Optimal Blood Screening Strategies for Infectious Agents: Mathematical Models and Decision Support Tools

传染源的最佳血液筛查策略：数学模型和决策支持工具

• 批准号: 1129688
• 负责人: Ebru Bish
• 金额: $ 34万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1129688&HistoricalAwards=false
• 关键词: Optimal; Blood; Screening; Strategies; Infectious

The research objective of this award is to develop mathematical models to determine an optimal screening strategy to eliminate pathogens from the blood supply. Blood products are essential for many medical treatments, and because many dangerous pathogens can be transmitted through blood products, blood screening is a vital medical service. For each pathogen, there are often multiple FDA-approved screening tests from which to choose, each having different characteristics (e.g., false-positive and false negative probabilities and cost). Thus, the set of screening tests, testing strategies (e.g., pooled versus individual testing), and decision rules (to classify blood as "safe" based on test outcomes) must be determined to minimize the risk of a transfusion-transmitted infection. This research will result in novel mathematical models and effective solution methodologies to determine an optimal screening strategy. Considerations include the amount of pathogen-free blood falsely discarded through screening, subpopulation specific and uncertain infection rates and test performance parameters, co-infection rates among pathogens, and the limited resources available. Other objectives focus on using this problem to help further the education of engineering students.If successful, the results of this research will contribute to the safety of healthcare delivery by building a better understanding of optimal screening strategies for donated blood, considering unconventional solutions, such as regional, blood group, or sub-population specific testing, which focus on equity in outcomes. As such, it has the potential to be transformative by shifting focus to unconventional testing schemes. This research is driven by the current needs of blood suppliers, and our collaboration with the American Red Cross, within a multi-disciplinary team of engineers and medical professionals, is an essential component of this project. Further, this research has application beyond blood screening (e.g., organ/tissue transplants, carcinogen testing, food testing). Graduate and undergraduate engineering students will benefit through classroom instruction and involvement in the research.

该奖项的研究目标是开发数学模型，以确定消除血液供应中病原体的最佳筛查策略。血液制品对于许多医疗治疗至关重要，并且由于许多危险的病原体可以通过血液制品传播，因此血液筛查是一项至关重要的医疗服务。对于每种病原体，通常有多种 FDA 批准的筛查测试可供选择，每种测试都有不同的特征（例如，假阳性和假阴性的概率和成本）。因此，必须确定一套筛查测试、测试策略（例如，汇总测试与个体测试）和决策规则（根据测试结果将血液分类为“安全”），以最大限度地降低输血传播感染的风险。这项研究将产生新颖的数学模型和有效的解决方法来确定最佳筛选策略。考虑因素包括通过筛查错误丢弃的无病原体血液量、亚群特异性和不确定的感染率和测试性能参数、病原体之间的共感染率以及可用资源有限。其他目标侧重于利用这个问题来帮助进一步教育工程专业的学生。如果成功，这项研究的结果将通过更好地理解献血的最佳筛查策略，考虑非常规的解决方案，例如注重结果公平性的区域、血型或亚人群特定测试，从而有助于医疗保健服务的安全。因此，它有可能通过将重点转移到非常规测试方案来实现变革。这项研究是由血液供应商当前的需求驱动的，我们与美国红十字会在工程师和医疗专业人员组成的多学科团队中的合作是该项目的重要组成部分。此外，这项研究的应用范围超出了血液筛查（例如器官/组织移植、致癌物检测、食品检测）。工程专业的研究生和本科生将通过课堂教学和参与研究受益。