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Diagnostic Impact and Cost-effectiveness of Whole-Exome Sequencing for Ambulant Children With Suspected Monogenic Conditions

Educational Objective
To investigate the impact of whole-exome sequencing (WES) in sequencing-naive children suspected of having a monogenic disorder and to determine the point in the diagnostic trajectory at which WES is most cost-effective.
1 Credit CME
Key Points

Question  What is the clinical impact and cost-effectiveness of whole-exome sequencing in ambulant children suspected of having a monogenic condition?

Findings  Singleton whole-exome sequencing identified diagnoses in 23 of 44 children (52%); the diagnoses were unexpected in 8 of 23 children (35%), and clinical management was altered in 6 of 23 children (26%). Whole-exome sequencing was most cost-effective when applied at initial presentation to tertiary care compared with first clinical genetics assessment and the standard diagnostic pathway.

Meaning  Pediatricians should consider early referral of children with undiagnosed conditions to clinical genetics specialists for whole-exome sequencing to maximize cost-effectiveness.

Abstract

Importance  Optimal use of whole-exome sequencing (WES) in the pediatric setting requires an understanding of who should be considered for testing and when it should be performed to maximize clinical utility and cost-effectiveness.

Objectives  To investigate the impact of WES in sequencing-naive children suspected of having a monogenic disorder and evaluate its cost-effectiveness if WES had been available at different time points in their diagnostic trajectory.

Design, Setting, and Participants  This prospective study was part of the Melbourne Genomics Health Alliance demonstration project. At the ambulatory outpatient clinics of the Victorian Clinical Genetics Services at the Royal Children’s Hospital, Melbourne, Australia, children older than 2 years suspected of having a monogenic disorder were prospectively recruited from May 1 through November 30, 2015, by clinical geneticists after referral from general and subspecialist pediatricians. All children had nondiagnostic microarrays and no prior single-gene or panel sequencing.

Exposures  All children underwent singleton WES with targeted phenotype-driven analysis.

Main Outcomes and Measures  The study examined the clinical utility of a molecular diagnosis and the cost-effectiveness of alternative diagnostic trajectories, depending on timing of WES.

Results  Of 61 children originally assessed, 44 (21 [48%] male and 23 [52%] female) aged 2 to 18 years (mean age at initial presentation, 28 months; range, 0-121 months) were recruited, and a diagnosis was achieved in 23 (52%) by singleton WES. The diagnoses were unexpected in 8 of 23 (35%), and clinical management was altered in 6 of 23 (26%). The mean duration of the diagnostic odyssey was 6 years, with each child having a mean of 19 tests and 4 clinical genetics and 4 nongenetics specialist consultations, and 26 (59%) underwent a procedure while under general anesthetic for diagnostic purposes. Economic analyses of the diagnostic trajectory identified that WES performed at initial tertiary presentation resulted in an incremental cost savings of A$9020 (US$6838) per additional diagnosis (95% CI, A$4304-A$15 404 [US$3263-US$11 678]) compared with the standard diagnostic pathway. Even if WES were performed at the first genetics appointment, there would be an incremental cost savings of A$5461 (US$4140) (95% CI, A$1433-A$10 557 [US$1086- US$8004]) per additional diagnosis compared with the standard diagnostic pathway.

Conclusions and Relevance  Singleton WES in children with suspected monogenic conditions has high diagnostic yield, and cost-effectiveness is maximized by early application in the diagnostic pathway. Pediatricians should consider early referral of children with undiagnosed syndromes to clinical geneticists.

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Article Information

Corresponding Author: Clara Gaff, PhD, Melbourne Genomics Health Alliance, 1G Royal Parade, Parkville, Victoria 3052, Australia (clara.gaff@melbournegenomics.org.au).

Accepted for Publication: May 1, 2017.

Published Online: July 31, 2017. doi:10.1001/jamapediatrics.2017.1755

Author Contributions: Drs Gaff and White contributed equally to this work. Drs Tan and White had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Tan, Dillon, Stark, Schofield, Alam, Amor, Macciocca, Gaff, White.

Acquisition, analysis, or interpretation of data: Tan, Dillon, Stark, Schofield, Alam, Shrestha, Chong, Phelan, Brett, Creed, Jarmolowicz, Yap, Walsh, Downie, Amor, Savarirayan, McGillivray, Yeung, Peters, Robertson, Robinson, Sadedin, Bell, Oshlack, Georgeson, Thorne, White.

Drafting of the manuscript: Tan, Dillon, Schofield, Alam, Shrestha, Chong, Amor, Robertson, Robinson, Georgeson, White.

Critical revision of the manuscript for important intellectual content: Tan, Stark, Schofield, Alam, Amor, Yeung, Macciocca, Sadedin, Bell, Oshlack, Thorne, Gaff, White.

Statistical analysis: Tan, Dillon, Schofield, Alam, Shrestha, Oshlack, Georgeson.

Obtained funding: Amor, Gaff.

Administrative, technical, or material support: Tan, Dillon, Alam, Phelan, Brett, Creed, Jarmolowicz, Yap, Downie, Yeung, Peters, Robertson, Robinson, Macciocca, Sadedin, Georgeson.

Study supervision: Tan, Stark, Schofield, Oshlack, White.

Conflict of Interest Disclosures: None reported.

Funding/Support: The study was funded by the founding organizations of the Melbourne Genomics Health Alliance (Royal Melbourne Hospital, Royal Children’s Hospital, University of Melbourne, Walter and Eliza Hall Institute, Murdoch Childrens Research Institute, Australian Genome Research Facility, and Commonwealth Scientific and Industrial Research Organisation) and the State Government of Victoria (Department of Health and Human Services). The involvement of Australian Genome Research Facility was supported by sponsorship from Bioplatforms Australia and the National Collaborative Research Infrastructure Strategy program.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Additional Contributions: Elin Wee, clinical costings officer at the Royal Children’s Hospital, and Leeanne Cavanough provided administrative support. They were not compensated for their work. We thank all collaborators in the Melbourne Genomics Health Alliance demonstration project.

References
1.
Kumar  P, Radhakrishnan  J, Chowdhary  MA, Giampietro  PF.  Prevalence and patterns of presentation of genetic disorders in a pediatric emergency department.  Mayo Clin Proc. 2001;76(8):777-783.PubMedGoogle ScholarCrossref
2.
McCandless  SE, Brunger  JW, Cassidy  SB.  The burden of genetic disease on inpatient care in a children’s hospital.  Am J Hum Genet. 2004;74(1):121-127.PubMedGoogle ScholarCrossref
3.
Yoon  PW, Olney  RS, Khoury  MJ, Sappenfield  WM, Chavez  GF, Taylor  D.  Contribution of birth defects and genetic diseases to pediatric hospitalizations: a population-based study.  Arch Pediatr Adolesc Med. 1997;151(11):1096-1103.PubMedGoogle ScholarCrossref
4.
Miller  DT, Adam  MP, Aradhya  S,  et al.  Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies.  Am J Hum Genet. 2010;86(5):749-764.PubMedGoogle ScholarCrossref
5.
Biesecker  LG, Green  RC.  Diagnostic clinical genome and exome sequencing.  N Engl J Med. 2014;370(52):2418-2425.PubMedGoogle ScholarCrossref
6.
Boycott  K, Hartley  T, Adam  S,  et al; Canadian College of Medical Geneticists.  The clinical application of genome-wide sequencing for monogenic diseases in Canada: Position Statement of the Canadian College of Medical Geneticists.  J Med Genet. 2015;52(7):431-437.PubMedGoogle ScholarCrossref
7.
Lazaridis  KN, Schahl  KA, Cousin  MA,  et al; Individualized Medicine Clinic Members.  Outcome of whole exome sequencing for diagnostic odyssey cases of an individualized medicine clinic: the Mayo Clinic experience.  Mayo Clin Proc. 2016;91(3):297-307.PubMedGoogle ScholarCrossref
8.
Neveling  K, Feenstra  I, Gilissen  C,  et al.  A post-hoc comparison of the utility of sanger sequencing and exome sequencing for the diagnosis of heterogeneous diseases.  Hum Mutat. 2013;34(12):1721-1726.PubMedGoogle ScholarCrossref
9.
Sawyer  SL, Hartley  T, Dyment  DA,  et al; FORGE Canada Consortium; Care4Rare Canada Consortium.  Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care.  Clin Genet. 2016;89(3):275-284.PubMedGoogle ScholarCrossref
10.
Lee  H, Deignan  JL, Dorrani  N,  et al.  Clinical exome sequencing for genetic identification of rare mendelian disorders.  JAMA. 2014;312(18):1880-1887.PubMedGoogle ScholarCrossref
11.
Yang  Y, Muzny  DM, Xia  F,  et al.  Molecular findings among patients referred for clinical whole-exome sequencing.  JAMA. 2014;312(18):1870-1879.PubMedGoogle ScholarCrossref
12.
Yang  Y, Muzny  DM, Reid  JG,  et al.  Clinical whole-exome sequencing for the diagnosis of mendelian disorders.  N Engl J Med. 2013;369(16):1502-1511.PubMedGoogle ScholarCrossref
13.
Valencia  CA, Husami  A, Holle  J,  et al.  Clinical impact and cost-effectiveness of whole exome sequencing as a diagnostic tool: a pediatric center’s experience.  Front Pediatr. 2015;3:67.PubMedGoogle ScholarCrossref
14.
Shashi  V, McConkie-Rosell  A, Rosell  B,  et al.  The utility of the traditional medical genetics diagnostic evaluation in the context of next-generation sequencing for undiagnosed genetic disorders.  Genet Med. 2014;16(2):176-182.PubMedGoogle ScholarCrossref
15.
Monroe  GR, Frederix  GW, Savelberg  SM,  et al.  Effectiveness of whole-exome sequencing and costs of the traditional diagnostic trajectory in children with intellectual disability.  Genet Med. 2016;18(9):949-956.PubMedGoogle ScholarCrossref
16.
Stark  Z, Tan  TY, Chong  B,  et al; Melbourne Genomics Health Alliance.  A prospective evaluation of whole-exome sequencing as a first-tier molecular test in infants with suspected monogenic disorders.  Genet Med. 2016;18(11):1090-1096.PubMedGoogle ScholarCrossref
17.
Girdea  M, Dumitriu  S, Fiume  M,  et al.  PhenoTips: patient phenotyping software for clinical and research use.  Hum Mutat. 2013;34(8):1057-1065.PubMedGoogle ScholarCrossref
18.
Gaff  CL, Winship  IM, Forrest  S,  et al. Preparing for genomic medicine: a real world demonstration of health system change. NPJ Genom Med. 2017;2(16). https://www.nature.com/articles/s41525-017-0017-4. Accessed March 7, 2017.
19.
Sadedin  SP, Dashnow  H, James  PA,  et al; Melbourne Genomics Health Alliance.  Cpipe: a shared variant detection pipeline designed for diagnostic settings.  Genome Med. 2015;7(1):68.PubMedGoogle ScholarCrossref
20.
Fokkema  IF, Taschner  PE, Schaafsma  GC, Celli  J, Laros  JF, den Dunnen  JT.  LOVD v.2.0: the next generation in gene variant databases.  Hum Mutat. 2011;32(5):557-563.PubMedGoogle ScholarCrossref
21.
Richards  CS, Bale  S, Bellissimo  DB,  et al; Molecular Subcommittee of the ACMG Laboratory Quality Assurance Committee.  ACMG recommendations for standards for interpretation and reporting of sequence variations: revisions 2007.  Genet Med. 2008;10(4):294-300.PubMedGoogle ScholarCrossref
22.
Chapko  MK, Liu  CF, Perkins  M, Li  YF, Fortney  JC, Maciejewski  ML.  Equivalence of two healthcare costing methods: bottom-up and top-down.  Health Econ. 2009;18(10):1188-1201.PubMedGoogle ScholarCrossref
23.
Forouzanfar  MH, Alexander  L, Anderson  HR,  et al; GBD 2013 Risk Factors Collaborators.  Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.  Lancet. 2015;386(10010):2287-2323.PubMedGoogle ScholarCrossref
24.
Briggs  AH, Wonderling  DE, Mooney  CZ.  Pulling cost-effectiveness analysis up by its bootstraps: a non-parametric approach to confidence interval estimation.  Health Econ. 1997;6(4):327-340.PubMedGoogle ScholarCrossref
25.
Gilissen  C, Hehir-Kwa  JY, Thung  DT,  et al.  Genome sequencing identifies major causes of severe intellectual disability.  Nature. 2014;511(7509):344-347.PubMedGoogle ScholarCrossref
26.
Retterer  K, Juusola  J, Cho  MT,  et al.  Clinical application of whole-exome sequencing across clinical indications.  Genet Med. 2016;18(7):696-704.PubMedGoogle ScholarCrossref
27.
Ankala  A, da Silva  C, Gualandi  F,  et al.  A comprehensive genomic approach for neuromuscular diseases gives a high diagnostic yield.  Ann Neurol. 2015;77(2):206-214.PubMedGoogle ScholarCrossref
28.
Saudi Mendeliome  G; Saudi Mendeliome Group.  Comprehensive gene panels provide advantages over clinical exome sequencing for Mendelian diseases.  Genome Biol. 2015;16:134.PubMedGoogle ScholarCrossref
29.
Petrikin  JE, Willig  LK, Smith  LD, Kingsmore  SF.  Rapid whole genome sequencing and precision neonatology.  Semin Perinatol. 2015;39(8):623-631.PubMedGoogle ScholarCrossref
30.
Saunders  CJ, Miller  NA, Soden  SE,  et al.  Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units.  Sci Transl Med. 2012;4(154):154ra135.PubMedGoogle ScholarCrossref
31.
Soden  SE, Saunders  CJ, Willig  LK,  et al.  Effectiveness of exome and genome sequencing guided by acuity of illness for diagnosis of neurodevelopmental disorders.  Sci Transl Med. 2014;6(265):265ra168.PubMedGoogle ScholarCrossref
32.
van Nimwegen  KJ, Schieving  JH, Willemsen  MA,  et al.  The diagnostic pathway in complex paediatric neurology: a cost analysis.  Eur J Paediatr Neurol. 2015;19(2):233-239.PubMedGoogle ScholarCrossref
33.
Farwell  KD, Shahmirzadi  L, El-Khechen  D,  et al.  Enhanced utility of family-centered diagnostic exome sequencing with inheritance model-based analysis: results from 500 unselected families with undiagnosed genetic conditions.  Genet Med. 2015;17(7):578-586.PubMedGoogle ScholarCrossref
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