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The impact of biomarker screening and cascade genetic testing on the cost-effectiveness of MODY genetic testing

Authors
  • GoodSmith, MS
  • Skandari, M
  • Huang, E
  • Naylor, R
Publication Date
Sep 10, 2019
Source
Spiral - Imperial College Digital Repository
Keywords
License
Unknown

Abstract

OBJECTIVE In the U.S., genetic testing for maturity-onset diabetes of the young (MODY) is frequently delayed because of difficulty with insurance coverage. Understanding the economic implications of clinical genetic testing is imperative to advance precision medicine for diabetes. The objective of this article is to assess the cost-effectiveness of genetic testing, preceded by biomarker screening and followed by cascade genetic testing of first-degree relatives, for subtypes of MODY in U.S. pediatric patients with diabetes. RESEARCH DESIGN AND METHODS We used simulation models of distinct forms of diabetes to forecast the clinical and economic consequences of a systematic genetic testing strategy compared with usual care over a 30-year time horizon. In the genetic testing arm, patients with MODY received treatment changes (sulfonylureas for HNF1A- and HNF4A-MODY associated with a 1.0% reduction in HbA1c; no treatment for GCK-MODY). Study outcomes included costs, life expectancy (LE), and quality-adjusted life years (QALY). RESULTS The strategy of biomarker screening and genetic testing was cost-saving as it increased average quality of life (+0.0052 QALY) and decreased costs (−$191) per simulated patient relative to the control arm. Adding cascade genetic testing increased quality-of-life benefits (+0.0081 QALY) and lowered costs further (−$735). CONCLUSIONS A combined strategy of biomarker screening and genetic testing for MODY in the U.S. pediatric diabetes population is cost-saving compared with usual care, and the addition of cascade genetic testing accentuates the strategy’s benefits. Widespread implementation of this strategy could improve the lives of patients with MODY while saving the health system money, illustrating the potential population health benefits of personalized medicine.

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