Policy #: 538 Latest Review Date: December 2015

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1 Name of Policy: Cardiovascular Risk Panels Policy #: 538 Latest Review Date: December 2015 Category: Laboratory Policy Grade: B Background/Definitions: As a general rule, benefits are payable under Blue Cross and Blue Shield of Alabama health plans only in cases of medical necessity and only if services or supplies are not investigational, provided the customer group contracts have such coverage. The following Association Technology Evaluation Criteria must be met for a service/supply to be considered for coverage: 1. The technology must have final approval from the appropriate government regulatory bodies; 2. The scientific evidence must permit conclusions concerning the effect of the technology on health outcomes; 3. The technology must improve the net health outcome; 4. The technology must be as beneficial as any established alternatives; 5. The improvement must be attainable outside the investigational setting. Medical Necessity means that health care services (e.g., procedures, treatments, supplies, devices, equipment, facilities or drugs) that a physician, exercising prudent clinical judgment, would provide to a patient for the purpose of preventing, evaluating, diagnosing or treating an illness, injury or disease or its symptoms, and that are: 1. In accordance with generally accepted standards of medical practice; and 2. Clinically appropriate in terms of type, frequency, extent, site and duration and considered effective for the patient s illness, injury or disease; and 3. Not primarily for the convenience of the patient, physician or other health care provider; and 4. Not more costly than an alternative service or sequence of services at least as likely to produce equivalent therapeutic or diagnostic results as to the diagnosis or treatment of that patient s illness, injury or disease. Page 1 of 12

2 Description of Procedure or Service: Cardiovascular risk panels refer to different combinations of cardiac markers that are intended to evaluate risk of cardiovascular disease. There are numerous commercially available risk panels that include different combinations of lipids, non-cardiac biomarkers (e.g. troponin, BNP), measures of inflammation, metabolic parameters, and/or genetic markers. Risk panels report the results of multiple individual tests, as distinguished from quantitative risk scores that combine the results of multiple markers into one score. Cardiovascular disease remains the single largest cause of morbidity and mortality in the developed world. As a result, accurate prediction of cardiovascular risk is a component of medical care that has the potential to focus and direct preventive and diagnostic activities. Current methods of risk prediction in use in general clinical care are not highly accurate, and as a result there is a potential unmet need for improved risk prediction instruments. Components of cardiovascular risk include family history, cigarette smoking, hypertension, and lifestyle factors such as diet and exercise. In addition, numerous laboratory tests have been associated with CV risk, most prominently lipids such as LDL and HDL. These clinical and lipid factors are often combined into simple risk prediction instruments, such as the Framingham risk score (FRS). The Framingham risk score provides an estimate of the 10 year risk for developing cardiac disease, and is currently used in clinical care to determine the aggressiveness of risk factor intervention, such as the decision to treat hyperlipidemia with statins. Many additional biomarkers, genetic factors and radiologic measures, have been associated with increased risk of CV disease. Over 100 emerging risk factors have been proposed as useful for refining estimates of cardiovascular risk. Some general categories of these potential risk factors are as follows: Lipid markers. In addition to LDL and HDL, other lipid markers may have predictive ability, including the apolipoproteins, Lipoprotein (a), lipid subfractions, and/or other measures. Inflammatory markers. Many measures of inflammation have been linked to the likelihood of CV disease. High sensitivity CRP is one example of an inflammatory markers, other include fibrinogen, interleukins, and tumor necrosis factor. Metabolic syndrome biomarkers. Measures associated with metabolic syndrome, such as specific dyslipidemic profiles or serum insulin levels, have been associated with increased risk of CV disease. Genetic markers. A number of mutations that associated with increased thrombosis risk, such as the MHTFR mutation or the prothrombin gene mutations, have been associated with increased CV risk. In addition, numerous single-nucleotide polymorphisms (SNP s) have been associated with CV disease in large genome-wide studies. CV risk panels may contain measures from one or all of the previous categories and may include additional measures not previously listed such as radiologic markers (carotid CMT, calcium scores). Some cardiovascular risk panels are relatively limited, including a few markers in addition to standard lipids. Others include a wide variety of potential risk factors from a number of different categories, often including both genetic and non-genetic risk factors. Other panels are composed entirely of genetic markers. Page 2 of 12

3 Some examples of commercially available CV risk panels are as follows: Health Diagnostics Cardiac Risk Panel: MTHFR gene analysis, common variants; vitamin D, 1,25 dihydroxy; B-type natriuretic peptide; lipoprotein-associated phospholipase A 2 (Lp-PLA 2 ); myeloperoxidase; apolipoprotein; immune complex assay; lipoprotein, blood; electrophoretic separation and quantitation; very long chain fatty acids; total cholesterol; HDL; LDL; triglycerides; hs-crp; Lp(a); insulin, total; fibrinogen; apolipoprotein analysis; multiple SNPs associated with coronary artery disease (CAD). Genova Diagnostics CV Health Plus Genomics Panel: apo E; prothrombin; factor V Leiden; fibrinogen; HDL; HDL size; HDL particle number; homocysteine; LDL; LDL size; LDL particle number; Lp(a); Lp-PLA 2 ; MTHFR gene; triglycerides; verylow-density lipoprotein (VLDL); VLDL size; vitamin D; hs-crp. Genova Diagnostics CV Health Plus Panel: fibrinogen; HDL; HDL size; HDL particle number; homocysteine; LDL; LDL size; LDL particle number; lipid panel; Lp(a); Lp-PLA 2 ; triglycerides; VLDL; VLDL size; vitamin D; hs-crp. Cleveland HeartLab CVD Inflammatory Profile: hs-crp, urinary microalbumin, myeloperoxidase, Lp-PLA 2, F 2 isoprostanes. Applied Genetics Cardiac Panel: genetic mutations associated with CAD: cytochrome p450 mutations associated with metabolism of clopidogrel, ticagrelor, warfarin, β-blockers, rivaroxaban, prasugrel (2C19, 2C9/VKORC1, 2D6, 3A4/3A5), factor V Leiden, prothrombin gene, MTHFR gene, APOE gene. Genetiks Genetic Diagnosis and Research Center Cardiovascular Risk Panel: factor V Leiden, factor V R2, prothrombin gene, factor XIII, fibrinogen-455, PAI-1, GPIIIs (HPA-1), MTHFR, ACE I/D, apo B, apo E. Singulex Cardiac-Related Test Panels: Several panels of markers related to cardiac dysfunction, vascular inflammation and dysfunction, dyslipidemia, and cardiometabolic status are offered by Singulex (Alameda, CA). Some of these panels are offered in conjunction with a CV disease testing and wellness management service. The test panels use an immunoassay method referred to as Proprietary highprecision Single Molecule Counting [SMC] technology. o Cardiac Dysfunction panel: SMC ctnl (high-sensitivity troponin), N- terminal pro-b-type natriuretic peptide o Vascular Inflammation and Dysfunction panel: SMC IL-6, SMC IL-17A, SMC TNFα, SMC Endothelin, Lp-PLA 2, hs-crp, homocysteine, vitamin B 12, folate. o Dyslipidemia panel: total cholesterol, LDL-C (direct), apo B, small dense LDL, HDL cholesterol, apo AI, HDL 2b, triglycerides, Lp(a). o Cardiometabolic panel: parathyroid, vitamin D, calcium, magnesium, leptin, adiponectin, ferritin, cortisol, cystatin C, hemoglobin A 1c, glucose, insulin, thyroid-stimulating hormone (TSH), T3 and free T4, uric acid, liver panel, renal panel, thyroid peroxidase antibody, thyroglobulin antibody. In addition to panels that are specifically focused on CV risk, a number of commercially available panels include markers associated with CV health, along with a range of other markers Page 3 of 12

4 that have been associated with inflammation, thyroid disorders and other hormonal deficiencies, and other disorders. Examples of these panels include: Singulex Cardiometabolic Panel: described above. WellnessFX (San Francisco, CA) Premium: total cholesterol, HDL, LDL, triglycerides, Apo AI, Apo B, LP(a), Lp-PLA 2, omega-3 fatty acids, free fatty acids, lipid particle numbers, lipid particle sizes, blood urea nitrogen/creatinine, aspartate aminotransferase and alanine aminotransferase, total bilirubin, albumin, total protein, dehydroepiandrosterone, free testosterone, total testosterone, estradiol, sex hormone binding globulin, cortisol, insulin-like growth factor 1, insulin, glucose, hemoglobin A 1c, total T4, T3 uptake, free T4 index, TSH, total T3, free T3, reverse T3, free T4, hs-crp, fibrinogen, homocysteine, complete blood count with differential, calcium, electrolytes, bicarbonate, ferritin, total iron binding capacity, vitamin B 12, red blood cell magnesium, 25-hydroxy vitamin D, progesterone, follicle-stimulating hormone, luteinizing hormone. Policy: Cardiovascular risk panels, consisting of multiple individual biomarkers intended to assess cardiac risk (other than simple lipid panels-see below), do not meet Blue Cross and Blue Shield of Alabama s medical criteria for coverage and are considered investigational. A simple lipid panel is generally composed of the following lipid measures: triglycerides, HDL, LDL and total cholesterol. Certain calculated ratios, such as the total/hdl cholesterol may also be reported as a part of a simple lipid panel. Other types of lipid testing, i.e., apolipoproteins, lipid particle number or particle size, lipoprotein (a), etc., are not considered to be components of a simple lipid panel. This policy does not address the use of panels of biomarkers in the diagnosis of acute myocardial infarction. Blue Cross and Blue Shield of Alabama does not approve or deny procedures, services, testing, or equipment for our members. Our decisions concern coverage only. The decision of whether or not to have a certain test, treatment or procedure is one made between the physician and his/her patient. Blue Cross and Blue Shield of Alabama administers benefits based on the member s contract and corporate medical policies. Physicians should always exercise their best medical judgment in providing the care they feel is most appropriate for their patients. Needed care should not be delayed or refused because of a coverage determination. Page 4 of 12

5 Key Points: This policy was created in November 2013, and updated periodically with literature reviews through searches of the Medline database, most recently through October There is a large amount of literature on the association of individual risk factors with cardiovascular disease. The vast majority of this literature evaluates correlations between individual biomarkers and the presence of, or future development of, cardiovascular disease. A framework for evaluation the clinical utility of risk factor assessment includes the following steps: 1. Standardization of the measurement of the risk factor. 2. Determination of its contribution to risk assessment. As a risk factor, it is important to determine whether the novel risk factor independently contributes to risk assessment compared to established risk factors. In addition, as there are many potential novel risk factors that could be incorporated into existing CV risk panels, it is important to understand the relationship of each individual risk factor with other risk factors. 3. Determination of how the novel risk assessment will be used in the management of the patient, compared to standard methods of assessing risk, and whether any subsequent changes in patient management result in an improvement in patient outcome. Helfand et al have suggested a similar framework for evaluating the utility of risk factors that includes the concept of reclassifying patients into clinically relevant risk factors. These suggested criteria are as follows: Risk factor should be easily and reliably measured. Risk factor should be an independent predictor of major CV events in patients with an intermediate risk of CV disease and no history of CV disease. Risk factor should reclassify a substantial portion of intermediate risk patients as highrisk. Reclassified individuals should be managed differently than they otherwise would have been. If other risk factors provide similar prognostic information, then convenience, availability, cost and safety should be considered in choosing among them. Literature Review Literature was sought that addressed the criteria for demonstrating clinical utility outlined above. Clinical Validity Association of Single Risk Markers with CV Risk There is a large evidence base on the association of individual risk markers with cardiovascular risk. Many observational studies have established that individual risk markers are independent predictors of cardiac risk. In 2013, van Holten et al conducted a systematic review of metaanalyses of prospective studies evaluating the association between serologic biomarkers and primary CV events (i.e., CV disease events and stroke in CV disease-naïve populations) and secondary CV events (i.e., CV disease events and stroke in populations with a history of CV disease). The final data synthesis included 85 studies published from 1988 to Sixty-five Page 5 of 12

6 meta-analyses reported biomarkers association with primary CV events and 43 reported associations with secondary CV events. Eighteen meta-analyses reported biomarkers association with ischemic stroke in patients with a history of CV disease. Only 2 meta-analyses that reported associations with ischemic stroke in patients with no history of CV disease were identified, and results were not reported. CV disease risks for markers with the strongest associations are summarized in Table 1. Table 1: Serum Biomarkers and Cardiovascular Risk in van Holten et al (2013) Study Type Marker RR, HR, or OR 95% Confidence Interval Prediction of CV Events in Patients with No History of CV Disease CRP 2.43 (RR) 2.10 to 2.83 Fibrinogen 2.33 (HR) 1.91 to 2.84 Cholesterol 0.44 (HR) 0.42 to 0.48 ApoB 1.99 (RR) 1.65 to 2.39 Apo A:Apo B ratio 1.86 (RR) 1.55 to 2.22 HDL 1.83 (HR) 1.65 to 2.03 Vitamin D 1.83 (HR) 1.19 to 2.80 Prediction of CV Events in Patients with Positive History of CV Disease ctn I and T 9.39 (OR) 6.46 to High sensitivity CRP 5.65 (OR) 1.71 to Creatinine 3.98 (HR) 3.02 to 5.24 Cystatin C 2.62 (RR) 2.05 to 3.37 Prediction of Ischemic Stroke in Patients with Positive History of CV Disease Fibrinogen 1.75 (HR) 1.55 to 1.98 Uric acid 1.47 (RR) 1.19 to 1.76 Apo: apolipoprotein; CRP: C-reactive protein; ctn: cardiac troponin; CV: cardiovascular; HDL: high density lipoprotein; HR; hazard radio; OR: odds ratio; RR: relative risk Since the publication of the van Holten et al systematic review, multiple additional studies have reported the association between a various risk factors and cardiovascular outcomes. Representative examples of reported associations include: endothelin-1 in predicting mortality in patients with heart failure with reduced ejection fraction; troponin and B-type natriuretic peptide in predicting CV disease-related death; growth differentiation factor and interleukin 6 (IL-6) with CV disease- and non-cv-disease-related death mid-regional pro-atrial natriuretic peptide and C- terminal pro-endothelin-1 with morbidity and mortality after cardiac surgery. Risk markers and CV Risk Reclassification Other studies have demonstrated that risk markers can be used to reclassify patients into different risk categories. Helfand et al reported a summary of nine systematic reviews evaluating novel risk factors association with coronary heart disease (CHD). Of the laboratory risk factors evaluated, C-reactive protein (CRP), homocysteine, and lipoprotein (a) were independent predictors of major CHD events when added to the Framingham risk score. However, none of the available systematic reviews evaluated the effect of each novel risk factor on risk classification among patients classified as intermediate-risk by the Framingham risk score. In a study of 165,544 patients without baseline CV disease enrolled in 37 prospective cohorts, the addition of individual novel lipid-related risk factors to a conventional risk-classification models including total cholesterol and high-density lipoprotein cholesterol, net reclassification improvements were less than 1% with the addition of each of these markers to risk scores containing conventional risk factors. Page 6 of 12

7 Association of Multiple-Marker Panels with CV Risk A more limited body of literature has evaluated the association between panels of markers and CV risk and/or the reclassification of patients into different risk categories. Greiseneggar et al evaluated the association between a panel of biomarkers and mortality after transient ischemic attack (TIA) and minor ischemic stroke. The study population included 929 patients who were enrolled from and followed until Fifteen potential risk markers were prospectively measured (IL-6, CRP, neutrophil-gelatinase-associated lipocalin, soluble tumor necrosis factor α receptor-1 [stnfr-1], thrombomodulin, fibrinogen, von Willebrand factor [vwf], P-selectin, protein Z, D-dimer, antiphosphorylcholin, N-terminal pro- B-type natriuretic peptide [NT-proBNP], heart-type fatty acid binding protein [HFABP], neuronspecific enolase, brain-derived neurotrophic factor). None of the biomarkers was predictive of nonfatal ischemic stroke or myocardial infarction (MI). Six factors were individually associated with vascular death, of which the four with the strongest association (vwf, HFABP, NTproBNP, stnfr-1) were entered into a predictive model. The independent contribution of the four biomarkers taken together added more prognostic information than the established clinical risk factors used in a conventional model (clinical risk factors: χ 2 =43, p=0.002; 4 biomarkers: χ 2 =55, p<0.001). Cho et al reported on the impact of six biomarkers (high-sensitivity C-reactive protein; IL-6; receptor for advanced glycation end products; lipoprotein-associated phospholipase A 2 ; adiponectin; regulated on activation, normal T cell expressed and secreted) on CV risk classification in a case-control study with 503 patients with coronary artery disease and 503 healthy controls. The addition of the six novel biomarkers to the multivariable risk prediction model led to an improvement in the C statistic (0.953 vs 0.937, p<0.001). However, the performance of the model in a cohort not enriched with coronary artery disease patients is unknown. Wilsgaard et al evaluated 51 protein biomarkers for association with risk of incident MI with the goal of developing a clinically significant risk model that adds information to conventional risk models. Patients were drawn from a population-based cohort study to form a case-control study, with 419 cases who experienced a first-ever MI within the 10-year follow-up period and 398 controls randomly selected from the participants who had no MI during the follow-up period. Fifty-one markers were selected for evaluation based on previously reported associations and the availability of immunoassay techniques and passage of internal quality controls. Seventeen markers were predictive of MI after adjustment for traditional CV risk factors. By adding risk markers back into the traditional risk factor-based model, the authors determined that a composite of Apo B/Apo AI, plasma kallikrein, lipoprotein (a), and matrix metalloproteinase 9 increased the model s area under the receiver operating curve by 0.027, with a net reclassification index of 9%. Association of Multiple-Marker Panels With Wellness The preponderance of the literature on CV risk panels has focused on risk of specific events related to CV disease (e.g., stroke, MI) or on the development of CV disease. With the development of panels that address wellness more broadly, studies were sought on the Page 7 of 12

8 association of risk markers and measures of overall wellness or health. No empiric studies were identified. In 2015, Lara et al reported the recommendations of a work group convened by the U.K.s Medical Research Council to develop recommendations for a panel of biomarkers for healthy aging. A variety of markers, some laboratory-based, associated with physical capability and physiologic, cognitive, endocrine, immune, and sensory functions are proposed. Clinical Utility While multiple risk factors have been individually associated with CV disease, there is no convincing evidence that addition of any individual risk marker, or combination or risk markers, leads to clinically meaningful changes in management that improve outcomes. In the available studies, the improvement in risk prediction has generally been of a small magnitude, and/or has not been found to be associated with clinically meaningful management changes. Because of this uncertain impact on management, the clinical utility for any of the individual risk markers is either low or uncertain. The available evidence on individual risk markers is only of limited value in evaluating CV risk panels. It is difficult to extrapolate the results of single risk factors to panels given the variable composition of panels. Ideally, panels should be evaluated individually as to their impact on clinical decision making. No published studies were identified that evaluated the use of commercially available CV risk panels as risk prediction instruments in clinical care. Some studies have attempted to incorporate novel risk markers into an overall quantitative risk score, but these risk scores are not the same as CV risk panels, which report the results of individual risk factors. Furthermore, there are no standardized methods for combining multiple individual risk factors with each other, or with established risk prediction instruments such as the Framingham risk score. Therefore, there is a potential for both overestimation and underestimation of the true cardiac risk. This may lead to management decisions that are based on an inaccurate risk assessment. As a result of these deficiencies, it is not possible to make a reliable assessment of the impact of using CV risk panels on health outcomes. Summary The evidence for the use of cardiovascular (CV) risk panels in individuals who have risk factors for CV disease includes multiple cohort and case-control studies and systematic reviews of these studies. Relevant outcomes are test accuracy and validity, other test performance measures, change in disease status, and morbid events. The available evidence from cohort and case-control studies indicates that many of the individual risk factors included in CV risk panels are associated with increased risk of CV disease. However, it is not clear how the results of individual risk factors impact management changes, so it is also uncertain how the panels will impact management decisions. Given the lack of evidence for clinical utility of any individual risk factor beyond simple lipid measures, it is unlikely that the use of CV risk panels improves outcome. Studies that have evaluated the clinical validity of panels of multiple markers have not assessed management changes that would occur as a result of testing, or demonstrated improvements in outcomes. The evidence is insufficient to determine the effects of the technology on health outcomes. Page 8 of 12

9 Practice Guidelines and Position Statements In 2013, the American College of Cardiology (ACC) and the American Heart Association (AHA) issued guidelines on the assessment of CV risk. These guidelines recommend that ageand sex-specific pooled cohort equations which include total cholesterol and HDL to predict the 10-year risk of a first hard atherosclerotic CV disease event be used in non-hispanic blacks and non-hispanic whites age between 40 and 79 years (AHA/ACC class of recommendation: I; AHA/ACC level of evidence: B). Regarding newer risk markers after quantitative risk assessment, the guidelines state the following: If, after quantitative risk assessment, a risk-based treatment decision is uncertain, assessment of 1 of the following family history, hs-crp, CAC [coronary artery calcium] score, or ABI [ankle-brachial index] may be considered to inform treatment decision-making (class of recommendation: IIb; level of evidence: B). The guidelines do not recommend other novel cardiac risk factors or panels of cardiac risk factors. U.S. Preventive Services Task Force Recommendations No recommendations specific to the use of cardiovascular risk panels were identified. In 2009, USPSTF made the following recommendation about using nontraditional risk factors in coronary heart disease risk assessment: The U.S. Preventive Services Task Force (USPSTF) concludes that the evidence is insufficient to assess the balance of benefits and harms of using the nontraditional risk factors discussed in this statement to screen asymptomatic men and women with no history of CHD to prevent CHD events (select "Clinical Considerations" for suggestions for practice when evidence is insufficient). (Grade: I) The nontraditional risk factors included in this recommendation are hscrp, ABI, leukocyte count, fasting blood glucose level, periodontal disease, carotid intima-media thickness, coronary artery calcification score on electron beam computed tomography, homocysteine level, and lipoprotein (a) level. Key Words: Cardiovascular risk panel, cardiovascular disease, CAD, Health Diagnostics Cardiac Risk Panel, Genova Diagnostics CV Health Plus Genomics Panel, Genova Diagnostics CV Health Plus Panel, Cleveland HeartLab CVD Inflammatory Profile, Applied Genetics Cardiac Panel, Genetiks Genetic Diagnosis and Research Center Cardiovascular Risk Panel, Singulex Cardiac-Related Test Panels, Singulex Cardiometabolic Panel, WellnessFX Premium Approved by Governing Bodies: Multiple assay methods for cardiac risk marker components, such as lipid panels and other biochemical assays, have clearance for marketing through the FDA 510(k) process. Other components of testing panels are laboratory-developed tests. Clinical laboratories may develop and validated tests in-house and market them as laboratory services; laboratorydeveloped tests (LDTs) must meet the general regulatory standards of Clinical Laboratory Page 9 of 12

10 Improvement Act (CLIA). Laboratories that offer LDTs must be licensed by CLIA for highcomplexity testing. To date, the FDA has chosen not to require any regulatory review of this test. Benefit Application: Coverage is subject to member s specific benefits. Group specific policy will supersede this policy when applicable. ITS: Home Policy provisions apply. FEP: Special benefit consideration may apply. Refer to member s benefit plan. FEP does not consider investigational if FDA approved and will be reviewed for medical necessity. Coding: CPT Codes: There are no specific codes for cardiovascular risk testing by panels. If the specific analyte/gene mutation/biomarker is listed in CPT codes * and the specific CPT code would be reported. If the specific analyte/gene mutation/biomarker is not listed in the more specific CPT codes, unlisted codes 81599, or would be reported. * Code is out of numerical sequence. See CPT codes for this code. For Secretory type II phospholipase: 0423T Secretory type II phospholipase A2(sPLA2-IIA) (Effective 01/01/2016) References: 1. Brotman DJ, Walker E, Lauer MS et al. In search of fewer independent risk factors. Archives of internal medicine 2005; 165(2): Cho S, Lee SH, Park S, et al. The additive value of multiple biomarkers in prediction of premature coronary artery disease. Acta Cardiol. Apr 2015; 70(2): D'Agostino RB, Sr., Grundy S, Sullivan LM et al. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA: the journal of the American Medical Association 2001; 286(2): Emerging Risk Factors Collaboration, Di Angelantonio E, Gao P et al. Lipid-related markers and cardiovascular disease prediction. JAMA: the journal of the American Medical Association 2012; 307(23): Goff, Jr DC, Lloyd-Jones DM, Bennett G et al ACC/AHA Guideline on the Assessment of Cardiovascular Risk: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation, November 12, 2013 [published online before print] accessed online at circ.ahajournals.org/content/early/2013/11/11/01.cir Gottlieb SS, Harris K, Todd J, et al. Prognostic significance of active and modified forms of endothelin 1 in patients with heart failure with reduced ejection fraction. Clin Biochem. Mar 2015; 48(4-5): Page 10 of 12

11 7. Greenland P, Alpert JS, Beller GA et al 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. Journal of the American College of Cardiology 2010; 56(25):e Goff DC, Lloyd-Jones DM, Bennett G, et al ACC/AHA Guideline on the Assessment of Cardiovascular Risk: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. November 12, Greisenegger S, Segal HC, Burgess AI, et al. Biomarkers and mortality after transient ischemic attack and minor ischemic stroke: population-based study. Stroke. Mar 2015;46(3): Helfand M, Buckley DI, Freeman M et al. Emerging risk factors for coronary heart disease: a summary of systematic reviews conducted for the U.S. Preventive Services Task Force. Annals of internal medicine 2009; 151(7): Lara J, Cooper R, Nissan J, et al. A proposed panel of biomarkers of healthy ageing. BMC Med. 2015; 13: Patterson CC, Blankenberg S, Ben-Shlomo Y, et al. Which biomarkers are predictive specifically for cardiovascular or for non-cardiovascular mortality in men? Evidence from the Caerphilly Prospective Study (CaPS). Int J Cardiol. Dec ; 201: Paynter NP, Chasman DI, Pare G et al. Association between a literature-based genetic risk score and cardiovascular events in women. JAMA: the journal of the American Medical Association 2010; 303(7): Ridker PM, Buring JE, Rifai N et al. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA: the journal of the American Medical Association 2007; 297(6): Schoe A, Schippers EF, Ebmeyer S, et al. Predicting mortality and morbidity after elective cardiac surgery using vasoactive and inflammatory biomarkers with and without the EuroSCORE model. Chest. Nov 2014; 146(5): Singulex. Clinical Lab Testing Services: Enabling proactive cardiovascular health management. 2015; // 17. van Holten TC, Waanders LF, de Groot PG, et al. Circulating biomarkers for predicting cardiovascular disease risk; a systematic review and comprehensive overview of metaanalyses. PLoS One. 2013; 8(4):e WellnessFX. Premium: The Deluxe Deep Dive. 2015; // 19. Wilsgaard T, Mathiesen EB, Patwardhan A, et al. Clinically significant novel biomarkers for prediction of first ever myocardial infarction: the Tromso Study. Circ Cardiovasc Genet. Apr 2015; 8(2): Zethelius B, Berglund L, Sundstrom J et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. The New England journal of medicine 2008; 358(20): Policy History: Medical Policy Panel, September 2013 Medical Policy Group, October 2013 (1): New policy Medical Policy Administration Committee, October 2013 Page 11 of 12

12 Available for comment October 3 through November 16, 2013 Medical Policy Group, November 2013 (1) removal of specific laboratory names and clarification of what a CV risk panel may include in the Description section; added clarification for what a simple lipid panels consists of in Policy section; new 2013 ACC/AHA guidelines for cardiovascular risk assessment added to Key Points and References; no change to intent of policy Medical Policy Administration Committee, December 2013 Available for comment December 7, 2013 through January 20, 2014 Medical Policy Group, November 2014: 2015 Coding Updates Code is out of sequence. It is now listed in CPT code range Medical Policy Panel, November 2014 Medical Policy Group, November 2014 (1): Updates to Description, Key Points, Approved Governing Bodies, and References. Also added code range and unlisted CPT codes to coding section. No change to policy statement, but added This policy does not address the use of panels of biomarkers in the diagnosis of acute myocardial infarction. no change in coverage intent. Medical Policy Group, November 2015: 2016 Annual Coding Update. Added CPT code 0423T to coding section. Medical Policy Panel, November 2015 Medical Policy Group, December 2015 (3): Updates to Description, Key Points, Key Words, Approved by Governing Bodies, and References; no change in policy statement. This medical policy is not an authorization, certification, explanation of benefits, or a contract. Eligibility and benefits are determined on a caseby-case basis according to the terms of the member s plan in effect as of the date services are rendered. All medical policies are based on (i) research of current medical literature and (ii) review of common medical practices in the treatment and diagnosis of disease as of the date hereof. Physicians and other providers are solely responsible for all aspects of medical care and treatment, including the type, quality, and levels of care and treatment. This policy is intended to be used for adjudication of claims (including pre-admission certification, pre-determinations, and pre-procedure review) in Blue Cross and Blue Shield s administration of plan contracts. Page 12 of 12

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