In:
Clinical Chemistry, Oxford University Press (OUP), Vol. 67, No. 8 ( 2021-08-05), p. 1122-1132
Abstract:
Multi–gene panel sequencing using next-generation sequencing (NGS) methods is a key tool for genomic medicine. However, with an estimated 140 000 genomic tests available, current system inefficiencies result in high genetic-testing costs. Reduced testing costs are needed to expand the availability of genomic medicine. One solution to improve efficiency and lower costs is to calculate the most cost-effective set of panels for a typical pattern of test requests. Methods We compiled rare diseases, associated genes, point prevalence, and test-order frequencies from a representative laboratory. We then modeled the costs of the relevant steps in the NGS process in detail. Using a simulated annealing-based optimization procedure, we determined panel sets that were more cost-optimal than whole exome sequencing (WES) or clinical exome sequencing (CES). Finally, we repeated this methodology to cost-optimize pharmacogenomics (PGx) testing. Results For rare disease testing, we show that an optimal choice of 4–6 panels, uniquely covering genes that comprise 95% of the total prevalence of monogenic diseases, saves $257–304 per sample compared with WES, and $66–135 per sample compared with CES. For PGx, we show that the optimal multipanel solution saves $6–7 (27%–40%) over a single panel covering all relevant gene–drug associations. Conclusions Laboratories can reduce costs using the proposed method to obtain and run a cost-optimal set of panels for specific test requests. In addition, payers can use this method to inform reimbursement policy.
Type of Medium:
Online Resource
ISSN:
0009-9147
,
1530-8561
DOI:
10.1093/clinchem/hvab059
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2021
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