Tracking plasmid spread and transmission in the hospital: A novel tool for infection prevention and control

追踪医院内的质粒传播和传播：感染预防和控制的新工具

• 批准号: 10721660
• 负责人: Lee H Harrison
• 金额: $ 23.36万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10721660
• 关键词: Algorithms; Antimicrobial Resistance; Bacteria; Bioinformatics; Centers for Disease Control and Prevention (U.S.); Discipline of Nursing; Disease Outbreaks; Electronic Health Record; Endoscopes; Environment; Epidemiologic Methods; Event; Genes; Genomics; Goals; Healthcare Systems; Hospitalization; Hospitals; Hybrids; Incidence; Infection Control; Infection prevention; Infectious Disease Epidemiology; Infrastructure; Interruption; Intervention; Investigation; Laboratories; Machine Learning; Medical Device; Methods; Mining; Monitor; Patient-Focused Outcomes; Patients; Plasmids; Prevalence; Procedures; Reporting; Research; Route; Running; Sampling; Services; Site; System; Time; acute care; antimicrobial drug; bacterial genome sequencing; bioinformatics pipeline; cost effective; data mining; detection method; detection platform; experience; genome sequencing; genomic epidemiology; healthcare-associated infections; hospital care; improved; innovation; machine learning algorithm; microbial genomics; novel; pathogen; patient safety; prevent; real time monitoring; sequencing platform; success; tool; transmission process; whole genome

Project Summary Despite recent progress in reducing the incidence of healthcare-associated infections (HAIs), the Centers for Disease Control and Prevention estimated that 687,000 HAIs occurred in U.S. acute care hospitals in 2015 and that the HAI prevalence on a given day was one in 30 patients. An estimated 72,000 patients died with HAIs during their hospitalization. In addition, outbreaks in hospitals remain a serious problem but the vast majority of hospitals use antiquated and ineffective methods to detect them. We established the Enhanced Detection System for Healthcare Acquired Transmission (EDS-HAT) (R01AI127472), which combines bacterial whole genome sequencing (WGS) surveillance (as opposed to reactive WGS) to detect outbreaks with data mining (DM) of the electronic health record (EHR) and machine learning (ML) to identify the responsible transmission routes. We have demonstrated that EDS-HAT detects both serious outbreaks that were otherwise unrecognized and novel transmission routes and therefore now run the system in real time. Plasmids carried by bacteria frequently encode genes that confer resistance to antimicrobial agents. When patients or hospital environments are co-colonized with two bacterial species, these settings provide the opportunity for plasmid transfer to occur from a species carrying a plasmid to one that does not. The species with the newly acquired plasmid can then be transmitted to another patient, a combination of events we call transfer to transmission (T2T). Importantly, T2T events are not captured by traditional WGS analysis or EDS-HAT. In this R21 application, we propose to leverage the success and infrastructure of EDS-HAT by developing methods for detection of T2T events and determining the potential utility of incorporating surveillance for these events into EDS-HAT. In Aim 1, we plan to develop and validate optimal laboratory and bioinformatics approaches for real-time identification of T2T events in the hospital. In Aim 2, we will determine the feasibility and potential impact of real-time monitoring for T2T events and determining the responsible transmission routes. Actionable T2T events will be reported to our infection prevention team so that interventions can be developed to interrupt transmission. These aims will be accomplished by a team with expertise in infectious diseases epidemiology, outbreak investigation, infection prevention and control, microbial genomics and genomic epidemiology. If successful, this research will lead to a novel infection prevention tool. The proposed research is highly translational and will improve patient safety through incorporation of innovative genomic and computational approaches for identifying otherwise unrecognized T2T events.

项目概要 尽管最近在降低医疗保健相关感染 (HAI) 发生率方面取得了进展，但中心 美国疾病控制与预防中心估计，2015 年美国急症护理医院发生了 687,000 例 HAI 并且某一天的 HAI 患病率为 30 名患者中就有 1 人。估计有 72,000 名患者死于 住院期间发生 HAI。此外，医院内的疫情暴发仍然是一个严重的问题，但 大多数医院使用陈旧且无效的方法来检测它们。我们建立了增强型 医疗保健获得性传播检测系统 (EDS-HAT) (R01AI127472)，结合了细菌 全基因组测序 (WGS) 监测（与反应性 WGS 相对）通过数据检测疫情 电子健康记录 (EHR) 的挖掘 (DM) 和机器学习 (ML) 来识别责任人 传播途径。我们已经证明 EDS-HAT 可以检测到两种严重的疫情 未被识别和新颖的传输路线，因此现在实时运行系统。携带的质粒 细菌经常编码赋予抗菌药物抗性的基因。当患者或医院 环境与两种细菌共存，这些环境为质粒提供了机会 从携带质粒的物种到不携带质粒的物种发生转移。新获得的物种 然后质粒可以被传播给另一个患者，我们称之为转移到传播的事件组合 （T2T）。重要的是，传统的 WGS 分析或 EDS-HAT 无法捕获 T2T 事件。在这个R21 应用程序中，我们建议通过开发方法来利用 EDS-HAT 的成功和基础设施 检测 T2T 事件并确定将这些事件的监视纳入其中的潜在效用 EDS-帽子。在目标 1 中，我们计划开发和验证最佳实验室和生物信息学方法 实时识别医院内的T2T事件。在目标 2 中，我们将确定可行性和潜力 实时监控 T2T 事件并确定负责的传播路线的影响。可行的 T2T 事件将报告给我们的感染预防团队，以便制定干预措施来中断 传播。这些目标将由具有​​传染病流行病学专业知识的团队来实现， 疫情调查、感染预防和控制、微生物基因组学和基因组流行病学。如果 成功后，这项研究将带来一种新型的感染预防工具。拟议的研究高度 转化并将通过结合创新的基因组和计算来提高患者的安全 识别其他无法识别的 T2T 事件的方法。