Precision Medicine and Treatment (PreEMPT)

精准医学与治疗 (PreEMPT)

• 批准号: 9381957
• 负责人: Ann Chen Wu
• 金额: $ 59.89万
• 依托单位: HARVARD PILGRIM HEALTH CARE, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-11 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9381957
• 关键词: Address; Admission activity; Adoption; Adult; Affect; Area; Benefits and Risks; Biological Models; Birth; Caring; Childhood; Clinical; Clinical Data; Clinical Trials; Complex; Computer Simulation; Computers; Cost Measures; Cost of Illness; Costs and Benefits; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Economics; Epidemiology; Funding; Future; Genetic; Genetic Models; Genetic screening method; Genome; Genomic medicine; Genomics; Goals; Health; Health Benefit; Health Policy; Health system; Hereditary Disease; Heritability; Hypertrophic Cardiomyopathy; Informatics; Intervention; Knowledge; Lead; Life; Link; Logic; Long-Term Effects; Measurement; Medicine; Mendelian disorder; Methods; Modeling; Morbidity - disease rate; Natural History; Neonatal Intensive Care Units; Neonatal Screening; Newborn Infant; Outcome; Patient Care; Pediatrics; Policies; Precision therapeutics; Public Health; Quality of life; Randomized Clinical Trials; Randomized Controlled Trials; Research; Risk; SCID Mice; Sample Size; Scientific Advances and Accomplishments; Severe Combined Immunodeficiency; Syndrome; Technology; Testing; Time; Translations; Treatment Cost; United States; United States National Institutes of Health; Update; base; clinical care; clinically actionable; congenital deafness; cost; cost effective; cost effectiveness; economic outcome; economic value; epidemiologic data; exome; experience; flexibility; genetic variant; genome sequencing; health economics; high risk; improved; innovation; insight; mathematical model; models and simulation; mortality; novel; policy implication; population based; precision medicine; predictive modeling; prevent; routine care; screening; simulation; standard of care; systems research; tool; trial design; whole genome

ABSTRACT Advances in technology have led to the availability of genetic testing for a wide range of conditions for healthy or high-risk newborns. It is expected that the funds spent on genetic testing in the U.S. will reach $25 billion by 2021. With the numerous uses of genomic information, understanding the clinical value and long-term impact of genomic technologies on morbidity, mortality, quality of life, and diagnosis and treatment costs is essential. Conducting genomic sequencing in the newborn period of life has compelling logic, as it may provide insights for an active illness that a baby has, or early warning for future illnesses in childhood or adulthood. While providing genomic sequencing and interpretation for all newborns may be unrealistic at the present time, rapid advances in genomic technologies and informatics may make this feasible. Regardless of the cost of sequencing newborns, what is as yet unclear is how beneficial and valuable such population-based testing might be. A randomized clinical trial to study and provide timely estimates of the lifetime health impact and cost of population-based newborn genomic sequencing is infeasible given the sample size and time horizon needed. Thus, in this proposed study, we aim to develop a detailed mathematical model to simulate the natural history, clinical outcomes, and cost-effectiveness of integrating various genomic sequencing strategies into clinical care in the U.S. The model will provide an important link between scientific developments in genomics and the policy implications of using this information, both in clinical and economic terms. We will create a flexible model that will allow updating with the most current evidence in genomic medicine as it evolves. Thus, as new genomic technologies and screening tests are developed, we can quickly assess their clinical utility and economic value. This study will leverage the direct sequencing experiences of the NIH-funded BabySeq Project, a first-of-its-kind randomized controlled trial designed to examine how best to use genomics in clinical pediatric medicine by integrating genomic sequencing into the care of healthy and high-risk newborns. We have assembled an interdisciplinary team of experts in simulation modeling, health economics, genomics, pediatrics, predictive modeling, and health systems research. We propose a highly innovative application of modeling methods to genomic technologies and will develop a novel analytic framework, with the goal of synthesizing available clinical and epidemiological data into a unified modeling effort. The goal is to project clinical and economic outcomes associated with alternative strategies to assess the potential value of genomic technologies for newborn screening. This study will provide a durable platform for integration of genomic information into clinical care and health policy over the next decades.

摘要 技术的进步使得基因检测可以广泛地应用于 健康或高危新生儿的条件。预计用于遗传学的资金 到2021年，美国的测试费用将达到250亿美元。随着基因组的大量使用， 信息，了解基因组技术的临床价值和长期影响， 发病率、死亡率、生活质量以及诊断和治疗费用至关重要。进行 新生儿时期的基因组测序具有令人信服的逻辑，因为它可能提供 对婴儿患有的活动性疾病的见解，或对儿童期未来疾病的早期预警， 成年虽然为所有新生儿提供基因组测序和解释可能是可行的， 目前，基因组技术和信息学的快速发展可能是不现实的， 使之可行。先不考虑新生儿测序的成本，目前还不清楚的是， 这种基于人群的测试有多大的益处和价值。 一项随机临床试验，研究并及时估计终身健康影响， 鉴于样本量，基于人群的新生儿基因组测序的成本是不可行的， 需要的时间范围。因此，在这项拟议的研究中，我们的目标是制定一个详细的数学 模型，以模拟自然史，临床结果和成本效益的整合 该模型将提供一个新的基因组测序策略， 基因组学的科学发展与使用基因组学的政策影响之间的重要联系 这些信息，无论是在临床上还是在经济上。我们将创建一个灵活的模型， 允许随着基因组医学的发展更新最新的证据。作为新 基因组技术和筛选测试的发展，我们可以快速评估他们的临床 实用性和经济价值。这项研究将利用直接测序的经验， NIH资助的BabySeq项目是一项首次随机对照试验，旨在检查 如何通过将基因组测序整合到临床儿科医学中， 健康和高危新生儿的护理。 我们组建了一个跨学科的专家团队，在仿真建模，健康， 经济学、基因组学、儿科学、预测建模和卫生系统研究。我们 提出了一个高度创新的应用建模方法的基因组技术，并将 开发一个新的分析框架，目标是综合现有的临床和 将流行病学数据整合到统一的建模工作中。目标是项目临床和经济 与评估基因组潜在价值的替代战略相关的结果 新生儿筛查技术。这项研究将为整合以下方面提供一个持久的平台： 基因组信息在未来几十年的临床护理和卫生政策。