Precision Medicine and Treatment (PreEMPT)

摘要 技术的进步使广泛的基因检测成为可能。 健康或高危新生儿的条件。预计花在基因上的资金 到2021年，在美国的测试将达到250亿美元。随着基因组的众多用途 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。正在进行 新生命时期的基因组测序具有令人信服的逻辑，因为它可能提供 对婴儿患有活动性疾病的洞察，或对儿童时期或未来疾病的早期预警 成人期。虽然为所有新生儿提供基因组测序和解释可能是 目前，基因组技术和信息学的快速进步可能 让这一切变得可行。不管新生儿测序的成本是多少，目前尚不清楚的是 这种以人群为基础的测试可能有多大的益处和价值。 一项随机临床试验，研究并及时估计对终生健康的影响和 以人口为基础的新生儿基因组测序的成本是不可行的，因为样本大小和 需要时间范围。因此，在这项拟议的研究中，我们的目标是开发一种详细的数学 模拟自然病史、临床结果和整合成本效益的模型 各种基因组测序策略在美国的临床护理中的应用。该模型将提供 基因组学科学发展与使用的政策影响之间的重要联系 这些信息，在临床和经济方面都是如此。我们将创建一种灵活的模式， 允许在基因组医学进化过程中使用最新的证据进行更新。因此，作为新的 基因组技术和筛查试验的发展，我们可以快速评估他们的临床 实用价值和经济价值。这项研究将利用直接测序的经验 NIH资助的BabySeq项目，这是第一个此类随机对照试验，旨在检查 如何通过将基因组测序整合到儿科临床医学中来最好地使用基因组学 照顾健康和高危新生儿。 我们已经组建了一个跨学科的模拟建模专家团队， 经济学、基因组学、儿科学、预测建模和卫生系统研究。我们 建议将建模方法高度创新地应用于基因组技术，并将 开发新的分析框架，目标是综合现有的临床和 将流行病学数据整合为统一的建模工作。目标是计划临床和经济方面的 与评估基因组潜在价值的替代策略相关的结果 新生儿筛查技术。这项研究将为整合 将基因组信息纳入未来几十年的临床护理和卫生政策。

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.