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

项目摘要 尽管最近在减少医疗保健相关感染(HAI)方面取得了进展，但美国疾病控制和预防中心 疾病控制和预防中心估计，2015年美国急性护理医院发生了68.7万例甲型HAI 而且某一天的HAI患病率是每30名患者中就有一名。据估计，有72,000名患者死于 是在他们住院期间发生的。此外，医院的疫情仍然是一个严重的问题，但 大多数医院使用过时和无效的方法来检测它们。我们建立了增强的 结合细菌的医疗获得性传播检测系统(EDS-HAT)(R01AI127472) 全基因组测序(WGS)监视(与反应性WGS相对)，通过数据检测疫情 挖掘(DM)电子健康记录(EHR)和机器学习(ML)以识别责任人 传输路线。我们已经证明，EDS-HAT检测到了两种不同情况下的严重疫情 无法识别的和新的传输路线，因此现在实时运行该系统。携带的质粒数 细菌经常编码对抗菌剂产生抗药性的基因。当病人或医院 环境与两种细菌共存，这些环境为质粒化提供了机会 从携带质粒的物种转移到没有携带质粒的物种。与新获得的物种 然后，质粒可以被传播给另一名患者，这是一系列我们称之为传递的事件的组合 (T2T)。重要的是，传统的WGS分析或EDS-HAT不能捕获T2T事件。在此R21中 应用程序，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事件并确定将对这些事件的监测纳入 艾兹-帽子。在目标1中，我们计划开发和验证最佳的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 实时监测T2T事件的影响，并确定负责的传输路线。可操作的 T2T事件将报告给我们的感染预防团队，以便开发干预措施来中断 变速箱。这些目标将由一个在传染病流行病学方面具有专业知识的团队来实现， 暴发调查、感染预防和控制、微生物基因组学和基因组流行病学。如果 如果成功，这项研究将带来一种新的感染预防工具。拟议中的研究具有很高的 翻译，并将通过整合创新的基因组和计算来提高患者的安全性 用于识别其他未识别的T2T事件的方法。

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.