Browsing by Author "Morales, Ana"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Genetic Evaluation of Cardiomyopathy - a Heart Failure Society of America Practice Guideline
    (Elsevier, 2018) Hershberger, Ray E.; Givertz, Michael; Ho, Carolyn Y.; Judge, Daniel P.; Kantor, Paul; McBride, Kim L.; Morales, Ana; Taylor, Matthew R. G.; Vatta, Matteo; Ware, Stephanie M.; Pediatrics, School of Medicine
    This guideline describes the approach and expertise needed for the genetic evaluation of cardiomyopathy. First published in 2009 by the Heart Failure Society of America (HFSA), this guidance has now been updated in collaboration with the American College of Medical Genetics and Genomics (ACMG). The writing group, composed of cardiologists and genetics professionals with expertise in adult and pediatric cardiomyopathy, reflects the emergence and increased clinical activity devoted to cardiovascular genetic medicine. The genetic evaluation of cardiomyopathy is a rapidly emerging key clinical priority, as high throughput sequencing is now feasible for clinical testing, and conventional interventions can improve survival, reduce morbidity, and enhance quality of life. Moreover, specific interventions may be guided by genetic analysis. A systematic approach is recommended: always a comprehensive family history; an expert phenotypic evaluation of the proband and at-risk family members to confirm a diagnosis and guide genetic test selection and interpretation; referral to expert centers as needed; genetic testing, with pre- and post-test genetic counseling; and specific guidance as indicated for drug and device therapies. The evaluation of infants and children demands special expertise. The approach to manage secondary and incidental sequence findings as recommended by the ACMG is provided.
  • Thumbnail Image
    Item
    Harmonizing the Collection of Clinical Data on Genetic Testing Requisition Forms to Enhance Variant Interpretation in Hypertrophic Cardiomyopathy (HCM): A Study from the ClinGen Cardiomyopathy Variant Curation Expert Panel
    (Elsevier, 2021-05) Morales, Ana; Ing, Alexander; Antolik, Christian; Austin-Tse, Christina; Baudhuin, Linnea M.; Bronicki, Lucas; Cirino, Allison; Hawley, Megan H.; Fietz, Michael; Garcia, John; Ho, Carolyn; Ingles, Jodie; Jarinova, Olga; Johnston, Tami; Kelly, Melissa A.; Kurtz, C. Lisa; Lebo, Matt; Macaya, Daniela; Mahanta, Lisa; Maleszewski, Joseph; Manrai, Arjun K.; Murray, Mitzi; Richard, Gabriele; Semsarian, Chris; Thomson, Kate L.; Winder, Tom; Ware, James S.; Hershberger, Ray E.; Funke, Birgit H.; Vatta, Matteo; Medical and Molecular Genetics, School of Medicine
    Diagnostic laboratories gather phenotypic data through requisition forms, but there is no consensus as to which data are essential for variant interpretation. The ClinGen Cardiomyopathy Variant Curation Expert Panel defined a phenotypic data set for hypertrophic cardiomyopathy (HCM) variant interpretation, with the goal of standardizing requisition forms. Phenotypic data elements listed on requisition forms from nine leading cardiomyopathy testing laboratories were compiled to assess divergence in data collection. A pilot of 50 HCM cases was implemented to determine the feasibility of harmonizing data collection. Laboratory directors were surveyed to gauge potential for adoption of a minimal data set. Wide divergence was observed in the phenotypic data fields in requisition forms. The 50-case pilot showed that although demographics and assertion of a clinical diagnosis of HCM had 86% to 98% completion, specific phenotypic features, such as degree of left ventricular hypertrophy, ejection fraction, and suspected syndromic disease, were completed only 24% to 44% of the time. Nine data elements were deemed essential for variant classification by the expert panel. Participating laboratories unanimously expressed a willingness to adopt these data elements in their requisition forms. This study demonstrates the value of comparing and sharing best practices through an expert group, such as the ClinGen Program, to enhance variant interpretation, providing a foundation for leveraging cumulative case-level data in public databases and ultimately improving patient care.