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Guideline Summary
Guideline Title
Recommendations from the EGAPP Working Group: genomic profiling to assess cardiovascular risk to improve cardiovascular health.
Bibliographic Source(s)
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 Dec;12(12):839-43. [18 references] PubMed External Web Site Policy
Guideline Status

This is the current release of the guideline.

Scope

Disease/Condition(s)
  • Cardiovascular disease (CVD), including myocardial infarction, stroke
  • Conditions that are risk factors for development of CVD, including:
    • Hypertension
    • Dyslipidemia
    • Obesity
    • Diabetes
    • Smoking
    • Physical inactivity

Note: This guideline does not apply to patients with known preexisting CVD.

Guideline Category
Evaluation
Prevention
Risk Assessment
Technology Assessment
Clinical Specialty
Cardiology
Family Practice
Internal Medicine
Medical Genetics
Preventive Medicine
Intended Users
Advanced Practice Nurses
Health Care Providers
Health Plans
Managed Care Organizations
Physician Assistants
Physicians
Utilization Management
Guideline Objective(s)

To address the key question of whether using available "cardiogenomic profiling" leads to improved health outcomes (e.g., reduction in rates of myocardial infarction and stroke) and whether profiling is useful in medical or personal decision making

Target Population

The general population of adults without known preexisting cardiovascular disease (CVD), regardless of family history

Interventions and Practices Considered

Genomic profiling for cardiovascular disease risk assessment was considered but not recommended.

Major Outcomes Considered

Analytic Validity

Analytic specificity and sensitivity of individual gene variant testing

Clinical Validity

  • Area under the receiver-operator curve
  • Odds ratio of a tested variant correlating with heart disease or stroke

Clinical Utility

Benefit vs. harm of adding genetic testing to traditional risk factor assessment

Methodology

Methods Used to Collect/Select the Evidence
Hand-searches of Published Literature (Primary Sources)
Hand-searches of Published Literature (Secondary Sources)
Searches of Electronic Databases
Searches of Unpublished Data
Description of Methods Used to Collect/Select the Evidence

Note from the National Guideline Clearinghouse (NGC): Consultants from Women & Infants Hospital, Department of Pathology and Laboratory Medicine, with experience in evidence review of genetic tests were contracted by the Office of Public Health Genomics at the Centers for Disease Control and Prevention to perform the targeted review with the assistance of four Evaluation of Genomic Applications in Practice and Prevention (EGAPP) staff members and other outside consultants. Guidance was provided by a Technical Expert Panel. (See the "Availability of Companion Documents" field.)

Key Questions Relating to the Analytic Framework

  1. Does the use of "cardiogenomic profiling" lead to improved outcomes for the patient/consumer, or is it useful in medical or personal decision making? (Overarching question)
  2. What is known about the analytic validity of tests that identify variations in genes associated with "heart" or cardiovascular health, including the analytic sensitivity and specificity, assay robustness (e.g., failure rates and resistance to changes in variables such as sample quality), and other factors?
  3. What is the clinical validity of cardiogenomic profiles, including clinical sensitivity and specificity and positive and negative predictive value?
    1. What is the strength of association of cardiovascular health outcomes with the presence of specific gene variants (e.g., odds ratios)?
    2. How well does this testing alone predict specific cardiovascular outcomes (e.g., myocardial infarction [MI] and stroke)?
    3. How well does this testing in combination with other cardiovascular disease (CVD) testing (e.g., cholesterol) predict specific cardiovascular outcomes?
    4. Do other factors (e.g., race/ethnicity, family history, smoking, diet, exercise level, and other conditions) affect the clinical validity of the testing?
  4. What are the issues relating to the use of cardiogenomic profiles in the designated populations and what is the impact on patient/consumer outcomes?
    1. What are the management options for patients/consumers based on cardiogenomic profile results in a medical model vs. a direct-to-consumer (DTC) model and how would they differ from routine health messages?
    2. How could the results of cardiogenomic profiling in the general population impact health behaviors or inform decision making by patients and their healthcare providers that affect outcomes?
    3. In what ways could the use of cardiogenomic profiling in the designated populations impact clinical outcomes (e.g., morbidity/mortality)
    4. What is known about other contextual issues, such as cost-effectiveness, likelihood of behavioral change, and family history considerations?
  5. What are the potential harms associated with cardiogenomic profiling (e.g., marketing direct to consumers, distress or stigma for a "poor prognosis" result, misinterpretation of results leading to excessive or inadequate treatment, and exploitation of hypervigilant people).

Data Sources for Analytic Validity

PubMed searches were performed for the alleles and single nucleotide polymorphisms (SNPs) (e.g., as "AGTR1" or "AGTR1 genotyping") and specific terms (e.g., "analytic validity" and "clinical test"). Gray literature searches (e.g., company and genetic testing web sites) were conducted to collect information on laboratories offering testing for these markers and the methodologies used. When these sources were not sufficient, the companies offering heart health genomic panels were contacted for information, first by an e-mail questionnaire and then by phone (for those not responding).

Clinical Validity Literature Search

A comprehensive review of all gene/CVD associations was determined by the EGAPP Working Group (EWG) to be outside the scope of the review, which was limited to (1) addressing two major CVD outcomes groupings (i.e., coronary heart disease [CHD] and stroke), (2) examining only those genes/polymorphisms included in cardiogenomic panels available in April 2008, and (3) using existing high-quality meta-analyses whenever possible. Literature searches were conducted using HuGE Navigator v1.3 after crosschecking a subset of genes by a PubMed search. Specific search strategies for each gene are contained in Appendix, Supplemental Digital Content 1 External Web Site Policy. Reference lists of retrieved publications were examined for relevant studies. Searches were performed between June 2008 and January 2009. One investigator had primary responsibility for each gene, and results were reviewed by another. Discrepancies were resolved by discussion.

Criteria for Inclusion of Studies for Clinical Validity

To be included, a publication needed to be in English and include information about whites. The outcome needed to be a primary CVD event, heart disease, or stroke. Sufficient data needed to be present to express the effect size as an odds ratio with confidence intervals. Existing meta-analyses were used preferentially, but structured summaries of original publications were created when no suitable meta-analyses were found.

Clinical Utility

A systematic review of the literature on the clinical utility of the 29 markers was not undertaken. Rather, PubMed searches focused on identifying potential benefits and harms associated with addition of CVD-associated alleles/SNPs to existing risk assessment algorithms based on conventional risk factors and on the potential for genetic information to motivate behavioral change. Articles from the clinical validity review were also reviewed for information related to clinical utility.

Number of Source Documents

Not stated

Methods Used to Assess the Quality and Strength of the Evidence
Weighting According to a Rating Scheme (Scheme Not Given)
Rating Scheme for the Strength of the Evidence

Not stated

Methods Used to Analyze the Evidence
Meta-Analysis
Review of Published Meta-Analyses
Systematic Review with Evidence Tables
Description of the Methods Used to Analyze the Evidence

Note from the National Guideline Clearinghouse (NGC): Consultants from Women & Infants Hospital, Department of Pathology and Laboratory Medicine, with experience in evidence review of genetic tests were contracted by the Office of Public Health Genomics at the Centers for Disease Control and Prevention to perform the targeted review with the assistance of four Evaluation of Genomic Applications in Practice and Prevention (EGAPP) staff members and other outside consultants. Guidance was provided by a Technical Expert Panel. (See the "Availability of Companion Documents" field.)

An analytic framework and key questions (see the "Description of Methods Used to Collect/Select the Evidence" field and Figure 1 in the original guideline document) were developed and refined by the EGAPP Working Group (EWG) and Technical Expert Panel members, with support from EGAPP staff. The review focuses on clinical validity but addresses the limited information available on analytic validity and considers proposed measures of clinical utility in clinical practice and in direct-to-consumer settings. Standard methods used include systematic search criteria for identification of published and gray literature; application of inclusion/exclusion criteria; abstraction of data; meta-analysis; assessment of the quality of individual studies; and overall strength of evidence. Limiting key questions and truncating search strategies (e.g., targeted gray literature searches) are two common methods in targeted reviews. In reviewing the available evidence, questions from the ACCE (Analytic validity, Clinical validity, Clinical utility and Ethical, Legal and Social Implication) review framework were often used to identify and organize the specific information needed to address the key questions.

Data Analyses for Clinical Validity

Summary odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were derived using a random-effects model (Comprehensive Meta-analysis, Version 2, Englewood, NJ), from the original source (published meta-analysis), from a reanalysis of the reported data, or from a new literature summary. The preferred method for determining the summary ORs was to compare wild-type individuals (no at-risk alleles) with heterozygotes (one at-risk allele) and then separately compare wild type with homozygotes (two at-risk alleles). Other models were also used. For example, when the frequency of the at-risk allele was very low, heterozygotes and homozygotes were combined and compared against the wild type. Some existing meta-analyses included formal assessment of heterogeneity, usually a chi-squared-based Q statistic. This was converted to the I2 statistic for ease of interpretation and comparison between studies. Some also included a formal examination of publication bias or stratified the summary OR by sample size. If sufficient data were available, potential publication bias was looked for.

Evaluating Credibility of the Cumulative Evidence for a Gene-Disease Association

In 2007, a consensus group recommended evaluation guidelines to assess the cumulative evidence provided by genetic association studies. These "Venice" criteria focus on amount of evidence, replication of evidence, and protection from bias. See the "Rating Scheme for the Strength of the Evidence" field.

There is no generally accepted process to revise the effect size estimate, if credibility is rated as moderate or low due to potential biases (e.g., publication bias). To provide an indication of the extent of potential change in effect size, we performed a cumulative effects analysis for combinations having a minimum of six studies (three of which included 500 or more participants). The analysis adds one study at a time, from smallest to largest confidence interval, to create multiple estimates for the summary OR. If the range of these cumulative ORs is wide, potential for bias exists. The range was defined as the difference between the final cumulative OR and the first stable estimate (the third of three consecutive estimates all within 10% of each other). Usually, but not always, this occurred within the first three large studies. The ranges <0.1, 0.1–0.19, and ≥0.2 were considered small, medium, and large ranges, respectively.

Combining CVD Genetic Markers

As a way to set a reasonable upper limit on the effect size of several genetic markers, a multiplicative model was chosen that assumes each marker is an independent predictor of CVD risk. Before multiplication, the ORs were adjusted, so that there was no overall impact on CVD risk. For example, if an at-risk genotype was present in 10% of the population and was associated with a summary OR of 1.1, an OR of 1.09 would be used for the 10% at risk and 0.99 for the remaining 90%. A Monte Carlo simulation generated 100,000 individuals with, and 100,000 without, heart disease (or stroke) with their associated cumulative ORs. When multiple variants were available for a given gene, the one with the strongest evidence was used. If two variants had similar credibility, the one with the largest impact (effect size time × at risk proportion) was used.

Methods Used to Formulate the Recommendations
Expert Consensus
Description of Methods Used to Formulate the Recommendations

Note from the National Guideline Clearinghouse (NGC): Consultants from Women & Infants Hospital, Department of Pathology and Laboratory Medicine, with experience in evidence review of genetic tests were contracted by the Office of Public Health Genomics at the Centers for Disease Control and Prevention (CDC) to perform the targeted review with the assistance of four Evaluation of Genomic Applications in Practice and Prevention (EGAPP) staff members and other outside consultants. Guidance was provided by a Technical Expert Panel. (See the "Availability of Companion Documents" field.)

EGAPP is a project developed by the Office of Public Health Genomics at the CDC to support a rigorous, evidence-based process for evaluating genetic tests and other genomic applications that are in transition from research to clinical and public health practice in the United States. The EGAPP Working Group (EWG) commissioned evidence review was contracted by the Office of Public Health Genomics and performed by a collaboration of external consultants and four EGAPP staff members. A Technical Expert Panel that included four EWG members and two additional consultants provided expert guidance during the course of the review. Two main groups of outcomes were defined based on the International Statistical Classification of Diseases and Related Health Problems (I00–I99). The first group, coronary heart disease (CHD), includes coronary artery disease, ischemic heart disease, and myocardial infarction. The second group, stroke, includes intracerebral and subarachnoid hemorrhage, ischemic stroke, and other diseases (e.g., cerebral infarction and occlusion/stenosis of cerebral arteries). In addition to the larger review, a peer-reviewed article on 9p21 has been published with detailed information available (see "Availability of Companion Documents"). The final EWG recommendation statement was formulated based on magnitude of effect, certainty of evidence, and consideration of contextual factors.

An analytic framework and key questions were developed and refined by the EWG and Technical Expert Panel members, with support from EGAPP staff. The review focuses on clinical validity but addresses the limited information available on analytic validity and considers proposed measures of clinical utility in clinical practice and in direct-to-consumer settings.

In reviewing the available evidence, questions from the ACCE (Analytic validity, Clinical validity, Clinical utility and Ethical, legal and social implication) review framework were often used to identify and organize the specific information needed to address the key questions. The draft report was then revised in response to reviewers' comments, and the final manuscript was submitted to the EWG.

Rating Scheme for the Strength of the Recommendations

Recommendations Based on Certainty of Evidence, Magnitude of Net Benefit, and Contextual Issues

High or Moderate Recommend for:
  • If the magnitude of net benefit is Substantial, Moderate, or Small, unless additional considerations warrant caution.
  • Consider the importance of each relevant contextual factor and its magnitude or finding.
Recommend against:
  • If the magnitude of net benefit is Zero or there are net harms.
  • Consider the importance of each relevant contextual factor and its magnitude or finding.
Low Insufficient evidence:
  • If the evidence for clinical utility or clinical validity is insufficient in quantity or quality to support conclusions or make a recommendation.
  • Consider the importance of each contextual factor and its magnitude or finding.
  • Determine whether the recommendation should be Insufficient (neutral), Insufficient (encouraging), or Insufficient (discouraging).
  • Provide information on key information gaps to drive a research agenda.

Teutsch SM, Bradley LA, Palomaki GE, Haddow JE, Piper M, Calonge N, Dotson WD, Douglas MP, Berg AO; EGAPP Working Group. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative: methods of the EGAPP Working Group. Genet Med. 2009 Jan;11(1):3-14.

Cost Analysis

A formal cost analysis was not performed and published cost analyses were not reviewed.

Method of Guideline Validation
Not stated
Description of Method of Guideline Validation

Not applicable

Recommendations

Major Recommendations

Summary of Recommendations

The Evaluation of Genomic Applications in Practice and Prevention Working Group (EWG) found insufficient evidence to recommend testing for the 9p21 genetic variant or 57 other variants in 28 genes (listed in Table 1 in the original guideline document) to assess risk for cardiovascular disease (CVD) in the general population, specifically heart disease and stroke. The EWG found that the magnitude of net health benefit from use of any of these tests alone or in combination is negligible. The EWG discourages clinical use unless further evidence supports improved clinical outcomes. Based on the available evidence, the overall certainty of net health benefit is deemed "Low."

Rationale

It has been suggested that an improvement in CVD risk classification (adjusting intermediate risk of CVD into high- or low-risk categories) might lead to management changes (e.g., earlier initiation or higher rates of medical interventions, or targeted recommendations for behavioral change) that improve CVD outcomes. In the absence of direct evidence to support this possibility, this review sought indirect evidence aimed at documenting the extent to which genomic profiling alters CVD risk estimation, alone and in combination with traditional risk factors, and the extent to which risk reclassification improves health outcomes.

Analytic Validity

Assay-related evidence on available genomic profiling tests was deemed inadequate. However, based on existing technologies that have been or may be used and on data from two of the companies performing such testing, the analytic sensitivity and specificity of tests for individual gene variants might be at least satisfactory.

Clinical Validity

Twenty-nine gene candidates were evaluated, with 58 different gene variant/disease associations (see Table 1 in the original guideline document). Evidence on clinical validity was rated inadequate for 34 of these associations (59%) and adequate for 23 (40%). Inadequate grades were based on limited evidence, poor replication, existence of possible biases, or combinations of these factors. For heart disease (25 combined associations) and stroke (13 combined associations), profiling provided areas under the receiver operator characteristics curve of 66% and 57%, respectively. Only the association of 9p21 variants with heart disease had convincing evidence of a per-allele odds ratio of between 1.2 and 1.3; this was the highest effect size for any variant/disease combination with at least adequate evidence. Although the 9p21 association seems to be independent of traditional risk factors, there is adequate evidence that the improvement in risk prediction is, at best, small.

Clinical Utility

Clinical utility was not formally evaluated in any of the studies reported to date, including for 9p21. As a result, no evidence was available on the balance of benefits and harms. Also, there was no direct evidence available to assess the health benefits and harms of adding these markers to traditional risk factors (e.g., Framingham Risk Score). However, the estimated additional benefit from adding genomic markers to traditional risk factors was found to be negligible.

Contextual Issues

Prevention of CVD is a public health priority. Improvements in outcomes associated with genomic profiling could have important impacts. Traditional risk factors such as those used in the Framingham Risk Scores have an advantage in clinical screening and risk assessment strategies because they measure the actual targets for therapy (e.g., lipid levels and blood pressure). To add value, genomic testing should lead to better outcomes than those achievable by assessment and treatment of traditional risk factors alone. Some issues important for clinical utility remain unknown, such as the biological mechanism underlying the most convincing marker's (9p21) association with CVD; the level of risk that changes intervention; whether long-term disease outcomes will improve; how individuals ordering direct to consumer tests will understand/respond to test results and interact with the health care system; and whether direct to consumer testing will motivate behavior change or amplify potential harms.

Clinical Considerations

Definitions Used by Evaluation of Genomic Applications in Practice and Prevention

  • Analytic validity refers to a test's ability to accurately and reliably measure the genotype or analyte of interest.
  • Clinical validity defines the ability of the test to accurately and reliably identify or predict the intermediate or final outcomes of interest. This is usually reported as clinical sensitivity and specificity.
  • Clinical utility defines the balance of benefits and harms associated with using the test in practice, including improvement in measurable clinical outcomes and added value in clinical management and decision making compared with not using the test.

Patient Population Under Consideration

These recommendations apply to the general population of adults without known preexisting cardiovascular disease (CVD), regardless of family history.

Considerations for Practice

These tests have become available through primary care clinician offices and through direct to consumer marketing. Patients may ask about such tests or bring results of completed tests to their physicians for advice or consultation. Physicians should routinely consider well-established recommendations for cardiac risk assessment in the primary care setting (e.g., smoking, blood pressure, and lipid screening). In addition, all patients should be consistently counseled regarding appropriate physical activity and nutrition behaviors to reduce cardiac risk. Based on the available evidence, it is unclear how the results of genomic profiling should modify patient care to improve outcomes.

Contextual Issues Important to the Recommendation

  • CVD is an important public health problem and improvements in outcomes associated with genomic testing could have important impacts.
  • The traditional modifiable CVD risk factors, such as those used in the Framingham Risk Scores, have an advantage in clinical screening and risk assessment strategies because they measure the actual targets for therapy (e.g., lipid levels and blood pressure).
  • It is important to recognize that there may be differences in the utility of genomic markers in predicting coronary risk compared with stroke risk. This is true for the traditional risk factors used in Framingham, which has better utility for coronary artery disease than for stroke. Ultimately, it would be preferable to have research that evaluates and reports the utility for each condition separately.
  • To be useful, genomic testing should provide demonstrable improvement on the predictive value of traditional risk factors.
  • The genetic mechanism of some candidate gene variants (e.g., 9p21) is unknown.

Definitions:

Recommendations Based on Certainty of Evidence, Magnitude of Net Benefit, and Contextual Issues

High or Moderate Recommend for:
  • If the magnitude of net benefit is Substantial, Moderate, or Small, unless additional considerations warrant caution.
  • Consider the importance of each relevant contextual factor and its magnitude or finding.
Recommend against:
  • If the magnitude of net benefit is Zero or there are net harms.
  • Consider the importance of each relevant contextual factor and its magnitude or finding.
Low Insufficient evidence:
  • If the evidence for clinical utility or clinical validity is insufficient in quantity or quality to support conclusions or make a recommendation.
  • Consider the importance of each contextual factor and its magnitude or finding.
  • Determine whether the recommendation should be Insufficient (neutral), Insufficient (encouraging), or Insufficient (discouraging).
  • Provide information on key information gaps to drive a research agenda.

Teutsch SM, Bradley LA, Palomaki GE, Haddow JE, Piper M, Calonge N, Dotson WD, Douglas MP, Berg AO; EGAPP Working Group. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative: methods of the EGAPP Working Group. Genet Med. 2009 Jan;11(1):3-14.

Clinical Algorithm(s)

None provided

Evidence Supporting the Recommendations

Type of Evidence Supporting the Recommendations

The type of supporting evidence is not specifically stated for each recommendation.

Benefits/Harms of Implementing the Guideline Recommendations

Potential Benefits

Prevention of unnecessary genotyping for the risk of cardiovascular disease

Potential Harms

Not stated

Qualifying Statements

Qualifying Statements
  • This recommendation statement is a product of the independent Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group. Although the Centers for Disease Control and Prevention (CDC) provides support to the EGAPP Working Group, including staff support in the preparation of this document, recommendations made by the EGAPP Working Group should not be construed as official positions of the CDC or the U.S. Department of Health and Human Services.
  • The EGAPP Working Group found the research literature insufficient, with important gaps in knowledge.
  • Most analyses have been restricted to whites, usually of European descent; results in other race/ethnic groups might differ in effect size, prevalence of at-risk genotypes, variant of interest, or some combination of these. Some of the gene-disease associations under review involve a small effect size for relatively uncommon genotypes. The current genome-wide association (GWA) studies may be underpowered or limited in single-nucleotide polymorphisms (SNPs) coverage of the genome to reliably confirm or reject real associations in the genes reviewed. The finding of weak associations between gene variants and cardiovascular disease (CVD) outcomes does not exclude possible roles for these variants in the pathway of lipid metabolism or in other pathways associated with CVD. Many studies have confirmed both gene/lipid associations and that the variants identified are functional. However, the function of the marker with the highest credibility, 9p21, is not well described. Understanding its role in disease etiology might result in an improved understanding of the process, leading to improved treatments or prevention activities. Publication bias seems to be an important problem in the literature regarding genomic tests for CVD. Finally, the genes included in this study were, on average, described more than five years ago, with the newest one (9p21) first reported in 2007. Since that time, one GWA study has reported new markers that may also have strong credibility, once confirmatory studies have been reported.

Implementation of the Guideline

Description of Implementation Strategy

An implementation strategy was not provided.

Institute of Medicine (IOM) National Healthcare Quality Report Categories

IOM Care Need
Staying Healthy
IOM Domain
Effectiveness
Patient-centeredness

Identifying Information and Availability

Bibliographic Source(s)
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 Dec;12(12):839-43. [18 references] PubMed External Web Site Policy
Adaptation

Not applicable: The guideline was not adapted from another source.

Date Released
2010 Dec
Guideline Developer(s)
Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group - Independent Expert Panel
Source(s) of Funding

United States Government

Guideline Committee

The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group

Composition of Group That Authored the Guideline

Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group: Chair: Alfred O. Berg, MD, MPH (Department of Family Medicine, University of Washington); Members: Jeffrey Botkin, MD, MPH (University of Utah); Ned Calonge, MD, MPH (Colorado Department of Public Health and Environment); Doug Campos-Outcalt, MD, MPA (Department of Family-Community Medicine, University of Arizona College of Medicine); James E. Haddow, MD (Department of Pathology and Laboratory Medicine, The Warren Alpert Medical School of Brown University); Maxine Hayes, MD, MPH (Washington State Department of Health); Celia Kaye, MD, PhD (Department of Pediatrics, University of Colorado School of Medicine); Roger D. Klein, MD, JD (BloodCenter of Wisconsin; Medical College of Wisconsin); Kenneth Offit, MD, MPH (Clinical Genetics Service, Memorial Sloan-Kettering Cancer Center); Stephen G. Pauker, MD, MACP, FACC, ABMH (Division of Clinical Decision Making, Informatics and Telemedicine, Department of Medicine, Tufts Medical Center); Margaret Piper, PhD, MPH (Blue Cross/Blue Shield Association Technology Evaluation Center); Carolyn Sue Richards, PhD, FACMG (Oregon Health & Science University); Joan A. Scott, MS, CGC (Genetics and Public Policy Center, Johns Hopkins University); Ora L. Strickland, PhD, DSc (Hon.), RN, FAAN (Nell Hodgson Woodruff School of Nursing, Emory University); Steven Teutsch, MD, MPH (Los Angeles County Department of Public Health); David L. Veenstra, PharmD, PhD (Pharmaceutical Outcomes Research and Policy Program, and Institute for Public Health Genetics, University of Washington)

Financial Disclosures/Conflicts of Interest

Steven Teutsch is a former employee and an option holder in Merck & Co., Inc.

Guideline Status

This is the current release of the guideline.

Guideline Availability

Electronic copies: Available from the Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Web site External Web Site Policy.

Also available in EPUB for eBook devices from the Genetics in Medicine Journal Web site External Web Site Policy.

Availability of Companion Documents

The following are available:

Patient Resources

None available

NGC Status

This NGC summary was completed by ECRI Institute on May 3, 2011. The information was verified by the guideline developer on July 19, 2011.

Copyright Statement

This NGC summary is based on the original guideline: Recommendations from the EGAPP Working Group: Genomic profiling to assess cardiovascular risk to improve cardiovascular health. Genet Med 2010 Dec;12(12):839-43. ©American College of Medical Genetics. Reprinted with permission of Lippincott Williams & Wilkins.

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