A widely-held vision arising from the Human Genome Project is to guide therapeutic decision making with genetic data to improve the safety and efficacy of patient care, a promise that is fueled by extraordinary advances in the discovery of genomic variation that predicts drug response. The FDA recognizes many of these compelling associations by listing genetic variants that affect prescribing on at least 70 drug labels. However, translating this knowledge to clinical practice has met logistical and economic challenges. First, the feasibility and cost- effectiveness of large-scale pharmacogenomics testing for current health systems is unproven. Second, lack of evidence on the societal value of genetic panel tests in determining health care spending and patient outcomes has created economic barriers to the adoption of and reimbursement for more widespread pharmacogenomics testing. Existing research to quantify the value of genomic data has focused on the short-term cost-effectiveness of single drug-gene interactions (DGIs), an approach which underestimates the lifetime value of multi-gene assays. As we rapidly approach an era of inexpensive sequencing, new approaches to quantify and optimize the economic and clinical value of genome-tailored care are needed. For the Rational Integration of Genomic Healthcare Technology (RIGHT) project, we propose to develop a Discrete Event Simulation (DES) to estimate the average clinical efficacy and cost-effectiveness of prospective pharmacogenetic testing across a diverse patient population. The simulation will leverage literature-based estimates of clinical outcome rates, costs, and utilities with internal data describing the use of pharmacogenetic tests in routine patient care. The investigative team for this proposal has already launched one of the largest implementation programs for pharmacogenomic testing, with 10,000 patients enrolled and followed within the Vanderbilt Health System. Known as the Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment (PREDICT), the initial success provides a high-quality cohort of providers and patients to model and analyze real-world clinical decision making and patient outcomes associated with genotype-tailored care. Through PREDICT, our team has established procedures for routine measurement of drug metabolism genotypes and subsequent use of variant genotypes in clinical decision-making at the point of care. The RIGHT project will rigorously test hypotheses on the cost-effectiveness and factors that may affect cost-savings over time using three different strategies for pharmacogenomic implementation.
The Rational Integration of Genomic Healthcare Technology (RIGHT) project will evaluate the feasibility and cost-effectiveness of three different pharmacogenomic implementation strategies. Capitalizing on the initial success of one of the largest pharmacogenomic testing programs in place within the Vanderbilt Health System, this project will measure the cost-effectiveness of prospective genotyping and determine factors that support successful genotype-tailored prescribing.
|Empey, Philip E; Stevenson, James M; Tuteja, Sony et al. (2018) Multisite Investigation of Strategies for the Implementation of CYP2C19 Genotype-Guided Antiplatelet Therapy. Clin Pharmacol Ther 104:664-674|
|Batsis, John A; Pletcher, Sarah N; Stahl, James E (2017) Telemedicine and primary care obesity management in rural areas - innovative approach for older adults? BMC Geriatr 17:6|
|Wiley, Laura K; Vanhouten, Jacob P; Samuels, David C et al. (2017) STRATEGIES FOR EQUITABLE PHARMACOGENOMIC-GUIDED WARFARIN DOSING AMONG EUROPEAN AND AFRICAN AMERICAN INDIVIDUALS IN A CLINICAL POPULATION. Pac Symp Biocomput 22:545-556|
|Schildcrout, Jonathan S; Denny, Joshua C; Roden, Dan M (2017) On the Potential of Preemptive Genotyping Towards Preventing Medication-Related Adverse Events: Results from the South Korean National Health Insurance Database. Drug Saf 40:1-2|
|Peterson, J F; Field, J R; Shi, Y et al. (2016) Attitudes of clinicians following large-scale pharmacogenomics implementation. Pharmacogenomics J 16:393-8|
|Peterson, J F; Field, J R; Unertl, K M et al. (2016) Physician response to implementation of genotype-tailored antiplatelet therapy. Clin Pharmacol Ther 100:67-74|
|Schildcrout, Jonathan S; Shi, Yaping; Danciu, Ioana et al. (2016) A prognostic model based on readily available clinical data enriched a pre-emptive pharmacogenetic testing program. J Clin Epidemiol 72:107-15|
|Unertl, Kim M; Habiba, Jaffa; Field, Julie R et al. (2015) Clinician Perspectives on Using Pharmacogenomics in Clinical Practice. Per Med 12:339-347|