A Pilot Project Exploring the Impact of Whole Genome Sequencing in Healthcare

The MedSeq Project Pilot Study: Integrating Whole Genome Sequencing Into the Practice of Clinical Medicine

The MedSeq™ Project seeks to explore the impact of incorporating information from a patient's whole genome sequence into the practice of clinical medicine. In the extension phase of MedSeq we are attempting increase our participant diversity by increasing targeted enrollment of African/African American patient participants.

Study Overview

• Status: Completed
• Conditions: Healthy Adults (Full Study and Extension Phase); Hypertrophic Cardiomyopathy or Dilated Cardiomyopathy
• Intervention / Treatment: Other: Family History + Whole Genome Sequencing; Other: Family History Only

Detailed Description

Whole genome sequencing (WGS) and whole exome sequencing (WES) services are currently available to and are being utilized by physicians and their patients in both research and clinical settings. The widespread availability and use of WGS and WES in the practice of clinical medicine is imminent. In the very near future, sequencing of individual genomes will be inexpensive and ubiquitous, and patients will be looking to the medical establishment for interpretations, insight and advice to improve their health. Developing standards and procedures for the use of WGS information in clinical medicine is an urgent need, but there are numerous obstacles related to integrity and storage of WGS data, interpretation and responsible clinical integration. MedSeq™ seeks to develop a process to integrate WGS into clinical medicine and explore the impact of doing so.

We believe that WGS will be used in many ways, including two distinct and complementary situations. In generally healthy patients, physicians will use the results of WGS to derive insight into future health risks and inform prevention and surveillance efforts, a category we refer to as General Genomic Medicine. In patients presenting with a family history or symptoms of a disease, physicians will use the results of WGS to interrogate particular sets of genes known to be associated with the disease in question, a category we refer to as Disease-Specific Genomic Medicine.

Beginning in fall 2012, we will enroll 10 primary care physicians and 100 of their healthy middle-aged patients to evaluate the use of General Genomic Medicine, and 10 cardiologists and 100 of their patients presenting with hypertrophic cardiomyopathy (HCM) or dilated cardiomyopathy (DCM) to evaluate the use of Disease-Specific Genomic Medicine. We will randomize physicians and their patients within each of the above models to receive clinically meaningful information derived from WGS versus current standard of care without the use of WGS.

MedSeq™ is comprised of three distinct but highly collaborative projects. Project 1 will enroll physicians and patients into the protocol, educate the physicians on basic genomic principles and safely monitor the use of genomic information in clinical practice. Project 2 will use a WGS analysis/interpretation pipeline to generate a genome report on each patient randomized to receive WGS in this protocol. Project 3 will examine preferences and motivations of physicians and patients enrolled, evaluate the flow and utilization of genomic information within the clinical interactions, and assess understanding, behavior, medical consequences and healthcare costs associated with the use of WGS in these models of medical practice.

In an extension phase of the study, we will 1) recruit approximately 10-15 patient-participants who self-identify as African or African American, whose physicians deem to be healthy. All will be placed in the whole genome-sequencing arm of the study. They will undergo the same activities as traditional MedSeq participants except for randomization. 2) We will conduct a targeted phenotype assessment on MedSeq Project patient-participants who are identified to have a monogenic finding. We plan to perform additional analysis by reviewing their medical records and looking specifically with their variant in mind to see if features associated with the variants were known prior to the study or were identified by further testing or by their physical during the course of the study.

This initiative will significantly accelerate the use of genomics in clinical medicine by creating and safely testing novel methods for integrating information from WGS into physicians' care of patients.

• Study Type: Interventional
• Enrollment (Actual): 213
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Massachusetts
    • Boston, Massachusetts, United States, 02115
      • Brigham and Women's Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 90 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

Description

Note for Age Eligibility:

• Cardiology patients 18 Years to 90 Years OR
• Primary Care Patients 40 Years to 65 Years (Adult, Senior)

Inclusion Criteria:

Primary Care

• Generally healthy (as defined by the primary care provider) adult patients at Brigham and Women's Hospital ages 40-65. All patients must be fluent in English.

Cardiology

• Patients in the Partners Healthcare System who are 18 years or older with a diagnosis of hypertrophic cardiomyopathy (HCM) or dilated cardiomyopathy (DCM) and a family history of HCM or DCM who previously had or who are candidates for targeted HCM or DCM genetic testing through routine clinical practice within Partners. All patients must be fluent in English.

Exclusion Criteria:

Primary Care

• Patients who do not meet the above criteria. Patients with cardiac disease or a progressive debilitating illness. Patients who are pregnant or patients whose spouses/significant others are pregnant. Patients with untreated clinical anxiety or depression (as measured by a Hospital Anxiety and Depression Scale (HADS) score > 11 administered at the baseline study visit.)

Cardiology

• Patients who do not meet the above criteria. Patients with a progressive debilitating illness. Patients who are pregnant or patients whose spouses/significant others are pregnant. Patients with untreated clinical anxiety or depression (as measured by a Hospital Anxiety and Depression Scale (HADS) score > 11 administered at the baseline study visit.)

Extension Phase - Additional Inclusion Criteria

Part 1:

• Above inclusion and exclusion criteria PLUS:
• Inclusion: Self-identify as African or African American.

Part 2:

Inclusion Criteria

• MedSeq participants determined to have a monogenic finding

Exclusion Criteria

• Participants not previously enrolled in MedSeq Project
• Participants not identified to have a monogenic finding

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Family History + Whole Genome Sequencing; Doctors and their patients receive a Genome Report and an Annotated Family History Report.	Other: Family History + Whole Genome Sequencing; Doctors and their patients receive a Genome Report and a Family History report. There are two sections of the Genome Report: The General Genome Report, which include highly penetrant disease mutations, carrier status for recessive disease, and pharmacogenetic associations.; The Cardiac Risk Supplement, which contain genetic information found in the genome regarding cardiac diseases or a risk of cardiovascular diseases that can help with the care of the patient. Extension Phase: Experimental: Family History + Whole Genome Sequencing *In the main study participants are randomized to either the Experimental or Other Arm, in the Extension phase of the study all participants are in the Experimental Arm.
Active Comparator: Family History Only; Doctors and their patients receive an Annotated Family History Report only.	Other: Family History Only; Doctors and their patients receive a Family History report.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in Attitudes and Trust	Adapted measures (Hall, MA, et al. 2006) assessed participants' attitudes toward genetic information, trust of their physicians and the medical system regarding interpretation and use of genetic information. Higher scores on a 12-60 scale represent more positive attitudes and greater trust.	Change at 6-weeks post-results disclosure relative to baseline, administered approx.12.5 months after baseline
Change in Self Efficacy	Assessed through a scale developed for the Multiplex Initiative (Kaphingst, K.A., et al. 2012). Higher scores on a 0-24 scale indicate greater confidence in participants' abilities to understand genetic information.	Baseline and 6-months post-results disclosure (6 mos. follow-up administered approx. 17 months after baseline)
Change in Preferences for WGS Information	Through nine novel survey items, participants were asked about their preferences for the types of genetic testing results they would like to receive from their whole genome sequence. Scores on an 0-9 scale represent the change in the number of categories of types of genetic testing results out of 9 that participants wanted to learn about from Baseline to 6-weeks follow-up.	Baseline and 6-weeks post-disclosure (6 wks follow-up administered approx. 12.5 mos. after baseline)
Change in Perceived Health	A single-item measure assessed how participants perceived their own health on a 1-5 scale. Adapted from the SF-12 (DeSalvo KB, Qual Life Res, 2006). Higher scores indicate more positive perceptions of health at follow-up	Baseline, at the disclosure visit (about 1 hour after results disclosure, avg. 11 mos. after baseline) and 6-months post-disclosure (6 mos. follow-up follow-up administered approx. 17 months after baseline)
Change in Shared Decision Making	Changes in shared decision making were assessed through a single item adapted from the Control Preferences Scale, a measure designed to ascertain the degree of control an individual wants to assume when decisions are being made about medical treatment. Higher scores on a scale of 1-3 indicate preferences towards more equally shared decision making (Heisler et al 2003). Higher mean changes over time indicate a change in preference towards more equally shared decision making at follow-up.	Baseline and 6-weeks post-disclosure (6 wks follow-up administered approx. 12.5 mos. after baseline)
Change in Intolerance of Uncertainty	Changes in participants' tolerance for uncertainty were assessed through a short 12-item version of the Intolerance of Uncertainty Scale (Carleton, 2007). Total summed scale range is 12-60, with higher scores indicating increased negative feelings about uncertainty from baseline to follow-up.	Baseline and 6-months post-disclosure (6 mos. follow-up administered approx. 17 mos. after baseline)
Change in General Anxiety and Depression	The Hospital Anxiety and Depression Scale (HADS) scale was administered through a survey. This is a validated scale designed to assess the participants' level of depression and anxiety through Likert-type questions. Total ranges for each summed subscale, anxiety and depression, is 0-21. Any participant scoring >14 on the anxiety subscale or >16 on the depression subscale were contacted by study staff for evaluation. Higher scores indicate increased anxiety or depression from baseline to follow-up.	Baseline, at the disclosure visit (about 1 hour after results disclosure, avg. 11 mos. after baseline), 6-weeks post-disclosure and 6-months post-disclosure (6 wks. follow-up administered approx. 12.5 mos and 6 mos follow-up approx 17 mos. after baseline)
Change in Health Behaviors	Novel items that asked whether participants changed vitamin use, supplement use, medication use, diet, exercise, or "other" health behaviors. Counts and percentages represent participants who reported any health behavior changes.	6-weeks post-disclosure and 6-months post-disclosure (6 wks. follow-up administered approx. 12.5 mos. and 6 mos. follow-up approx. 17 mos. after baseline)
Information Sharing	Sharing of information was assessed by asking patients if they intended to share results with others (at the end of the disclosure visit) and if they had shared their results with others (6 months after disclosure) adapted from the Health Information National Trends Survey (HINTS).	At the disclosure visit (about 1 hour after results disclosure, avg. 11 mos. after baseline) and 6-months post-disclosure (approx. 17 mos. after baseline)
Changes in Genomic Literacy	Changes in participants' genomic literacy were measured with an 11-item measure adapted from the ClinSeq Study (Kaphingst K.A. et al. 2012) administered at baseline and 6 months post-disclosure. Items are marked as correct (1) or incorrect (0) and summed for a total scale range of 0 to 11, with higher scores indicating higher genomic literacy.	Assessing Genomic Literacy at baseline and 6-months post-disclosure (approx. 17 mos. after baseline)
Changes in Health Care Utilization	Participants' health care utilization was assessed through a combination of medical record reviews and novel and adapted measures from the Behavioral Risk Factor Surveillance System (BRFSS). Changes are assessed by comparing the number of services and procedures received in 6 months following disclosure against the number of services and procedures received in the 6 months prior to disclosure.	6 months prior to disclosure and 6-months post-disclosure (approx. 17 mos. after baseline) and 5-years post-disclosure
Change in Perceived Utility	A novel survey item asked participants to rate the usefulness of whole genome sequencing results for managing health on a 1-10 scale. Scores at 6 months were compared to scores at baseline.	At baseline and 6-months post-disclosure (approx. 17 mos. after baseline)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Psychological Impact	Psychological impact was assessed by a modified version of the Multidimensional Impact of Cancer Risk Assessment (MICRA) questionnaire. Higher scores indicated more distress related to study results.	6-weeks post-disclosure and 6-months post-disclosure (6wks. follow-up administered approx. 12.5 mos. and 6 mos. follow-up approx. 17 mos. after baseline)
Decisional Regret	Participants' satisfaction with their decision to participate in the MedSeq Project through a 5-item validated scale (Brehaut 2003). Average score computed after reversing scores of 2 negatively phrased items and converting score to range from 0-100 by subtracting 1 and multiplying by 25. Higher scores indicate greater regret.	At post-disclosure visit (about 1 hour after results disclosure, avg. 11 mos. after baseline), at 6-weeks post-disclosure, and at 6-months post-disclosure (6 wks follow-up approx. 12.5 mos. and 6 mos. follow-up approx. 17 mos. after baseline)
Understanding	A novel item assessed participants' subjective understanding of their study results on a 1-5 scale, where higher scores indicate greater subjective understanding.	At post-disclosure visit (about 1 hour after results disclosure, avg. 11 mos. after baseline), at 6-weeks post-disclosure, and at 6-months post-disclosure (6 wks follow-up approx. 12.5 mos. and 6 mos. follow-up approx. 17 mos. after baseline)
Expectations	Novel survey items asked participants about whether or not their genetic test results would be useful for specific reasons. Response options were "no," "probably not", "probably yes," and "yes." Responses of "probably yes" and "yes" were combined to simplify presentation of data.	Baseline

Collaborators and Investigators

• Sponsor: Brigham and Women's Hospital
• Collaborators: Baylor College of Medicine; Duke University; National Human Genome Research Institute (NHGRI)
• Investigators: Principal Investigator: Robert C Green, MD, MPH, Brigham and Women's Hospital

Publications and helpful links

General Publications

• Brehaut JC, O'Connor AM, Wood TJ, Hack TF, Siminoff L, Gordon E, Feldman-Stewart D. Validation of a decision regret scale. Med Decis Making. 2003 Jul-Aug;23(4):281-92. doi: 10.1177/0272989X03256005.
• Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, McGuire AL, Nussbaum RL, O'Daniel JM, Ormond KE, Rehm HL, Watson MS, Williams MS, Biesecker LG; American College of Medical Genetics and Genomics. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med. 2013 Jul;15(7):565-74. doi: 10.1038/gim.2013.73. Epub 2013 Jun 20. Erratum In: Genet Med. 2017 May;19(5):606.
• Carleton RN, Norton MA, Asmundson GJ. Fearing the unknown: a short version of the Intolerance of Uncertainty Scale. J Anxiety Disord. 2007;21(1):105-17. doi: 10.1016/j.janxdis.2006.03.014. Epub 2006 May 2.
• Evans JP, Meslin EM, Marteau TM, Caulfield T. Genomics. Deflating the genomic bubble. Science. 2011 Feb 18;331(6019):861-2. doi: 10.1126/science.1198039. No abstract available.
• Kaphingst KA, Facio FM, Cheng MR, Brooks S, Eidem H, Linn A, Biesecker BB, Biesecker LG. Effects of informed consent for individual genome sequencing on relevant knowledge. Clin Genet. 2012 Nov;82(5):408-15. doi: 10.1111/j.1399-0004.2012.01909.x. Epub 2012 Aug 7.
• Green ED, Guyer MS; National Human Genome Research Institute. Charting a course for genomic medicine from base pairs to bedside. Nature. 2011 Feb 10;470(7333):204-13. doi: 10.1038/nature09764.
• Roter D, Larson S. The Roter interaction analysis system (RIAS): utility and flexibility for analysis of medical interactions. Patient Educ Couns. 2002 Apr;46(4):243-51. doi: 10.1016/s0738-3991(02)00012-5.
• Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. Measuring numeracy without a math test: development of the Subjective Numeracy Scale. Med Decis Making. 2007 Sep-Oct;27(5):672-80. doi: 10.1177/0272989X07304449. Epub 2007 Jul 19.
• Biesecker LG. Opportunities and challenges for the integration of massively parallel genomic sequencing into clinical practice: lessons from the ClinSeq project. Genet Med. 2012 Apr;14(4):393-8. doi: 10.1038/gim.2011.78. Epub 2012 Feb 16.
• Kohane IS, Masys DR, Altman RB. The incidentalome: a threat to genomic medicine. JAMA. 2006 Jul 12;296(2):212-5. doi: 10.1001/jama.296.2.212. No abstract available. Erratum In: JAMA. 2006 Sep 27;296(12):1466.
• Khoury MJ, Berg A, Coates R, Evans J, Teutsch SM, Bradley LA. The evidence dilemma in genomic medicine. Health Aff (Millwood). 2008 Nov-Dec;27(6):1600-11. doi: 10.1377/hlthaff.27.6.1600.
• Varmus H. Ten years on--the human genome and medicine. N Engl J Med. 2010 May 27;362(21):2028-9. doi: 10.1056/NEJMe0911933. No abstract available.
• Hall MA, Camacho F, Lawlor JS, Depuy V, Sugarman J, Weinfurt K. Measuring trust in medical researchers. Med Care. 2006 Nov;44(11):1048-53. doi: 10.1097/01.mlr.0000228023.37087.cb.
• Lipkus IM. Numeric, verbal, and visual formats of conveying health risks: suggested best practices and future recommendations. Med Decis Making. 2007 Sep-Oct;27(5):696-713. doi: 10.1177/0272989X07307271. Epub 2007 Sep 14.
• Jarvik GP, Amendola LM, Berg JS, Brothers K, Clayton EW, Chung W, Evans BJ, Evans JP, Fullerton SM, Gallego CJ, Garrison NA, Gray SW, Holm IA, Kullo IJ, Lehmann LS, McCarty C, Prows CA, Rehm HL, Sharp RR, Salama J, Sanderson S, Van Driest SL, Williams MS, Wolf SM, Wolf WA; eMERGE Act-ROR Committee and CERC Committee; CSER Act-ROR Working Group; Burke W. Return of genomic results to research participants: the floor, the ceiling, and the choices in between. Am J Hum Genet. 2014 Jun 5;94(6):818-26. doi: 10.1016/j.ajhg.2014.04.009. Epub 2014 May 8.
• Biesecker LG, Green RC. Diagnostic clinical genome and exome sequencing. N Engl J Med. 2014 Jun 19;370(25):2418-25. doi: 10.1056/NEJMra1312543. No abstract available.
• Arndt AK, MacRae CA. Genetic testing in cardiovascular diseases. Curr Opin Cardiol. 2014 May;29(3):235-40. doi: 10.1097/HCO.0000000000000055.
• Hwang KB, Lee IH, Park JH, Hambuch T, Choe Y, Kim M, Lee K, Song T, Neu MB, Gupta N, Kohane IS, Green RC, Kong SW. Reducing false-positive incidental findings with ensemble genotyping and logistic regression based variant filtering methods. Hum Mutat. 2014 Aug;35(8):936-44. doi: 10.1002/humu.22587. Epub 2014 Jun 24.
• Lee IH, Lee K, Hsing M, Choe Y, Park JH, Kim SH, Bohn JM, Neu MB, Hwang KB, Green RC, Kohane IS, Kong SW. Prioritizing disease-linked variants, genes, and pathways with an interactive whole-genome analysis pipeline. Hum Mutat. 2014 May;35(5):537-47. doi: 10.1002/humu.22520. Epub 2014 Mar 6.
• Vassy JL, Green RC, Lehmann LS. Genomic medicine in primary care: barriers and assets. Postgrad Med J. 2013 Nov;89(1057):615-6. doi: 10.1136/postgradmedj-2013-132093. No abstract available.
• Berg JS, Amendola LM, Eng C, Van Allen E, Gray SW, Wagle N, Rehm HL, DeChene ET, Dulik MC, Hisama FM, Burke W, Spinner NB, Garraway L, Green RC, Plon S, Evans JP, Jarvik GP; Members of the CSER Actionability and Return of Results Working Group. Processes and preliminary outputs for identification of actionable genes as incidental findings in genomic sequence data in the Clinical Sequencing Exploratory Research Consortium. Genet Med. 2013 Nov;15(11):860-7. doi: 10.1038/gim.2013.133. Epub 2013 Oct 24. Erratum In: Genet Med. 2014 Feb;16(2):203.
• McGuire AL, Joffe S, Koenig BA, Biesecker BB, McCullough LB, Blumenthal-Barby JS, Caulfield T, Terry SF, Green RC. Point-counterpoint. Ethics and genomic incidental findings. Science. 2013 May 31;340(6136):1047-8. doi: 10.1126/science.1240156. Epub 2013 May 16. No abstract available.
• McGuire AL, McCullough LB, Evans JP. The indispensable role of professional judgment in genomic medicine. JAMA. 2013 Apr 10;309(14):1465-6. doi: 10.1001/jama.2013.1438. No abstract available.
• Rehm HL. Disease-targeted sequencing: a cornerstone in the clinic. Nat Rev Genet. 2013 Apr;14(4):295-300. doi: 10.1038/nrg3463. Epub 2013 Mar 12.
• Krier JB, Green RC. Management of incidental findings in clinical genomic sequencing. Curr Protoc Hum Genet. 2013;Chapter 9:Unit9.23. doi: 10.1002/0471142905.hg0923s77.
• MacRae CA. Action and the actionability in exome variation. Circ Cardiovasc Genet. 2012 Dec;5(6):597-8. doi: 10.1161/CIRCGENETICS.112.965152. No abstract available.
• Song T, Hwang KB, Hsing M, Lee K, Bohn J, Kong SW. gSearch: a fast and flexible general search tool for whole-genome sequencing. Bioinformatics. 2012 Aug 15;28(16):2176-7. doi: 10.1093/bioinformatics/bts358. Epub 2012 Jun 23.
• Green RC, Berg JS, Berry GT, Biesecker LG, Dimmock DP, Evans JP, Grody WW, Hegde MR, Kalia S, Korf BR, Krantz I, McGuire AL, Miller DT, Murray MF, Nussbaum RL, Plon SE, Rehm HL, Jacob HJ. Exploring concordance and discordance for return of incidental findings from clinical sequencing. Genet Med. 2012 Apr;14(4):405-10. doi: 10.1038/gim.2012.21. Epub 2012 Mar 15.
• Green RC, Rehm H, Kohane I. Clinical Genome Sequencing. Genomic and Personalized Medicine 2nd Edition: 102- 122, 2012.
• Blumenthal-Barby JS, McGuire AL, Green RC, Ubel PA. How behavioral economics can help to avoid 'The last mile problem' in whole genome sequencing. Genome Med. 2015 Jan 22;7(1):3. doi: 10.1186/s13073-015-0132-8. eCollection 2015.
• Green RC, Lautenbach D, McGuire AL. GINA, genetic discrimination, and genomic medicine. N Engl J Med. 2015 Jan 29;372(5):397-9. doi: 10.1056/NEJMp1404776. No abstract available.
• Vassy JL, Lautenbach DM, McLaughlin HM, Kong SW, Christensen KD, Krier J, Kohane IS, Feuerman LZ, Blumenthal-Barby J, Roberts JS, Lehmann LS, Ho CY, Ubel PA, MacRae CA, Seidman CE, Murray MF, McGuire AL, Rehm HL, Green RC; MedSeq Project. The MedSeq Project: a randomized trial of integrating whole genome sequencing into clinical medicine. Trials. 2014 Mar 20;15:85. doi: 10.1186/1745-6215-15-85.
• Vassy JL, McLaughlin HM, MacRae CA, Seidman CE, Lautenbach D, Krier JB, Lane WJ, Kohane IS, Murray MF, McGuire AL, Rehm HL, Green RC. A one-page summary report of genome sequencing for the healthy adult. Public Health Genomics. 2015;18(2):123-9. doi: 10.1159/000370102. Epub 2015 Jan 21. Erratum In: Public Health Genomics. 2015 Apr;18(3):191. McLaughlin, Heather L [corrected to McLaughlin, Heather M].
• Cirino AL, Lakdawala NK, McDonough B, Conner L, Adler D, Weinfeld M, O'Gara P, Rehm HL, Machini K, Lebo M, Blout C, Green RC, MacRae CA, Seidman CE, Ho CY; MedSeq Project*. A Comparison of Whole Genome Sequencing to Multigene Panel Testing in Hypertrophic Cardiomyopathy Patients. Circ Cardiovasc Genet. 2017 Oct;10(5):e001768. doi: 10.1161/CIRCGENETICS.117.001768.
• Vassy JL, Christensen KD, Schonman EF, Blout CL, Robinson JO, Krier JB, Diamond PM, Lebo M, Machini K, Azzariti DR, Dukhovny D, Bates DW, MacRae CA, Murray MF, Rehm HL, McGuire AL, Green RC; MedSeq Project. The Impact of Whole-Genome Sequencing on the Primary Care and Outcomes of Healthy Adult Patients: A Pilot Randomized Trial. Ann Intern Med. 2017 Jun 27;167(3):159-169. doi: 10.7326/M17-0188. Print 2017 Aug 1.

Helpful Links

• NHGRI

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2011
• Primary Completion (Actual): November 4, 2016
• Study Completion (Actual): January 2, 2021

Study Registration Dates

• First Submitted: August 17, 2012
• First Submitted That Met QC Criteria: November 28, 2012
• First Posted (Estimated): November 29, 2012

Study Record Updates

• Last Update Posted (Actual): April 23, 2024
• Last Update Submitted That Met QC Criteria: April 1, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Keywords: Primary Care; Dilated Cardiomyopathy; Hypertrophic Cardiomyopathy; Cardiology; Whole Genome Sequencing
• Additional Relevant MeSH Terms: Heart Diseases; Cardiovascular Diseases; Genetic Diseases, Inborn; Pathological Conditions, Anatomical; Aortic Valve Disease; Heart Valve Diseases; Cardiomegaly; Laminopathies; Aortic Stenosis, Subvalvular; Aortic Valve Stenosis; Hypertrophy; Cardiomyopathies; Cardiomyopathy, Dilated; Cardiomyopathy, Hypertrophic
• Other Study ID Numbers: MedSeq™; U01HG006500 (U.S. NIH Grant/Contract)