Advertisement

Pediatric Cardiology

, Volume 39, Issue 4, pp 709–717 | Cite as

Hypertrophic Cardiomyopathy Genotype Prediction Models in a Pediatric Population

  • Randa Newman
  • John Lynn Jefferies
  • Clifford Chin
  • Hua He
  • Amy Shikany
  • Erin M. Miller
  • Ashley Parrott
Original Article

Abstract

The Toronto Hypertrophic Cardiomyopathy (HCM) Genotype Score and Mayo HCM Genotype Predictor are risk assessment models developed to estimate a patient’s likelihood of testing positive for a pathogenic variant causative of HCM. These models were developed from adult populations with HCM based on factors that have been associated with a positive genotype and have not been validated in external populations. The purpose of this study was to evaluate the overall predictive abilities of these models in a clinical pediatric HCM setting. A retrospective medical record review of 77 pediatric patients with gene panel testing for HCM between September 2005 and June 2015 was performed. Clinical and echocardiographic variables used in the developed models were collected and used to calculate scores for each patient. To evaluate model performance, the ability to discriminate between a carrier and non-carrier was assessed by area under the ROC curve (AUC) and overall calibration was evaluated by the Hosmer–Lemeshow goodness-of-fit statistic. Discrimination assessed by AUC was 0.72 (P < 0.001) for the Toronto model and 0.67 (P = 0.004) for the Mayo model. The Toronto model and the Mayo model showed P values of 0.36 and 0.82, respectively, for model calibration. Our findings suggest that these models are useful in predicting a positive genetic test result in a pediatric HCM setting. They may be used to aid healthcare providers in communicating risk and enhance patient decision-making regarding pursuit of genetic testing.

Keywords

Hypertrophic cardiomyopathy Genotype Genetic testing Pediatrics Risk assessment Genetic counseling 

Notes

Compliance with Ethical Standards

Conflict of interest

Erin M. Miller has served as a consultant for Ambry Genetics, a clinical genetic testing laboratory. Amy Shikany and John L. Jefferies have employment responsibilities with the Heart Institute Diagnostic Lab, a clinical genetic testing laboratory at Cincinnati Children’s Hospital Medical Center. The additional authors report no conflict of interest.

Ethical Approval

For this type of study formal consent is not required.

Supplementary material

246_2018_1810_MOESM1_ESM.pdf (101 kb)
Supplementary material 1 (PDF 101 KB)

References

  1. 1.
    Maron BJ (2002) Hypertrophic cardiomyopathy: a systematic review. JAMA 287(10):1308–1320CrossRefPubMedGoogle Scholar
  2. 2.
    Teekakirikul P, Kelly MA, Rehm HL, Lakdawala NK, Funke BH (2013) Inherited cardiomyopathies: molecular genetics and clinical genetic testing in the postgenomic era. J Mol Diagn 15(2):158–170CrossRefPubMedGoogle Scholar
  3. 3.
    Watkins H, Ashrafian H, Redwood C (2011) Inherited cardiomyopathies. N Engl J Med 364(17):1643–1656CrossRefPubMedGoogle Scholar
  4. 4.
    Geisterfer-Lowrance AA, Kass S, Tanigawa G, Vosberg HP, McKenna W, Seidman C et al (1990) A molecular basis for familial hypertrophic cardiomyopathy: a beta cardiac myosin heavy chain gene missense mutation. Cell 62(5):999–1006CrossRefPubMedGoogle Scholar
  5. 5.
    Andersen PS, Havndrup O, Hougs L, Sorensen KM, Jensen M, Larsen LA et al (2009) Diagnostic yield, interpretation, and clinical utility of mutation screening of sarcomere encoding genes in Danish hypertrophic cardiomyopathy patients and relatives. Hum Mutat 30(3):363–370CrossRefPubMedGoogle Scholar
  6. 6.
    Erdmann J, Daehmlow S, Wischke S, Senyuva M, Werner U, Raible J et al (2003) Mutation spectrum in a large cohort of unrelated consecutive patients with hypertrophic cardiomyopathy. Clin Genet 64(4):339–349CrossRefPubMedGoogle Scholar
  7. 7.
    Richard P, Charron P, Carrier L, Ledeuil C, Cheav T, Pichereau C et al (2003) Hypertrophic cardiomyopathy: distribution of disease genes, spectrum of mutations, and implications for a molecular diagnosis strategy. Circulation 107(17):2227–2232CrossRefPubMedGoogle Scholar
  8. 8.
    Van Driest SL, Ommen SR, Tajik AJ, Gersh BJ, Ackerman MJ (2005) Yield of genetic testing in hypertrophic cardiomyopathy. Mayo Clin Proc 80(6):739–744CrossRefPubMedGoogle Scholar
  9. 9.
    Kaski JP, Syrris P, Esteban MT, Jenkins S, Pantazis A, Deanfield JE et al (2009) Prevalence of sarcomere protein gene mutations in preadolescent children with hypertrophic cardiomyopathy. Circ Cardiovasc Genet 2(5):436–441CrossRefPubMedGoogle Scholar
  10. 10.
    Kindel SJ, Miller EM, Gupta R, Cripe LH, Hinton RB, Spicer RL et al (2012) Pediatric cardiomyopathy: importance of genetic and metabolic evaluation. J Card Fail 18(5):396–403CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Morita H, Rehm HL, Menesses A, McDonough B, Roberts AE, Kucherlapati R et al (2008) Shared genetic causes of cardiac hypertrophy in children and adults. N Engl J Med 358(18):1899–1908CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Nugent AW, Daubeney PE, Chondros P, Carlin JB, Colan SD, Cheung M et al (2005) Clinical features and outcomes of childhood hypertrophic cardiomyopathy: results from a national population-based study. Circulation 112(9):1332–1338CrossRefPubMedGoogle Scholar
  13. 13.
    Ingles J, McGaughran J, Scuffham PA, Atherton J, Semsarian C (2012) A cost-effectiveness model of genetic testing for the evaluation of families with hypertrophic cardiomyopathy. Heart 98(8):625–630CrossRefPubMedGoogle Scholar
  14. 14.
    Wordsworth S, Leal J, Blair E, Legood R, Thomson K, Seller A et al (2010) DNA testing for hypertrophic cardiomyopathy: a cost-effectiveness model. Eur Heart J 31(8):926–935CrossRefPubMedGoogle Scholar
  15. 15.
    Ackerman MJ, Priori SG, Willems S, Berul C, Brugada R, Calkins H et al (2011) 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). Europace 13(8):1077–1109CrossRefPubMedGoogle Scholar
  16. 16.
    Gersh BJ, Maron BJ, Bonow RO, Dearani JA, Fifer MA, Link MS et al (2011) 2011 ACCF/AHA guideline for the diagnosis and treatment of hypertrophic cardiomyopathy: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation 124(24):e783–e831CrossRefPubMedGoogle Scholar
  17. 17.
    Hershberger RE, Lindenfeld J, Mestroni L, Seidman CE, Taylor MR, Towbin JA (2009) Genetic evaluation of cardiomyopathy: a Heart Failure Society of America practice guideline. J Card Fail 15(2):83–97CrossRefPubMedGoogle Scholar
  18. 18.
    Fischer C, Kuchenbacker K, Engel C, Zachariae S, Rhiem K, Meindl A et al (2013) Evaluating the performance of the breast cancer genetic risk models BOADICEA, IBIS, BRCAPRO and Claus for predicting BRCA1/2 mutation carrier probabilities: a study based on 7352 families from the German Hereditary Breast and Ovarian Cancer Consortium. J Med Genet 50:360–367CrossRefPubMedGoogle Scholar
  19. 19.
    Ramsoekh D, van Leerdam ME, Wagner A, Kuipers EJ, Steyerberg EW (2009) Mutation prediction models in Lynch syndrome: evaluation in a clinical genetic setting. J Med Genet 46:745–751CrossRefPubMedGoogle Scholar
  20. 20.
    Gruner C, Ivanov J, Care M, Williams L, Moravsky G, Yang H et al (2013) Toronto hypertrophic cardiomyopathy genotype score for prediction of a positive genotype in hypertrophic cardiomyopathy. Circ Cardiovasc Genet 6(1):19–26CrossRefPubMedGoogle Scholar
  21. 21.
    Bos JM, Will ML, Gersh BJ, Kruisselbring TM, Ommen ST, Ackerman MJ (2014) Characterization of a phenotype-based genetic test prediction score for unrelated patients with hypertrophic cardiomyopathy. Mayo Clin Proc 89(6):727–737CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Binder J, Ommen SR, Gersh BJ, Van Driest SL, Tajik AJ, Nishimura RA et al (2006) Echocardiography-guided genetic testing in hypertrophic cardiomyopathy: septal morphological features predict the presence of myofilament mutations. Mayo Clin Proc 81(4):459–467CrossRefPubMedGoogle Scholar
  23. 23.
    Girolami F, Olivotto I, Passerini I, Zachara E, Nistri S, Re F et al (2006) A molecular screening strategy based on beta-myosin heavy chain, cardiac myosin binding protein C and troponin T genes in Italian patients with hypertrophic cardiomyopathy. J Cardiovasc Med 7(8):601–607CrossRefGoogle Scholar
  24. 24.
    Ho CY (2010) Genetics and clinical destiny: improving care in hypertrophic cardiomyopathy. Circulation 122(23):2430–2440CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Murphy SL, Anderson JH, Kapplinger JD, Kruisselbrink TM, Gersh BJ, Ommen SR et al (2016) Evaluation of the Mayo Clinic phenotype-based genotype predictor score in patients with clinically diagnosed hypertrophic cardiomyopathy. J Cardiovasc Transl Res 9:153–161CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG (2009) Research electronic data capture (REDCap): a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42(2):377–381CrossRefPubMedGoogle Scholar
  27. 27.
    Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Hoover J, Jang W, Katz K, Ovetsky M, Riley G, Sethi A, Tully R, Villamarin-Salomon R, Rubinstein W, Maglott DR (2015) ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res 44:D862–D868CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T et al (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature 536:285–291CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  30. 30.
    Ingles J, Doolan A, Chiu C, Seidman J, Seidman C, Semsarian C (2005) Compound and double mutations in patients with hypertrophic cardiomyopathy: implications for genetic testing and counselling. J Med Genet 42(10):e59CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Van Driest SL, Vasile VC, Ommen SR, Will ML, Tajik AJ, Gersh BJ et al (2004) Myosin binding protein C mutations and compound heterozygosity in hypertrophic cardiomyopathy. J Am Coll Cardiol 44(9):1903–1910CrossRefPubMedGoogle Scholar
  32. 32.
    Niimura H, Patton KK, McKenna WJ, Soults J, Maron BJ, Seidman JG et al (2002) Sarcomere protein gene mutations in hypertrophic cardiomyopathy of the elderly. Circulation 105(4):446–451CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Human GeneticsCincinnati Children’s Hospital Medical Center (CCHMC)CincinnatiUSA
  2. 2.The Heart Institute at CCHMCCincinnatiUSA
  3. 3.University of CincinnatiCincinnatiUSA

Personalised recommendations