4.6 Article

The cost and diagnostic yield of exome sequencing for children with suspected genetic disorders: a benchmarking study

Journal

GENETICS IN MEDICINE
Volume 20, Issue 9, Pages 1013-1021

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/gim.2017.226

Keywords

cost; cost-effectiveness; diagnostic yield; exome sequencing; pediatric patients

Funding

  1. Genome British Columbia
  2. Provincial Health Services Authority
  3. Provincial Medical Genetics Program, BC Children's Hospital Pathology and Laboratory Medicine
  4. University of British Columbia
  5. Canadian Institutes of Health Research Emerging Team for Rare Diseases grant [119193]
  6. BC Children's Hospital
  7. BC Women's Hospital

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Purpose: This study aimed to generate benchmark estimates for the cost, diagnostic yield, and cost per positive diagnosis of diagnostic exome sequencing (ES) in heterogeneous pediatric patient populations and to illustrate how the design of an ES service can influence its cost and yield. Methods: A literature review and Monte Carlo simulations were used to generate benchmark estimates for singleton and trio ES. A cost model for the Clinical Assessment of the Utility of Sequencing and Evaluation as a Service (CAUSES) study, which is testing a proposed delivery model for diagnostic ES in British Columbia, is used to illustrate the potential effects of changing the service design. Results: The benchmark diagnostic yield was 34.3% (95% confidence interval (CI): 23.2-46.5) for trio ES and 26.5% (95% CI: 12.9-42.9) for singleton ES. The benchmark cost of delivery was C$ 6,437 (95% CI: $ 5,305-$ 7,704) in 2016 Canadian dollars (US $ 4,859; 4,391(sic)) for trio ES and C$ 2,576 (95% CI: $ 1,993-$ 3,270) (US$ 1,944; 1,757(sic)) for singleton ES. Scenario models for CAUSES suggest that alternative service designs could reduce costs but might lead to a higher cost per diagnosis due to lower yields. Conclusion: Broad conclusions about the cost-effectiveness of ES should be drawn with caution when relying on studies that use cost or yield assumptions that lie at the extremes of the benchmark ranges.

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