Rational Integration of Genomic Healthcare Testing (RIGHT)

描述(由申请人提供)：由人类基因组计划产生的一个广泛持有的愿景是用基因数据指导治疗决策，以提高患者护理的安全性和有效性，这一承诺得到了在发现预测药物反应的基因组变异方面的非凡进展的推动。FDA通过在至少70个药品标签上列出影响处方的基因变异，认识到其中许多令人信服的关联。然而，将这些知识转化为临床实践遇到了后勤和经济方面的挑战。首先，对当前卫生系统进行大规模药物基因组学测试的可行性和成本效益尚未得到证实。其次，缺乏证据表明基因小组检测在决定医疗支出和患者结果方面的社会价值，这给更广泛的药物基因组学检测的采用和报销造成了经济障碍。现有的量化基因组数据价值的研究主要集中在单一药物-基因相互作用(DGIs)的短期成本效益上，这是一种低估了多基因分析的终身价值的方法。随着我们迅速接近廉价测序的时代，需要新的方法来量化和优化基因组定制护理的经济和临床价值。对于合理集成基因组医疗保健技术(Right)项目，我们建议开发离散事件模拟(DES)来评估不同患者群体中前瞻性药物遗传测试的平均临床疗效和成本效益。该模拟将利用基于文献的对临床结果率、成本和效用的估计，以及描述在常规患者护理中使用药物遗传学测试的内部数据。这项提案的调查小组已经启动了最大的药物基因组测试实施计划之一，有1万名患者在Vanderbilt Health System内登记和跟踪。最初的成功被称为增强护理和治疗决策的药物基因组资源(FOREST)，它提供了一个高质量的提供者和患者队列，以模拟和分析与基因定制护理相关的现实世界临床决策和患者结果。通过预测，我们的团队已经建立了药物代谢基因类型的常规测量程序，并随后在护理点的临床决策中使用变异基因类型。Right项目将使用三种不同的药物基因组实施策略，严格测试关于成本效益和可能影响随着时间推移节省成本的因素的假设。

DESCRIPTION (provided by applicant): 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.