Factors associated with the time to complete clinical exome sequencing in a pediatric patient population |
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| Affiliation: | 1. Human Genetics and Genetic Counseling Master''s Program, Stanford Medicine, Stanford, CA;2. Stanford Hospitals and Clinics Genetic Testing Optimization Service, Stanford Medicine, Stanford, CA;3. Department of Pathology, Stanford University, Stanford, CA;4. Division of Medical Genetics, Department of Pediatrics, Stanford University, Stanford, CA;5. Quantitative Sciences Unit, Stanford University, Palo Alto, CA;1. Department of Computer Science, Stanford University School of Engineering, Stanford, CA;2. Medical Scientist Training Program, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA;3. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA;4. Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA;5. Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA;1. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN;2. Department of Medicine, Vanderbilt University Medical Center, Nashville, TN;3. Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY;4. Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL;5. Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, OH;6. Department of Medicine, Brigham and Women’s Hospital, Boston MA;7. Genomic Medicine Institute, Geisinger, Danville, PA;8. The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA;9. Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA;10. Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA;11. Division of Pulmonary Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA;12. Division of Medical Genetics, Department of Medicine, University of Washington Medical Center, Seattle, WA;13. Department of Genome Sciences, University of Washington Medical Center, Seattle, WA;14. Department of Pediatrics, Columbia University Irving Medical Center, New York, NY;15. Department of Medicine, Columbia University Irving Medical Center, New York, NY;16. Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD;17. Gonda Vascular Center, Mayo Clinic, Rochester, MN;1. Division of Pediatric Cardiology, Department of Pediatrics, Baylor College of Medicine, Texas Children’s Hospital, Houston, TX;2. Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, TX;3. Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Women and Infants Hospital of Rhode Island, Warren Alpert Medical School at Brown University, Providence, RI;4. Division of Vascular Surgery, Department of Surgery, University of Washington, Seattle, WA;5. Division of Medical Genetics, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science Center, Houston, TX;1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA;2. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA;3. Division of Biostatistics and Bioinformatics, Johns Hopkins School of Medicine, Baltimore, MD;4. Department of Mathematical Sciences, Tsinghua University, Beijing, China;5. Department of Computer Science, School of Engineering, Tufts University, Medford, MA;6. Cancer Genetics and Prevention Division, Dana-Farber Cancer Institute, Boston, MA;7. Division of Gastroenterology, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA;8. McGraw/Patterson Center for Population Sciences, Dana-Farber Cancer Institute, Boston, MA;9. Department of Pathology and Laboratory Medicine, Molecular Diagnostic Service, Memorial Sloan Kettering Cancer Center, New York, NY;10. Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Comprehensive Cancer Center, New York, NY;11. Niehaus Center for Inherited Cancer Genomics, Memorial Sloan Kettering Cancer Center, New York, NY;12. Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX;13. Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX;14. Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX;15. Gastroenterology, Hepatology and Nutrition, University of Texas MD Anderson Cancer Center, Houston, TX;16. Nokia Bell Labs, Murray Hill, NJ;17. Department of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD;18. Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia;19. University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia;20. Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia;21. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia;22. University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia;23. Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia;24. Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia;25. Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia;26. Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA;1. Medical Genetics Service, Gaffrée and Guinle University Hospital, Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, Brazil;2. D’Or Institute of Research and Education (IDOR), Rio de Janeiro, Brazil;3. Centro de Genética Médica do Rio de Janeiro, Rio de Janeiro, Brazil;4. Birth Defects Epidemiology Laboratory, Oswaldo Cruz Institute, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil;1. Department of Pediatrics, Columbia University Irving Medical Center, Columbia University, New York, NY;2. Department of Psychiatry, Columbia University Irving Medical Center, Columbia University, New York, NY;3. Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University Irving Medical Center, New York, NY;4. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN;5. Department of Pediatrics, Michigan Medicine, Ann Arbor, MI;6. Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN;7. Departments of Pediatrics and Human Genetics, University of Michigan Medical School, Michigan Medicine, Ann Arbor, MI;8. Department of Medicine, Columbia University Irving Medical Center, Columbia University, New York, NY |
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| Abstract: | PurposeExome sequencing (ES) is becoming increasingly important for diagnosing rare genetic disorders. Patients and clinicians face several barriers when attempting to obtain ES. This study is aimed to describe factors associated with a longer time interval between provider recommendation of testing and sample collection for ES.MethodsA retrospective chart review was conducted for insurance-authorized, completed pediatric ES in which initial requests were reviewed by Stanford’s Genetic Testing Optimization Service between November 2018 and December 2019. Regression analysis was used to determine the association between the geocoded median household income and 3 different time point intervals defined as time to test, insurance decision, and scheduling/consent.ResultsOf the 281 charts reviewed, 115 cases were included in the final cohort. The average time from provider preauthorization request to sample collection took 104.4 days, and income was negatively correlated with the length of the insurance decision interval.ConclusionPediatric patients undergo a lengthy, uncertain process when attempting to obtain ES, some of which is associated with income. More research and clinician interventions are required to clarify specific socioeconomic factors that influence the ability to obtain timely ES and develop optimal protocols. |
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| Keywords: | Exome sequencing Genetic testing Insurance Utilization management |
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