Literature DB >> 29362845

Hypertrophic Cardiomyopathy Genotype Prediction Models in a Pediatric Population.

Randa Newman1, John Lynn Jefferies2,3, Clifford Chin2,3, Hua He4, Amy Shikany2, Erin M Miller2,3, Ashley Parrott2.   

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.

Entities:  

Keywords:  Genetic counseling; Genetic testing; Genotype; Hypertrophic cardiomyopathy; Pediatrics; Risk assessment

Mesh:

Year:  2018        PMID: 29362845     DOI: 10.1007/s00246-018-1810-2

Source DB:  PubMed          Journal:  Pediatr Cardiol        ISSN: 0172-0643            Impact factor:   1.655


  31 in total

1.  Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

Authors:  Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde
Journal:  J Biomed Inform       Date:  2008-09-30       Impact factor: 6.317

2.  Sarcomere protein gene mutations in hypertrophic cardiomyopathy of the elderly.

Authors:  Hideshi Niimura; Kristen K Patton; William J McKenna; Johann Soults; Barry J Maron; J G Seidman; Christine E Seidman
Journal:  Circulation       Date:  2002-01-29       Impact factor: 29.690

Review 3.  DNA testing for hypertrophic cardiomyopathy: a cost-effectiveness model.

Authors:  Sarah Wordsworth; José Leal; Edward Blair; Rosa Legood; Kate Thomson; Anneke Seller; Jenny Taylor; Hugh Watkins
Journal:  Eur Heart J       Date:  2010-03-18       Impact factor: 29.983

4.  Toronto hypertrophic cardiomyopathy genotype score for prediction of a positive genotype in hypertrophic cardiomyopathy.

Authors:  Christiane Gruner; Joan Ivanov; Melanie Care; Lynne Williams; Gil Moravsky; Hua Yang; Balint Laczay; Katherine Siminovitch; Anna Woo; Harry Rakowski
Journal:  Circ Cardiovasc Genet       Date:  2012-12-13

5.  Characterization of a phenotype-based genetic test prediction score for unrelated patients with hypertrophic cardiomyopathy.

Authors:  J Martijn Bos; Melissa L Will; Bernard J Gersh; Teresa M Kruisselbrink; Steve R Ommen; Michael J Ackerman
Journal:  Mayo Clin Proc       Date:  2014-05-01       Impact factor: 7.616

Review 6.  Genetics and clinical destiny: improving care in hypertrophic cardiomyopathy.

Authors:  Carolyn Y Ho
Journal:  Circulation       Date:  2010-12-07       Impact factor: 29.690

7.  Pediatric cardiomyopathy: importance of genetic and metabolic evaluation.

Authors:  Steven J Kindel; Erin M Miller; Resmi Gupta; Linda H Cripe; Robert B Hinton; Robert L Spicer; Jeffrey A Towbin; Stephanie M Ware
Journal:  J Card Fail       Date:  2012-03-10       Impact factor: 5.712

8.  Myosin binding protein C mutations and compound heterozygosity in hypertrophic cardiomyopathy.

Authors:  Sara L Van Driest; Vlad C Vasile; Steve R Ommen; Melissa L Will; A Jamil Tajik; Bernard J Gersh; Michael J Ackerman
Journal:  J Am Coll Cardiol       Date:  2004-11-02       Impact factor: 24.094

9.  Genetic evaluation of cardiomyopathy--a Heart Failure Society of America practice guideline.

Authors:  Ray E Hershberger; Joann Lindenfeld; Luisa Mestroni; Christine E Seidman; Matthew R G Taylor; Jeffrey A Towbin
Journal:  J Card Fail       Date:  2009-03       Impact factor: 5.712

10.  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.

Authors:  Christine Fischer; Karoline Kuchenbäcker; Christoph Engel; Silke Zachariae; Kerstin Rhiem; Alfons Meindl; Nils Rahner; Nicola Dikow; Hansjörg Plendl; Irmgard Debatin; Tiemo Grimm; Dorothea Gadzicki; Ricarda Flöttmann; Judit Horvath; Evelin Schröck; Friedrich Stock; Dieter Schäfer; Ira Schwaab; Christiana Kartsonaki; Nasim Mavaddat; Brigitte Schlegelberger; Antonis C Antoniou; Rita Schmutzler
Journal:  J Med Genet       Date:  2013-04-06       Impact factor: 6.318

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  2 in total

1.  Prediction of Genotype Positivity in Patients With Hypertrophic Cardiomyopathy Using Machine Learning.

Authors:  Lusha W Liang; Michael A Fifer; Kohei Hasegawa; Mathew S Maurer; Muredach P Reilly; Yuichi J Shimada
Journal:  Circ Genom Precis Med       Date:  2021-04-23

Review 2.  Childhood Hypertrophic Cardiomyopathy: A Disease of the Cardiac Sarcomere.

Authors:  Gabrielle Norrish; Ella Field; Juan P Kaski
Journal:  Front Pediatr       Date:  2021-07-02       Impact factor: 3.418

  2 in total

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