gov NCT00500617) (Rosenberg, 2010). This is the same cohort from which the second assay development case-control cohort was drawn (Elashoff, 2011). Patients were sequentially allocated to development and validation sets. The authors defined obstructive CAD as 50% or greater stenosis in 1 or more major coronary arteries on quantitative coronary angiography, which they stated corresponds to approximately 65% to 70% stenosis on clinical angiography.
 
The assay AUC for CAD was 0.70 +/- 0.02 (p<0.001). The Diamond–Forrester clinical risk score AUC for CAD was 0.66; the combined AUC was 0.72 (p=0.003). Myocardial perfusion imaging (MPI) was performed on 310 patients; AUC for the assay algorithm score plus MPI versus MPI alone was 0.70 versus 0.43 (p<0.001). Sensitivity and specificity calculated for a disease likelihood of 20% were 85% and 43%, respectively, corresponding to negative and positive predictive values of 83% and 46%, respectively. The average scores for patients with and without obstructive CAD were 25 and 17, respectively; assay algorithm scores increased with increasing degree of stenosis by angiography, with score distributions overlapping considerably.
 
The authors conducted a reclassification analysis, in which patients were first classified by either the Diamond–Forrester clinical risk score or an expanded clinical model based on routine history and clinical evaluation, then reclassified by the assay algorithm score. The net reclassification improvement, which quantitates the difference between the proportion of patients who are correctly reclassified from an incorrect initial classification and the proportion who are incorrectly reclassified from a correct initial classification, was 20% (p<0.001) using the initial Diamond–Forrester clinical risk score and 16% (p<0.001) using the expanded clinical model.
 
Limitations
For the reclassification analysis, the authors prospectively defined reclassification transitions (by the assay results) from low risk or high risk to intermediate as censored for calculating the net reclassification improvement result. This resulted in ignoring nearly 15% of total patient test results. Moreover, 69% of the censored patient reclassifications were in the incorrect risk direction. The authors do not address how transitions to intermediate risk would be used in clinical practice. Additionally, neither the Diamond–Forrester clinical risk score nor the expanded clinical model included family history or electrocardiogram (EKG results), which might increase the accuracy of the initial classification and decrease the net reclassification improvement.
 
While the assay algorithm score discriminated cases from controls significantly better than the Diamond–Forrester clinical score by AUC analysis, it did not discriminate better than the expanded clinical model without family history or EKG (AUC, 0.745 vs. 0.732, respectively; p=0.089).
 
One difficulty in assay development may have been the selection of whole blood as the source of ribonucleic acid (RNA). While more convenient than isolated leukocytes, it has been reported that the abundance of globin gene RNA in whole blood may mask differences in expression of other genes despite globin reduction methods, making it difficult to detect the best gene signature (Li, 2008) (Min, 2008).
 
The results were validated on a population of patients already selected for coronary angiography. Whether it would perform similarly as a screen for CAD risk in a population at an earlier stage in evaluation is not known. Because the genes selected do not apply to diabetic patients, the test could not be used generally but would require clear patient selection procedures. Turnaround time may also be a limiting factor, since the test is not available as a manufactured kit for use in any clinical laboratory.
 
There were no identified published guidelines or position statements. The clinical utility of the Corus CAD™ is currnetly being tested in clinical trials (NCT01117506 and NCT01251302).
 
Summary
Gene expression assays to predict the likelihood of obstructive CAD have the potential to increase the proportion of patients selected for coronary angiography who truly have disease and reduce the number of patients who might otherwise be inappropriately exposed to radiation, contrast agent, and an invasive procedure. Corus CAD was developed and validated for this purpose in nondiabetic patients. Results of initial validation studies report that the test may improve CAD prediction beyond that of simple prediction models such as Diamond-Forrester, but the improvement in CAD prediction when added to routine clinical evaluation is uncertain. In particular, the impact of this test on the number of patients who have invasive testing is unclear. Furthermore, the test has only been validated in patients already selected for angiography and has not been generalized to the more clinically relevant population of patients who are being considered for angiography.
 
2012 Update
A literature search was conducted through May 2012. There was no new literature identified that would prompt a change in the coverage statement.
 
2013 Update
The most recent published literature was reviewed and summarized below. There was no new information identified that would prompt a change in the coverage statement.
 
PREDICT Trial
In another follow-up publication from the PREDICT trial, Lansky and colleagues found that GES was an independent predictor of CAD in multivariate analysis with an odds ratio of 2.53 (p=0.001) in the total study population and 1.99 (p=0.001) and 3.45 (p=0.001) for males and females respectively (Lansky, 2012). In this analysis MPI was not associated with any measures of CAD in the general population or when stratified by gender. For every 10-point increase in GES there was a corresponding 2 fold increase in odds of CAD, and an increase in maximum percent stenosis, the number of lesions, and total plaque volume.
 
COMPASS Study
Thomas and colleagues assessed the clinical validity and utility of the Corus CADTM for detection of obstructive CAD in non-diabetic patients in a multicenter, prospective study (COMPASS) (Thomas, 2013). Obstructive CAD was defined as 50% or greater stenosis in 1 or more major coronary arteries on quantitative coronary angiography. The COMPASS population differed from the PREDICT trial by including participants who had received a referral for myocardial perfusion imaging but had not been referred for invasive coronary angiography (ICA). Peripheral blood was drawn before MPI on all participants to obtain a GES. MPI positive participants underwent ICA based on the clinician’s judgment, and all other participants received CTA. Of the 537 enrolled patients only 431 (80.3%) were evaluable primarily due to refusal to perform ICA or CT-angiography. Follow-up was six months after testing with clinical end-points of MACE and revascularization. Using a GES cutoff of 15 or less, sensitivity and specificity of the Corus CAD test were 89% and 52% respectively. Net reclassification improvement in predicting CAD for GES compared to MPI (site-read), MPI (Core-Lab), Diamond-Forrester classification, and Morise score was 26%, 11%, 28% and 60% respectively.
 
Twenty-eight adverse events were observed which included 25 revascularizations within 30 days, 2 MACE, and 1 further revascularization. Twenty-five of the 26 patients with revascularization and both MACE patients had high GES (>15). The authors found that GES was associated with MACE and revascularization in a logistic regression model (p=0.0015) with a sensitivity of 96% and NPV of 99% at a score threshold of ≤15. The GES test was also correlated with maximum percent stenosis (r=0.46, P<0.001).
 
Results of the PREDICT and COMPASS study establish that the GES score has predictive ability for CAD. The PREDICT and COMPASS studies report that GES score is superior to the Diamond-Forrester model and to MPI in predicting CAD. However, there are several limitations to interpretation of the evidence on comparative predictive accuracy. In the PREDICT study the assay algorithm score discriminated cases from controls significantly better than the Diamond–Forrester clinical score by AUC analysis, however it did not discriminate better than the expanded clinical model without family history or electrocardiogram (AUC, 0.745 vs. 0.732, respectively; p=0.089). Additionally, neither the Diamond–Forrester clinical risk score nor the expanded clinical model included family history or EKG results, which might increase the accuracy of the initial classification and decrease the net reclassification improvement observed. Furthermore, the Diamond-Forrester model is a simple prediction rule that is not commonly used in clinical care. The Framingham risk score would be a more relevant comparator that is part of contemporary clinical care.
 
The COMPASS study compared the GES score to results from MPI stress testing. In that trial, the sensitivity of MPI was low at 27%. This is a considerably lower sensitivity than is routinely reported in the literature. For example, in one meta-analysis performed in support of ACC/AHA guidelines on myocardial perfusion imaging, sensitivity was estimated at 87-89% (Klocke, 2003). This raises the question of whether the accuracy of MPI in the COMPASS study is representative of that seen in current clinical care. Also, the comparison of overall accuracy of the GES score with MPI testing does not establish that clinical decisions would be changed, specifically whether patients with a positive MPI could safely forego further invasive testing based on a low GES score.
 
IMPACT Study
The IMPACT study compared a prospective cohort to matched historic controls to evaluate if the GES test altered the cardiologist’s evaluation and clinical management of CAD (McPherson, 2013). CAD was defined by the authors as no CAD (0% stenosis), CAD (≤50% stenosis) and CAD (>50% stenosis). All participants were non-diabetic, had no known prior MI or revascularization, were not using steroids, immune suppressive agents or chemotherapeutic agents, and had been referred to a cardiologist for evaluation of chest pain or angina equivalent symptoms. Eighty-eight patients were enrolled and 83 included in the final analysis. The matched cohort was composed of 83 patients selected with similar distributions of age, gender, clinical risk factors and had been evaluated at the institution within the past 3 to 30 months.
 
A change in patient management was defined prospectively as an increase or decrease in intensity of the diagnostic plan. GES were divided into a high risk group (>15) and a low risk group (≤15). The authors defined the categories of intensity in the following order: 1) no further cardiac testing or medical therapy for angina or non-cardiac chest pain, 2) stress testing (with/without imaging) or computed tomography coronary angiography, or 3) invasive coronary angiography (ICA). Within the prospective cohort, the diagnostic testing plan was changed for 58% of patients (95% CI, 46%-69%; p<0.001) with a greater reduction in testing intensity (39%) compared to increased testing intensity (19%). Compared to the historic control group the prospective cohort had a 71% reduction in overall diagnostic testing (P<0.001).
 
A secondary analysis examined the testing patterns around ICA. Thirty patients, 14 from the prospective cohort and 16 from the historic cohort, who underwent ICA were included in the analysis. The authors did not find a significant difference in diagnostic yield between the two groups (P=0.24). No major cardiovascular adverse events were observed for either cohort during the 6-month follow-up period.
 
Based on the IMPACT study, management decisions may be changed as a result of the GES score. This study is limited by comparison with historical controls, which were not well-matched to the study population. In addition, the impact of management changes in this study is uncertain. There is no information provided on whether the management changes led to beneficial effects on outcome, and it is not possible to estimate the likelihood of benefit from the information given in this study. Therefore, it is not possible to conclude that the GES score leads to changes in management that improve outcomes.
 
Summary
This test has been shown to have some predictive ability for future cardiac events and revascularization. In the COMPASS study, the overall accuracy of the GES score in predicting cardiac events was superior to MPI in patients who were referred for MPI testing. However, in that study, the reported sensitivity of MPI was considerably lower than generally reported in the literature. Also, it is unclear from the COMPASS study whether patients with a positive MPI could safely forego further testing based on a low GES score.
 
Clinical utility of the GES score has not been demonstrated. One study with methodologic limitations reports management changes as a result of the test, but the effect of these management changes is uncertain. There has been no convincing evidence presented that the use of GES scores can reduce unnecessary coronary angiography.
  
 
2014 Update
A literature search conducted through May 2014 did not reveal any new information that would prompt a change in the coverage statement. The key identified literature is summarized below.
 
A study by Voros et al (2013) pooled results from PREDICT and COMPASS to compare GES with CT imaging for detecting plaque burden (coronary artery calcium [CAC]), and luminal stenosis (Voros, 2014).  Six hundred ten patients, 216 from PREDICT (19% of enrolled patients) and 394 from COMPASS (73% of enrolled patients), who had undergone CAC scoring, CT angiography (CTA), and GES were included. Mean (SD) age was 57 (11) years; 50% were female, and approximately 50% used statin medication. Prevalence of obstructive CAD (≥50% stenosis) was 16% in the PREDICT cohort (patients referred for coronary angiography) and 13% in the COMPASS cohort (patients referred for MPI). In linear regression analyses, GES was statistically significantly correlated with CAC (r=0.50), the number of arterial segments with any plaque (r=0.37), overall stenosis severity (r=0.38), and maximum luminal stenosis (r=0.41) (all p<0.01), but strength of correlations was modest. Several GES cutoffs were explored (eg, to maximize diagnostic accuracy). Results using a cutoff of 15 points are shown in Table 2. For detecting luminal stenosis of 50% or greater, GES PPV and NPV were 0.23 and 0.95, respectively. For detecting clinically significant CAC (≥400), GES PPV and NPV were 0.14 and 0.97, respectively. Limitations of the study included lack of clinical outcomes (eg, survival, morbidity), and lack of comparison with CAC and CTA for predicting these outcomes (ie, incremental predictive value of GES was not assessed).
 
Performance of Gene Expression Score and Diamond-Forrester Classification10) a for Coronary Artery Plaque Burden and Luminal Stenosis compared against measurements of  CAC >0, CAC ≥400, Luminal Stenosis by CT Angiography ≥50% and ≥70%.
    • GES ROC AUC (95% CI), CAC >0 at 0.75 (0.71 to 0.79), CAC≥400 at 0.75 (0.68 to 0.82), Luminal Stenosis by CT angiography ≥50% at 0.75 (0.70 to 0.80), ≥70 at 0.75 (0.67 to 0.83).
 
    • Diamond-Forrester (95% CI), CAC >0 at 0.65 (0.61 to 0.69), CAC ≥400 at 0.61 (0.53 to 0.69), Luminal stenosis by CT angiography ≥50% at 0.65 (0.59 to 0.71), ≥70% at 0.63 (0.53 to 0.73).
 
Finally, modest correlations of GES with coronary artery plaque burden and luminal stenosis in the absence of clinical outcomes are of uncertain clinical significance.
 
In a similar but unmatched study, IMPACT-PCP evaluated whether GES altered primary care providers’ diagnostic evaluation and clinical management of stable, nonacute, nondiabetic patients presenting with CAD symptoms (Herman, 2014). Nine primary care providers at 4 centers evaluated 261 consecutive patients, 251 (96%) of whom were eligible for participation. Clinicians documented their pre-test impressions and recommendations for further evaluation and management on a clinical report form. All patients underwent GES testing. The primary outcome was the change in patient management between preliminary and final treatment plans.
 
Results from IMPACT-PCP were similar: Diagnostic testing plans were changed for 58% of patients, with reductions in testing intensity more common than increases (64% vs 34%; p<0.001). No study-related major adverse cardiovascular events were observed in 247 patients (98%) who had at least 30 days of follow-up.
 
The REGISTRY 1 assessed the impact of GES on patient management decisions by examining the association between GES test results and post-test referral patterns (Ladapo, 2014). Primary care practitioners at 7 centers evaluated 342 stable, nonacute, nondiabetic patients presenting with CAD symptoms. All patients underwent GES testing. Of 167 patients with low (≤15) GES, 10 (6%) were referred for further cardiac evaluation compared with 122 (70%) of 175 patients in the high GES group (p<0.001). Analysis of GES as a continuous variable showed a statistically significant change in cardiac referrals for every 10-point change in GES (adjusted OR, 13.7 [95% CI, 12.5-15.0]; p<0.001). Over a mean follow-up of 264 days, there were 5 major adverse cardiovascular events, 2 in the low GES group and 3 in the high GES group. Of 21 patients who underwent elective invasive coronary angiography, 1 (50%) of 2 in the low GES group and 8 (42%) of 19 in the high GES group had obstructive findings.
 
Although REGISTER 1 followed patients for approximately 9 months, reported clinical outcomes do not indicate benefits of GES testing. Therefore, it is not possible to conclude that GES leads to changes in management that improve outcomes.
 
One ongoing clinical trials was identified for CardioDX is establishing the PRESET registry to evaluate patterns of care associated with the use of Corus® CAD in real-world clinical care settings (NCT01677156). Adults who present to their primary clinician's office with chest pain suggesting obstructive CAD are eligible. Patients with a history of CAD, including previous MI, New York Heart Association class 3 or 4 heart failure, or diabetes mellitus are excluded. Estimated enrollment is 1000 patients, and study completion is expected in March 2015.
 
American Heart Association
In 2012, AHA released a policy statement on genetics and cardiovascular disease (Ashley, 2012) Gene expression testing is not specifically mentioned. Generally, the writing committee supported recommendations issued in 2000 by a now defunct Advisory Committee to the Department of Health and Human Services (DHHS), which stated, “No test should be introduced in the market before it is established that it can be used to diagnose and/or predict a health-related condition in an appropriate way.” (NIH, 2000).
 
2015 Update
A literature search conducted through May 2015 did not reveal any new information that would prompt a change in the coverage statement.  The key identified literature is summarized below.
 
In a follow-up assay validation publication, the authors evaluated GES performance in nondiabetic patients from the gene discovery and algorithm development cohorts in combination with the validation cohort (N=1038) and, as would be expected, found similar performance (AUC, 0.70±0.02; p<0.001) (Daniels, 2014).
 
In a follow-up study to evaluate biological variation over time, Daniels and colleagues drew second blood samples from 192 COMPASS participants (36%) 1 year after the original study (Daniels, 2014).  In 19 patients who had cardiac events, including revascularization, between blood draws, mean change in GES score was 1.1 points. In 173 patients without cardiac events, mean change was 1.4 points, from 15.9 to 17.3, corresponding to a 2.5% increase in predicted risk of obstructive CAD. On logistic regression, approximately half of the increase was due to increased patient age. Lack of paired second anatomic studies limits interpretation of these findings
 
In a follow-up study, Ladapo and colleagues pooled results for women who participated in the IMPACT-PCP (n=140) and REGISTRY 1 (n=180) studies to evaluate the impact of GES on further cardiac evaluation (N=320) (Ladapo, 2015). Mean age of this cohort was 58 years; mean systolic and diastolic blood pressure were 129 mmHg and 79 mmHg, respectively; most were white (84%) and nonsmokers (59%); and mean (SD) GES was 10 (8). Seventy-six percent of women had low GES (≤15). Referral rate for further cardiac evaluation was 4% for women with low GES (n=248) versus 83% for women with elevated GES (n=72). Overall, there were 4 MACE/revascularization events. (Median follow-up was 37 days in IMPACT-PCP and 278 days in REGISTRY I.) Events per GES risk group were not reported.
 
2016 Update
A literature search conducted through May 2016 did not reveal any new information that would prompt a change in the coverage statement.
 
2017 Update
A literature search conducted through May 2017 did not reveal any new information that would prompt a change in the coverage statement.
 
2018 Update
A literature search conducted using the MEDLINE database through May 2018 did not reveal any new information that would prompt a change in the coverage statement.
 
Voora et al evaluated the Corus CAD score in a cohort from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial funded by National Heart, Lung, and Blood Institute (Voora, 2017)  PROMISE was a randomized controlled trial (2015) that enrolled 10,003 outpatients who were randomized to functional (ie, exercise, echocardiographic, or nuclear stress testing) or anatomic (ie, computed tomography angiography [CTA]) diagnostic testing (Douglas, 2015). Patients were symptomatic and at increased risk for CAD based on age and/or the presence of CAD risk factors, and presented with symptoms suggestive of obstructive CAD. An ancillary analysis of PROMISE patients was supported in part by the manufacturer and included 2370 PROMISE patients without diabetes who were not on anti-inflammatory medications and who had samples in the biorepository of sufficient quality for analysis. The definition of obstructive CAD was 70% or more stenosis in a major coronary artery or 50% or more left main stenosis using CTA data. The PROMISE trial was the largest study and it used the American Heart Association definition for obstructive CAD (Voora, 2017). In this population of patients referred for nonurgent, noninvasive testing, the sensitivity was 73% (95% CI, 64% to 81%), the negative likelihood ratio was 0.56 (95% CI, 0.42 to 0.77), and the NPV was 94% (95% CI, 92% to 96%).
 
Ladapo et al evaluated the Corus CAD score in a cohort of patients from the PRESET (A Registry to Evaluate Patterns of Care Associated with the Use of Corus CAD in Real World Clinical Care Settings) registry (Ladapo, 2017). The PRESET registry is funded by the manufacturer. This registry enrolled patients from 21 primary care practices in the United States between August 2012 and August 2014. Patients had nonacute chest pain and typical or atypical symptoms of obstructive CAD without history of myocardial infraction or revascularization, diabetes, suspected acute myocardial infarction, high-risk unstable angina pectoris, New York Heart Association class III or IV heart failure symptoms, cardiomyopathy with an ejection fraction of 35% or less, severe cardiac valvular diseases, current systemic infectious or inflammatory condition, or recent treatment with an immunosuppressive or chemotherapeutic agent. The report is primarily focused on physician decision-making but includes a table of the Corus CAD score and advanced cardiac testing results for obstructive CAD in 84 patients.
 
Evidence for the Corus CAD score has not directly demonstrated that the test is clinically useful and a chain of evidence cannot be constructed to supports its utility. The evidence is insufficient to determine the effects of the technology on health outcomes.

CPT/HCPCS:
81493Coronary artery disease, mRNA, gene expression profiling by real-time RT-PCR of 23 genes, utilizing whole peripheral blood, algorithm reported as a risk score
81599Unlisted multianalyte assay with algorithmic analysis
84999Unlisted chemistry procedure

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