AI Article Synopsis

  • The text outlines the challenges of conducting economic evaluations of genome sequencing (GS) and exome sequencing (ES), aiming to create frameworks for cost-effectiveness analyses (CEAs) in various clinical scenarios.
  • A scoping review of 57 economic studies was conducted to develop these frameworks, which cover prenatal testing, pediatric diagnoses, hereditary cancer testing, tumor profiling, and general population screening.
  • The proposed frameworks emphasize the importance of considering costs, quality-adjusted life years (QALYs), and long-term implications, with the goal of improving the evidence supporting the economic value of GS/ES.

Article Abstract

Purpose: Methodological challenges have limited economic evaluations of genome sequencing (GS) and exome sequencing (ES). Our objective was to develop conceptual frameworks for model-based cost-effectiveness analyses (CEAs) of diagnostic GS/ES.

Methods: We conducted a scoping review of economic analyses to develop and iterate with experts a set of conceptual CEA frameworks for GS/ES for prenatal testing, early diagnosis in pediatrics, diagnosis of delayed-onset disorders in pediatrics, genetic testing in cancer, screening of newborns, and general population screening.

Results: Reflecting on 57 studies meeting inclusion criteria, we recommend the following considerations for each clinical scenario. For prenatal testing, performing comparative analyses of costs of ES strategies and postpartum care, as well as genetic diagnoses and pregnancy outcomes. For early diagnosis in pediatrics, modeling quality-adjusted life years (QALYs) and costs over ≥20 years for rapid turnaround GS/ES. For hereditary cancer syndrome testing, modeling cumulative costs and QALYs for the individual tested and first/second/third-degree relatives. For tumor profiling, not restricting to treatment uptake or response and including QALYs and costs of downstream outcomes. For screening, modeling lifetime costs and QALYs and considering consequences of low penetrance and GS/ES reanalysis.

Conclusion: Our frameworks can guide the design of model-based CEAs and ultimately foster robust evidence for the economic value of GS/ES.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997042PMC
http://dx.doi.org/10.1016/j.gim.2022.06.004DOI Listing

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