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Cost-effectiveness of CYP2C19-guided antiplatelet therapy in patients with acute coronary syndrome and percutaneous coronary intervention informed by real-world data

The Pharmacogenomics Journal volume 20, pages 724–735 (2020)Cite this article

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Abstract

Current guidelines recommend dual antiplatelet therapy (DAPT) consisting of aspirin and a P2Y12 inhibitors following percutaneous coronary intervention (PCI). CYP2C19 genotype can guide DAPT selection, prescribing ticagrelor or prasugrel for loss-of-function (LOF) allele carriers (genotype-guided escalation). Cost-effectiveness analyses (CEA) are traditionally grounded in clinical trial data. We conduct a CEA using real-world data using a 1-year decision-analytic model comparing primary strategies: universal empiric clopidogrel (base case), universal ticagrelor, and genotype-guided escalation. We also explore secondary strategies commonly implemented in practice, wherein all patients are prescribed ticagrelor for 30 days post PCI. After 30 days, all patients are switched to clopidogrel irrespective of genotype (nonguided de-escalation) or to clopidogrel only if patients do not harbor an LOF allele (genotype-guided de-escalation). Compared with universal clopidogrel, both universal ticagrelor and genotype-guided escalation were superior with improvement in quality-adjusted life years (QALY’s). Only genotype-guided escalation was cost-effective ($42,365/QALY) and demonstrated the highest probability of being cost-effective across conventional willingness-to-pay thresholds. In the secondary analysis, compared with the nonguided de-escalation strategy, although genotype-guided de-escalation and universal ticagrelor were more effective, with ICER of $188,680/QALY and $678,215/QALY, respectively, they were not cost-effective. CYP2C19 genotype-guided antiplatelet prescribing is cost-effective compared with either universal clopidogrel or universal ticagrelor using real-world implementation data. The secondary analysis suggests genotype-guided and nonguided de-escalation may be viable strategies, needing further evaluation.

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Fig. 1: Decision-analytic model simulating outcomes of five strategies over a 1-year horizon.
Fig. 2: Tornado plots of influential factors from the probabilistic sensitivity analyses (PSA).
Fig. 3

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Acknowledgements

This work was supported by grants from the National Institutes of Health: RO1HL092173 and K24HL133373, Clinical and Translational Science Award UL1TR000165, University of Alabama Birmingham’s Health Service Foundations’ General Endowment Fund and Hugh Kaul Personalized Medicine Institute (NAL, CD); U01 HG007269, U01 GM074492 and U01 HL105198, UL1 TR000064 and UL1 TR001427 (LHC, YG, JAJ); U01 HG007762 (AMH, TCS), K23 GM112014 (JDD), UL1 TR001857, an Anonymous Donor, and internal funds from UPMC, the University of Pittsburgh Clinical and Translational Science Institute, and the Institute for Precision Medicine (PEE), U01HG007775 (ALB) NIH Common Fund Program in Health Economics; NHGRI 1R01HG009694–01and NHLBI U01HL122904 (JFP).

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Authors and Affiliations

  1. Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA

    Nita A. Limdi & Chrisly Dillon

  2. Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA

    Larisa H. Cavallari, Julio D. Duarte, Yan Gong & Julie A. Johnson

  3. Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Craig R. Lee

  4. Departments of Data Science and Medicine, University of Mississippi Medical Center, Jackson, MS, USA

    William B. Hillegass

  5. Indiana University–Purdue University Indianapolis, Indianapolis, IN, USA

    Ann M. Holmes

  6. Indiana University, Indianapolis, IN, USA

    Todd C. Skaar

  7. Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, AL, USA

    Maria Pisu

  8. Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland, Baltimore, MD, USA

    Amber L. Beitelshees

  9. Department of Pharmacy and Therapeutics, Center for Clinical Pharmaceutical Sciences, University of Pittsburgh School of Pharmacy, Pittsburgh, PA, USA

    Philip E. Empey

  10. Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA

    Vakaramoko Diaby

  11. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

    John Graves, Shawn Garbett, Zilu Zhou & Josh F. Peterson

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Limdi, N.A., Cavallari, L.H., Lee, C.R. et al. Cost-effectiveness of CYP2C19-guided antiplatelet therapy in patients with acute coronary syndrome and percutaneous coronary intervention informed by real-world data. Pharmacogenomics J 20, 724–735 (2020). https://doi.org/10.1038/s41397-020-0162-5

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