Enhanced Detection System for Healthcare-Associated Transmission of Infection

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 未被识别的和新的传播路线。尽管取得了这样的成功，但还需要进行更多的研究来改进 在EDS-HAT之后，并进一步提高检测和阻断医院疫情的能力。例如，医院 流感和SARS-CoV-2等呼吸道病毒的爆发有很好的记录，但这一领域 预防感染的研究还不够深入。在EDS-HAT中增加呼吸道病毒监测将会有所改善 检测和预防这些代价高昂的禽流感病毒。此外，随时可用的临床微生物学数据可以 整合到EDS-HAT算法中，以减少对WGS监视的依赖。最后，WGS监视 完全基于核心单核苷酸多态(SNPs)的分析可能会错误地将患者归类。 因此，研究辅助基因组的贡献(S)是必要的，以提高 EDS-HAT的歧视能力。在此R01续订申请中，我们建议利用 EDS-HAT通过开发其他创新方法来识别和中断与医院相关的 变速箱。在目标1中，我们计划使用WGS监测和EHR DM/ML来研究肺炎的医院传播 呼吸道病毒阳性的回顾(目标1a)和预期收集(目标1b)中的呼吸道病毒 两家大型学术医院(EDS-HAT RV)的标本，一家用于成人，另一家用于儿科。在目标2中，我们 将开发高级分析方法来创建EDS-HAT版本，该版本主要依赖 EHR(EDS-HAT Lite)(目标2a)，并提高WGS的区分力，以正确地对 是暴发的一部分(目标2b)。EDS-HAT房车和EDS-HAT Lite将受到临床和预算影响 分析以确定预防的病例数量和避免的医疗成本。这些目标将是 由一个在传染病流行病学、疫情调查、感染方面拥有专业知识的团队完成 预防、微生物基因组学和基因组流行病学、机器学习和数据挖掘以及经济 分析和建模。我们提出的研究将提高患者的安全性，并可作为模型 了解如何在医院检测和中断疫情。

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. Despite this success, additional research is needed to improve upon EDS-HAT and further increase capacity to detect and interrupt hospital outbreaks. For example, hospital outbreaks of respiratory viruses such as influenza and SARS-CoV-2 are well documented, but this area of infection prevention is understudied. The addition of respiratory virus surveillance to EDS-HAT would improve detection and prevention of these costly HAIs. In addition, readily-available clinical microbiology data can be incorporated into EDS-HAT algorithms to reduce reliance on WGS surveillance. Finally, WGS surveillance analysis based entirely on core single nucleotide polymorphisms (SNPs) can falsely cluster patients. Therefore, research to investigate the contribution(s) of the accessory genome is necessary to improve discriminatory power of EDS-HAT. In this R01 renewal application, we propose to leverage the success of EDS-HAT by developing additional innovative methods for identification and interruption of hospital-associated transmission. In aim 1, we plan to use WGS surveillance and EHR DM/ML to study hospital transmission of respiratory viruses from retrospective (aim 1a) and prospective collections (aim 1b) of respiratory virus positive specimens at two large academic hospitals (EDS-HAT RV), one for adults and the other pediatric. In aim 2, we will develop advanced analytic methods to create a version of EDS-HAT that relies primarily on DM/ML of the EHR (EDS-HAT Lite) (aim 2a) and improve the discriminatory power of WGS to correctly classify patients who are part of an outbreak (aim 2b). EDS-HAT RV and EDS-HAT Lite will undergo clinical and budget impact analyses to determine the number of cases prevented and healthcare costs averted. These aims will be accomplished by a team with expertise in infectious diseases epidemiology, outbreak investigation, infection prevention, microbial genomics and genomic epidemiology, machine learning and data mining, and economic analysis and modeling. Our proposed research will lead to improved patient safety and can serve as a model for how outbreaks are detected and interrupted in hospitals.