Rational Integration of Genomic Healthcare Testing (RIGHT)

基因组医疗保健检测的合理整合（右）

• 批准号: 9059448
• 负责人: John A. Graves
• 金额: $ 52.11万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-25 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9059448
• 关键词: Address; Adoption; Affect; Algorithms; Behavior; Benchmarking; Biological Assay; Blood Platelets; Caring; Characteristics; Clinical; Computerized Medical Record; Cost Savings; Cost utility; Data; Decision Aid; Decision Making; Development; Disease; Dose; Drug Targeting; Economics; Enrollment; Environment; Event; Future; Genes; Genetic; Genetic screening method; Genome; Genomics; Genotype; Health; Health system; Healthcare; Human Genome Project; Insurance; Knowledge; Label; Literature; Measurement; Measures; Modeling; Monitor; Outcome; Outpatients; Patient Care; Patient-Focused Outcomes; Patients; Performance; Pharmaceutical Preparations; Pharmacogenomics; Physicians; Procedures; Program Effectiveness; Provider; Records; Research; Resources; Risk; Safety; Selection for Treatments; Simvastatin; Statistical Models; Technology; Testing; Therapeutic; Translating; Translations; Uncertainty; United States Food and Drug Administration; Variant; Vision; Warfarin; Work; base; behavioral outcome; clinical decision-making; clinical efficacy; clinical practice; clinically significant; cohort; cost effective; cost effectiveness; design; drug metabolism; economic value; gene interaction; genetic panel test; genetic variant; genomic data; genomic variation; improved; knowledge base; meetings; novel strategies; patient population; patient safety; pharmacogenetic testing; point of care; predictive modeling; prognostic; programs; prospective; prototype; response; routine care; simulation; success; thiopurine; time use; treatment program

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.

描述（由申请人提供）：人类基因组计划产生的一个广泛持有的愿景是用遗传数据指导治疗决策，以提高患者护理的安全性和有效性，这一承诺受到发现预测药物反应的基因组变异的非凡进展的推动。FDA通过在至少70种药物标签上列出影响处方的遗传变异来认识到许多这些令人信服的关联。然而，将这些知识转化为临床实践遇到了后勤和经济挑战。首先，目前卫生系统大规模药物基因组学测试的可行性和成本效益未经证实。其次，缺乏证据表明基因组检测在确定医疗保健支出和患者结局方面的社会价值，这为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药物-基因相互作用（DGI）的短期成本效益上，这种方法低估了多基因检测的生命周期价值。随着我们迅速接近一个廉价测序的时代，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。对于基因组医疗保健技术（RIGHT）项目的合理整合，我们建议开发离散事件模拟（DES），以估计不同患者人群中前瞻性药物遗传学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临床结局率、成本和效用的估计，以及描述药物遗传学检测在常规患者护理中使用的内部数据。该提案的调查小组已经启动了最大的药物基因组学测试实施计划之一，在范德比尔特卫生系统内招募并随访了10，000名患者。被称为增强护理和治疗决策的药物基因组学资源（PREDICT），最初的成功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型定制护理相关的现实世界临床决策和患者结局。通过PREDICT，我们的团队已经建立了药物代谢基因型的常规测量程序，并随后在护理点的临床决策中使用变异基因型。RIGHT项目将使用三种不同的药物基因组学实施策略，严格测试有关成本效益和可能影响成本节约的因素的假设。