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Challenges and opportunities for integrating genetic testing into a diagnostic workflow: heritable long QT syndrome as a model

  • Ira M. Lubin EMAIL logo , Edward R. Lockhart , Julie Frank , Vincent Y. See , Sudhir Vashist and Carol Greene
Published/Copyright: July 9, 2019

Abstract

Background

An increasing number of diagnostic evaluations incorporate genetic testing to facilitate accurate and timely diagnoses. The increasing number and complexity of genetic tests continue to pose challenges in deciding when to test, selecting the correct test(s), and using results to inform medical diagnoses, especially for medical professionals lacking genetic expertise. Careful consideration of a diagnostic workflow can be helpful in understanding the appropriate uses of genetic testing within a broader diagnostic workup.

Content

The diagnosis of long QT syndrome (LQTS), a life-threatening cardiac arrhythmia, provides an example for this approach. Electrocardiography is the preferred means for diagnosing LQTS but can be uninformative for some patients due to the variable presentation of the condition. Family history and genetic testing can augment physiological testing to inform a diagnosis and subsequent therapy. Clinical and laboratory professionals informed by peer- reviewed literature and professional recommendations constructed a generalized LQTS diagnostic workflow. This workflow served to explore decisions regarding the use of genetic testing for diagnosing LQTS.

Summary and outlook

Understanding the complexities and approaches to integrating genetic testing into a broader diagnostic evaluation is anticipated to support appropriate test utilization, optimize diagnostic evaluation, and facilitate a multidisciplinary approach essential for achieving accurate and timely diagnoses.


Corresponding author: Ira M. Lubin, PhD, FACMG, Division of Laboratory Systems, Centers for Disease Control and Prevention, 1600 Clifton Rd., MS V24-3, Atlanta, GA 30329, USA

Acknowledgments

This project was supported in part by an appointment of Edward R Lockhart to the Research Participation Program at the Centers for Disease Control and Prevention (CDC) administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and CDC. We thank Diego Arambula and Danielle Robinson-Holland for their careful review of the manuscript and helpful comments.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: VYS received support from the American Heart Association, Funder Id: http://dx.doi.org/10.13039/100000968: Award Number 16MCPRP31350041.

  3. Employment: IML is employed by the Centers for Disease Control and Prevention. JF, VYS, and CF are employed by the University of Maryland School of Medicine.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

  6. Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the CDC or the US Agency for Toxic Substances and Disease Registry.

References

1. Singh H, Meyer AN, Thomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf 2014;23:727–31.10.1136/bmjqs-2013-002627Search in Google Scholar PubMed PubMed Central

2. Institute of Medicine NAoS, Engineering, Medicine. Improving diagnosis in health care. In: Balogh EP, Miller BT, Ball JR, editors. Washington, DC: The National Academies Press, 2015:472.Search in Google Scholar

3. Borad MJ, LoRusso PM. Twenty-first century precision medicine in oncology: genomic profiling in patients with cancer. Mayo Clin Proc 2017;92:1583–91.10.1016/j.mayocp.2017.08.002Search in Google Scholar PubMed

4. Marian AJ, Braunwald E. Hypertrophic cardiomyopathy: genetics, pathogenesis, clinical manifestations, diagnosis, and therapy. Circ Res 2017;121:749–70.10.1161/CIRCRESAHA.117.311059Search in Google Scholar PubMed PubMed Central

5. Sawyer SL, Hartley T, Dyment DA, Beaulieu CL, Schwartzentruber J, Smith A, et al. Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care. Clin Genet 2016;89:275–84.10.1111/cge.12654Search in Google Scholar PubMed PubMed Central

6. Wolfe K, Stueber K, McQuillin A, Jichi F, Patch C, Flinter F, et al. Genetic testing in intellectual disability psychiatry: opinions and practices of UK child and intellectual disability psychiatrists. J Appl Res Intellect Disabil 2018;31:273–84.10.1111/jar.12391Search in Google Scholar PubMed PubMed Central

7. Alders M, Bikker H, Christiaans I. Long QT syndrome. In: Adam MP, Ardinger HH, Pagon RA, Wallace SE, Bean LJ, Stephens K, et al., editors. GeneReviews((R)). Seattle, WA: University of Washington, 2018.Search in Google Scholar

8. Eggeling T, Osterhues HH, Hoeher M, Gabrielsen FG, Weismueller P, Hombach V. Value of Holter monitoring in patients with the long QT syndrome. Cardiology 1992;81:107–14.10.1159/000175784Search in Google Scholar PubMed

9. Giudicessi JR, Ackerman MJ. Genotype- and phenotype-guided management of congenital long QT syndrome. Curr Probl Cardiol 2013;38:417–55.10.1016/j.cpcardiol.2013.08.001Search in Google Scholar PubMed PubMed Central

10. Ingles J, Semsarian C. The value of cardiac genetic testing. Trends Cardiovasc Med 2014;24:217–24.10.1016/j.tcm.2014.05.009Search in Google Scholar PubMed

11. Mauriello DA, Johnson JN, Ackerman MJ. Holter monitoring in the evaluation of congenital long QT syndrome. Pacing Clin Electrophysiol 2011;34:1100–4.10.1111/j.1540-8159.2011.03102.xSearch in Google Scholar PubMed

12. O’Malley AS, Reschovsky JD. Referral and consultation communication between primary care and specialist physicians: finding common ground. Arch Intern Med 2011;171:56–65.10.1001/archinternmed.2010.480Search in Google Scholar PubMed

13. Priori SG, Wilde AA, Horie M, Cho Y, Behr ER, Berul C, et al. HRS/EHRA/APHRS expert consensus statement on the diagnosis and management of patients with inherited primary arrhythmia syndromes: document endorsed by HRS, EHRA, and APHRS in May 2013 and by ACCF, AHA, PACES, and AEPC in June 2013. Heart Rhythm 2013;10:1932–63.10.1016/j.hrthm.2013.05.014Search in Google Scholar PubMed

14. Schwartz PJ. Practical issues in the management of the long QT syndrome: focus on diagnosis and therapy. Swiss Med Wkly 2013;143:w13843.10.4414/smw.2013.13843Search in Google Scholar PubMed

15. Ackerman MJ, Priori SG, Willems S, Berul C, Brugada R, CalkinsH, et al. HRS/EHRA expert consensus statement on the state of genetic testing for the channelopathies and cardiomyopathies this document was developed as a partnership between the Heart Rhythm Society (HRS) and the European Heart Rhythm Association (EHRA). Heart Rhythm 2011;8:1308–39.10.1093/europace/eur245Search in Google Scholar PubMed

16. Nakano Y, Shimizu W. Genetics of long-QT syndrome. J Hum Genet 2016;61:51–5.10.1038/jhg.2015.74Search in Google Scholar PubMed

17. Ruiter JS, Berkenbosch-Nieuwhof K, van den Berg MP, van Dijk R, Middel B, van Tintelen JP. The importance of the family history in caring for families with long QT syndrome and dilated cardiomyopathy. Am J Med Genet A 2010;152A:607–12.10.1002/ajmg.a.33270Search in Google Scholar PubMed

18. Magnusson P, Gustafsson PE. A case of long QT syndrome: challenges on a bumpy road. Clin Case Rep 2017;5:954–60.10.1002/ccr3.985Search in Google Scholar PubMed PubMed Central

19. Schwartz PJ, Stramba-Badiale M, Crotti L, Pedrazzini M, Besana A, Bosi G, et al. Prevalence of the congenital long-QT syndrome. Circulation 2009;120;1761–7.10.1161/CIRCULATIONAHA.109.863209Search in Google Scholar PubMed PubMed Central

20. Albert CM, MacRae CA, Chasman DI, VanDenburgh M, Buring JE, Manson JE, et al. Common variants in cardiac ion channel genes are associated with sudden cardiac death. Circ Arrhythm Electrophysiol 2010;3:222–9.10.1161/CIRCEP.110.944934Search in Google Scholar PubMed PubMed Central

21. Postema PG, Wilde AA. The measurement of the QT interval. Curr Cardiol Rev 2014;10:287–94.10.2174/1573403X10666140514103612Search in Google Scholar PubMed PubMed Central

22. Sturm AC, Hershberger RE. Genetic testing in cardiovascular medicine: current landscape and future horizons. Curr Opin Cardiol 2013;28:317–25.10.1097/HCO.0b013e32835fb728Search in Google Scholar PubMed

23. Schwartz PJ, Crotti L. QTc behavior during exercise and genetic testing for the long-QT syndrome. Circulation 2011;124:2181–4.10.1161/CIRCULATIONAHA.111.062182Search in Google Scholar PubMed

24. Dezman ZD, Mattu A, Body R. Utility of the history and physical examination in the detection of acute coronary syndromes in emergency department patients. West J Emerg Med 2017;18:752–60.10.5811/westjem.2017.3.32666Search in Google Scholar PubMed PubMed Central

25. Foy AJ, Filippone L. Chest pain evaluation in the emergency department. Med Clin North Am 2015;99:835–47.10.1016/j.mcna.2015.02.010Search in Google Scholar PubMed

26. Goldenberg I, Huang DT. Evaluation of a patient with a positive family history for long QT syndrome. Card Electrophysiol Clin 2012;4:239–48.10.1016/j.ccep.2012.02.004Search in Google Scholar PubMed

27. Miller CE, Krautscheid P, Baldwin EE, Tvrdik T, Openshaw AS, Hart K, et al. Genetic counselor review of genetic test orders in a reference laboratory reduces unnecessary testing. Am J Med Genet A 2014;164A:1094–101.10.1002/ajmg.a.36453Search in Google Scholar PubMed

28. Rubinstein WS, Maglott DR, Lee JM, Kattman BL, Malheiro AJ, Ovetsky M, et al. The NIH genetic testing registry: a new, centralized database of genetic tests to enable access to comprehensive information and improve transparency. Nucleic Acids Res 2013;41(Database issue):D925–35.10.1093/nar/gks1173Search in Google Scholar PubMed PubMed Central

29. Hickner J, Thompson PJ, Wilkinson T, Epner P, Sheehan M, Pollock AM, et al. Primary care physicians’ challenges in ordering clinical laboratory tests and interpreting results. J Am Board Fam Med 2014;27:268–74.10.3122/jabfm.2014.02.130104Search in Google Scholar PubMed

30. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015;17:405–24.10.1038/gim.2015.30Search in Google Scholar PubMed PubMed Central

31. Bean LJ, Hegde MR. Clinical implications and considerations for evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines. Genome Med 2017;9:111.10.1186/s13073-017-0508-zSearch in Google Scholar PubMed PubMed Central

32. Patel RY, Shah N, Jackson AR, Ghosh R, Pawliczek P, Paithankar S, et al. ClinGen Pathogenicity Calculator: a configurable system for assessing pathogenicity of genetic variants. Genome Med 2017;9:3.10.1186/s13073-016-0391-zSearch in Google Scholar PubMed PubMed Central

33. Bell CJ, Dinwiddie DL, Miller NA, Hateley SL, Ganusova EE, Mudge J, et al. Carrier testing for severe childhood recessive diseases by next-generation sequencing. Sci Transl Med 2011;3:65ra4.10.1126/scitranslmed.3001756Search in Google Scholar PubMed PubMed Central

34. O’Daniel JM, McLaughlin HM, Amendola LM, Bale SJ, Berg JS, Bick D, et al. A survey of current practices for genomic sequencing test interpretation and reporting processes in US laboratories. Genet Med 2017;19:575–82.10.1038/gim.2016.152Search in Google Scholar PubMed PubMed Central

35. Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res 2018;46(Database issue):D1062–7.10.1093/nar/gkx1153Search in Google Scholar PubMed PubMed Central

36. Scheuner MT, Peredo J, Tangney K, Schoeff D, Sale T, Lubick-Goldzweig C, et al. Electronic health record interventions at the point of care improve documentation of care processes and decrease orders for genetic tests commonly ordered by nongeneticists. Genet Med 2017;19:112–20.10.1038/gim.2016.73Search in Google Scholar PubMed PubMed Central

37. Rasmussen LV, Smith ME, Almaraz F, Persell SD, Rasmussen-Torvik LJ, Pacheco JA, et al. An ancillary genomics system to support the return of pharmacogenomic results. J Am Med Inform Assoc 2019;26:306–10.10.1093/jamia/ocy187Search in Google Scholar PubMed PubMed Central

38. Laposata M. A new kind of autopsy for 21st century medicine. Arch Pathol Lab Med 2017;141:887–8.10.5858/arpa.2016-0317-EDSearch in Google Scholar PubMed

Received: 2019-03-04
Accepted: 2019-06-18
Published Online: 2019-07-09
Published in Print: 2021-02-23

© 2019 Walter de Gruyter GmbH, Berlin/Boston

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