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.

项目总结