Abstract
We constructed a cost-effectiveness model to assess the clinical and economic value of a CDS alert program that provides pharmacogenomic (PGx) testing results, compared to no alert program in acute coronary syndrome (ACS) and atrial fibrillation (AF), from a health system perspective. We defaulted that 20% of 500,000 health-system members between the ages of 55 and 65 received PGx testing for CYP2C19 (ACS-clopidogrel) and CYP2C9, CYP4F2 and VKORC1 (AF-warfarin) annually. Clinical events, costs, and quality-adjusted life years (QALYs) were calculated over 20 years with an annual discount rate of 3%. In total, 3169 alerts would be fired. The CDS alert program would help avoid 16 major clinical events and 6 deaths for ACS; and 2 clinical events and 0.9 deaths for AF. The incremental cost-effectiveness ratio was $39,477/QALY. A PGx-CDS alert program was cost-effective, under a willingness-to-pay threshold of $100,000/QALY gained, compared to no alert program.
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Data availability
All data used in the model are publicly available and available by directly contacting the authors, as well as being included in the manuscript.
References
Collins FS, Varmus H. A New Initiative on Precision Medicine. N Engl J Med. 2015;372:793–5.
Relling M, Evans W. Pharmacogenomics in the clinic. Nature. 2015;526:343–50.
CPIC [Internet]. [cited 2022 Mar 22]. Available from: https://cpicpgx.org/.
Dunnenberger HM, Crews KR, Hoffman JM, Caudle KE, Broeckel U, Howard SC, et al. Preemptive clinical pharmacogenetics implementation: Current programs in five us medical centers. Annu Rev Pharmacol Toxicol. 2015;55:89–106.
Hocum BT, White JR, Heck JW, Thirumaran RK, Moyer N, Newman R, et al. Cytochrome P-450 gene and drug interaction analysis in patients referred for pharmacogenetic testing. Am J Health Syst Pharm. 2016;73:61–7.
van der Wouden CH, Cambon-Thomsen A, Cecchin E, Cheung KC, Dávila-Fajardo CL, Deneer VH, et al. Implementing Pharmacogenomics in Europe: Design and Implementation Strategy of the Ubiquitous Pharmacogenomics Consortium. Clin Pharmacol Ther. 2017;101:341–58.
Institute of Medicine (US) Roundtable on Evidence-Based Medicine. The learning healthcare system. In: Olsen L, Aisner D, McGinnis JM,eds. Workshop Summary. Washington (DC): National Academies Press(US); 2007. Available from: https://www.ncbi.nlm.nih.gov/books/NBK53494/.
Foley T, Fairmichael F. The potential of learning health care systems. The Learning Health Care Project. [Internet]. [cited 2022 Mar 23]. Available from: https://learninghealthcareproject.org/wp-content/uploads/2015/11/LHS_Report_2015.pdf.
Greene SM, Reid RJ, Larson EB. Implementing the learning health system: from concept to action. Ann Intern Med. 2012;157:207–10.
Welch BM, Eilbeck K, Del FG, Meyer LJ, Kawamoto K. Technical desiderata for the integration of genomic data with clinical decision support. J Biomed Inform. 2014;51:3–7.
Hess GP, Fonseca E, Scott R, Fagerness J. Pharmacogenomic and pharmacogenetic-guided therapy as a tool in precision medicine: current state and factors impacting acceptance by stakeholders. Genet Res (Camb). 2015;97:e13.
Stanek EJ, Sanders CL, Taber KAJ, Khalid M, Patel A, Verbrugge RR, et al. Adoption of pharmacogenomic testing by US physicians: Results of a nationwide survey. Clin Pharmacol Ther. 2012;91:450–8.
Haga S, Burke W, Ginsburg G, Mills R, Agans R. Primary care physicians’ knowledge of and experience with pharmacogenetic testing. Clin Genet. 2012;82:388–94.
Kim K, Magness JW, Nelson R, Baron V, Brixner DI. Clinical utility of pharmacogenetic testing and a clinical decision support tool to enhance the identification of drug therapy problems through medication therapy management in polypharmacy patients. J Manag Care Spec Pharm. 2018;24:1251–9.
Blagec K, Koopmann R, Crommentuijn-Van Rhenen M, Holsappel I, Van Der Wouden CH, Konta L, et al. Implementing pharmacogenomics decision support across seven European countries: The Ubiquitous Pharmacogenomics (U-PGx) project. J Am Med Inform Assoc. 2018;25:893–8.
Berner ES, La Lande TJ. Overview of Clinical Decision Support Systems. In: Berner ES, eds. Clinical Decision Support Systems: Theory and Practice. New York, NY: Springer New York; 2007. p. 3–22.
Eichner J, Das M. Challenges and Barriers to Clinical Decision Support (CDS) Design and Implementation Experienced in the Agency for Healthcare Research and Quality CDS Demonstrations [Internet]. 2010. Available from: https://healthit.ahrq.gov/sites/default/files/docs/page/CDS_challenges_and_barriers.pdf.
Welch BM, Kawamoto K. Clinical decision support for genetically guided personalized medicine: a systematic review. J Am Med Inform Assoc. 2013;20:388–400.
Sebastian A, Carroll JC, Oldfield LE, Mighton C, Shickh S, Uleryk E, et al. Effect of genetics clinical decision support tools on health-care providers’ decision making: a mixed-methods systematic review. Genet Med. 2021;23:593–602.
Liberati EG, Ruggiero F, Galuppo L, Gorli M, González-Lorenzo M, Maraldi M, et al. What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement Sci. 2017;12:1–13.
Devaraj S, Sharma SK, Fausto DJ, Viernes S, Kharrazi H. Barriers and facilitators to clinical decision support systems adoption: a systematic review. J Bus Adm Res. 2014;3:36–53.
Zhu Y, Swanson KM, Rojas RL, Wang Z, St. Sauver JL, Visscher SL, et al. Systematic review of the evidence on the cost-effectiveness of pharmacogenomics-guided treatment for cardiovascular diseases. Genet Med. 2020;22:475–86.
AlMukdad S, Elewa H, Al-Badriyeh D. Economic Evaluations of CYP2C19 Genotype-Guided Antiplatelet Therapy Compared to the Universal Use of Antiplatelets in Patients With Acute Coronary Syndrome: A Systematic Review. J Cardiovasc Pharmacol Ther. 2020;25:201–11.
Yoon HY, Lee N, Seong JM, Gwak HS. Efficacy and safety of clopidogrel versus prasugrel and ticagrelor for coronary artery disease treatment in patients with CYP2C19 LoF alleles: a systemic review and meta-analysis. Br J Clin Pharmacol. 2020;86:1489–98.
Vries MJA, van der Meijden PEJ, Henskens YMC, ten Cate-Hoek AJ, ten Cate H. Assessment of bleeding risk in patients with coronary artery disease on dual antiplatelet therapy: a systematic review. Thromb Haemost. 2016;115:7–24.
Dahabreh IJ, Moorthy D, Lamont JL, Chen ML, Kent DM, Lau J. Testing of CYP2C19 Variants and Platelet Reactivity for Guiding Antiplatelet Treatment. Agency for Healthcare Research and Quality (US), Rockville (MD); 2013. Available from: https://www.ncbi.nlm.nih.gov/books/NBK236984/.
Goulding R, Dawes D, Price M, Wilkie S, Dawes M. Genotype-guided drug prescribing: a systematic review and meta-analysis of randomized control trials. Br J Clin Pharmacol. 2015;80:868–77.
Tang Q, Zou H, Guo C, Liu Z. Outcomes of pharmacogenetics-guided dosing of warfarin: a systematic review and meta-analysis. Int J Cardiol. 2014;175:587–91.
Wang ZQ, Zhang R, Zhang PP, Liu XH, Sun J, Wang J, et al. Pharmacogenetics-based warfarin dosing algorithm decreases time to stable anticoagulation and the risk of major hemorrhage: an updated meta-analysis of randomized controlled trials. J Cardiovasc Pharmacol. 2015;65:364–70.
Franchini M, Mengoli C, Cruciani M, Bonfanti C, Mannucci PM. Effects on bleeding complications of pharmacogenetic testing for initial dosing of vitamin K antagonists: a systematic review and meta-analysis. J Thromb Haemost. 2014;12:1480–7.
Sanders GD, Neumann PJ, Basu A, Brock DW, Feeny D, Krahn M, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health and medicine. JAMA. 2016;316:1093–103.
U.S. Census Bureau QuickFacts: United States [Internet]. [cited 2022 Mar 22]. Available from: https://www.census.gov/quickfacts/fact/table/US/PST045219.
Scott SA, Sangkuhl K, Stein CM, Hulot JS, Mega JL, Roden DM, et al. Clinical pharmacogenetics implementation consortium guidelines for CYP2C19 genotype and clopidogrel therapy: 2013 update. Clin Pharmacol Ther. 2013;94:317–23.
Johnson JA, Caudle KE, Gong L, Whirl-Carrillo M, Stein CM, Scott SA, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Pharmacogenetics-Guided Warfarin Dosing: 2017 Update. Clin Pharmacol Ther. 2017;102:397–404.
Anderson JL, Horne BD, Stevens SM, Grove AS, Barton S, Nicholas ZP, et al. Randomized trial of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation. Circulation. 2007;116:2563–70.
Caraco Y, Blotnick S, Muszkat M. CYP2C9 genotype-guided warfarin prescribing enhances the efficacy and safety of anticoagulation: a prospective randomized controlled study. Clin Pharmacol Ther. 2008;83:460–70.
Burmester JK, Berg RL, Yale SH, Rottscheit CM, Glurich IE, Schmelzer JR, et al. A randomized controlled trial of genotype-based Coumadin initiation. Genet Med. 2011;13:509–18.
Borgman MP, Pendleton RC, McMillin GA, Reynolds KK, Vazquez S, Freeman A, et al. Prospective pilot trial of PerMIT versus standard anticoagulation service management of patients initiating oral anticoagulation. Thromb Haemost. 2012;108:561–9.
Kimmel SE, French B, Kasner SE, Johnson JA, Anderson JL, Gage BF, et al. A pharmacogenetic versus a clinical algorithm for warfarin dosing. N Engl J Med. 2013;369:2283–93.
Jonas DE, Evans JP, McLeod HL, Brode S, Lange LA, Young ML, et al. Impact of genotype-guided dosing on anticoagulation visits for adults starting warfarin: a randomized controlled trial. Pharmacogenomics. 2013;14:1593–603.
Verhoef TI, Ragia G, de Boer A, Barallon R, Kolovou G, Kolovou V, et al. A randomized trial of genotype-guided dosing of acenocoumarol and phenprocoumon. N Engl J Med. 2013;369:2304–12.
MarketScan Research Databases | IBM [Internet]. [cited 2022 Mar 22]. Available from: https://www.ibm.com/products/marketscan-research-databases.
McCoy AB, Thomas EJ, Krousel-Wood M, Sittig DF. Clinical decision support alert appropriateness: a review and proposal for improvement. Ochsner J. 2014;14:195–202.
Brodowy B, Nguyen D. Optimization of clinical decision support through minimization of excessive drug allergy alerts. Am J Health Syst Pharm. 2016;73:526–8.
Bryant AD, Fletcher GS, Payne TH. Drug interaction alert override rates in the Meaningful Use era: no evidence of progress. Appl Clin Inform. 2014;5:802–13.
Hsieh TC, Kuperman GJ, Jaggi T, Hojnowski-Diaz P, Fiskio J, Williams DH, et al. Characteristics and consequences of drug allergy alert overrides in a computerized physician order entry system. J Am Med Inform Assoc. 2004;11:482–91.
Lam JH, Ng O. Monitoring clinical decision support in the electronic health record. Am J Health Syst Pharm. 2017;74:1130–4.
Nanji KC, Seger DL, Slight SP, Amato MG, Beeler PE, Her QL, et al. Medication-related clinical decision support alert overrides in inpatients. J Am Med Inform Assoc. 2018;25:476–81.
Kawamanto K, Flynn MC, Kukhareva P, ElHalta D, Hess R, Gregory T, et al. A Pragmatic Guide to Establishing Clinical Decision Support Governance and Addressing Decision Support Fatigue: a Case Study. AMIA Annu Symp Proc. 2018;2018:624–33.
Duke JD, Li X, Dexter P. Adherence to drug-drug interaction alerts in high-risk patients: a trial of context-enhanced alerting. J Am Med Inform Assoc. 2013;20:494–8.
Kazi DS, Garber AM, Shah RU, Dudley RA, Mell MW, Rhee C, et al. Cost-effectiveness of genotype-guided and dual antiplatelet therapies in acute coronary syndrome. Ann Intern Med. 2014;160:221–32.
Dhanda DS, Guzauskas GF, Carlson JJ, Basu A, Veenstra DL. Are Evidence Standards Different for Genomic- vs. Clinical-Based Precision Medicine? A Quantitative Analysis of Individualized Warfarin Therapy. Clin Pharmacol Ther. 2017;102:805–14.
Mathias PC, Tarczy-Hornoch P, Shirts BH. Modeling the costs of clinical decision support for genomic precision medicine. AMIA Jt Summits Transl Sci Proc. 2016;2016:60–4.
CPI Home: U.S. Bureau of Labor Statistics [Internet]. [cited 2022 Mar 22]. Available from: https://www.bls.gov/cpi/.
Briggs AH, Weinstein MC, Fenwick EAL, Karnon J, Sculpher MJ, Paltiel AD. Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6. Value Health. 2012;15:835–42.
Overby CL, Kohane I, Kannry JL, Williams MS, Starren J, Bottinger E, et al. Opportunities for genomic clinical decision support interventions. Genet Med. 2013;15:817–23.
Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17.
Kawamoto K, Del Fiol G, Lobach DF, Jenders RA. Standards for scalable clinical decision support: need, current and emerging standards, gaps, and proposal for progress. Open Med Inform J. 2010;4:235–44.
Liu D, Olson KL, Manzi SF, Mandl KD. Patients dispensed medications with actionable pharmacogenomic biomarkers: rates and characteristics. Genet Med. 2021;23:782–6.
Value and Science-Driven Health Care - National Academy of Medicine [Internet]. [cited 2022 Mar 22]. Available from: https://nam.edu/programs/value-science-driven-health-care/.
McGinnis JM, Fineberg HV, Dzau VJ. Advancing the Learning Health System. N Engl J Med. 2021;385:1–5.
Digital Healthcare Research [Internet]. [cited 2022 Mar 23]. Available from: https://digital.ahrq.gov/.
Funding
This project was funded by Agency for Healthcare Research and Quality (AHRQ) R21-HS26544.
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SJ was responsible for performing literature review, analyzing data, and manuscript preparation. SJ, PCM, NH, DV and BD developed the cost-utility model. PCM, BHS, PTH, DV, DM, and BD contributed to research development. All authors provided with constructive suggestions in the manuscript. All authors reviewed the results and approved the final version of the manuscript.
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Jiang, S., Mathias, P.C., Hendrix, N. et al. Implementation of pharmacogenomic clinical decision support for health systems: a cost-utility analysis. Pharmacogenomics J 22, 188–197 (2022). https://doi.org/10.1038/s41397-022-00275-7
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DOI: https://doi.org/10.1038/s41397-022-00275-7