Enhanced Detection System for Healthcare-Associated Transmission of Infection

增强型医疗相关感染传播检测系统

• 批准号: 10297306
• 负责人: Lee H Harrison
• 金额: $ 77.76万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-26 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10297306
• 关键词: 2019-nCoV; Adult; Algorithms; Area; Centers for Disease Control and Prevention (U.S.); Childhood; Clinical; Clinical Data; Clinical Microbiology; Collection; Complex; Computing Methodologies; Data; Databases; Detection; Development; Disease Outbreaks; Economic Models; Economics; Electronic Health Record; Epidemiology; Evaluation; Genes; Genetic; Genome; Genomics; Goals; Health Care Costs; Healthcare; Healthcare Systems; Hospital Costs; Hospitalization; Hospitals; Incidence; Infection; Infection prevention; Infectious Disease Epidemiology; Influenza; Interruption; Investigation; Laboratories; Lead; Link; Machine Learning; Methods; Minisatellite Repeats; Mobile Genetic Elements; Modeling; Morbidity - disease rate; Patients; Policies; Prevalence; Prevention; Prevention program; Research; Residual state; Resources; Respiratory syncytial virus; Route; Sampling; Seasons; Single Nucleotide Polymorphism; Specimen; Synteny; Testing; Time; Validation; Viral; Viral Respiratory Tract Infection; Virus; acute care; adjudicate; advanced analytics; analytical method; analytical tool; bacterial genome sequencing; base; budget impact; data mining; detection platform; economic evaluation; evidence base; genome sequencing; genomic epidemiology; health care settings; healthcare-associated infections; improved; innovation; microbial genomics; mortality; neglect; novel; novel strategies; pathogen; patient safety; prevent; prospective; respiratory; respiratory infection virus; respiratory virus; sound; success; surveillance data; transmission process; viral transmission; wasting; 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. 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.

项目摘要 尽管最近在减少医疗保健相关感染（HAI）的发生率方面取得了进展 疾病控制和预防估计2015年在美国急诊医院发生了687,000 HAI 并且在特定日期的HAI患病率是30例患者中的1例。估计有72,000名患者死于 海斯在他们住院期间。此外，医院的爆发仍然是一个严重的问题 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 结合细菌的医疗保健收购传输检测系统（EDS帽）（R01AI127472） 整个基因组测序（WGS）监视（与反应性WGS相反）以检测数据爆发 电子健康记录（EHR）和机器学习（ML）的采矿（DM），以识别负责任 传输路线。我们已经证明，EDS帽检测到这两个严重的暴发 无法识别和新颖的传输路线。尽管取得了成功，但仍需要进一步的研究来改进 在EDS帽子并进一步增加了检测和中断医院暴发的能力。例如，医院 呼吸道病毒（如流感和SARS-COV-2）的爆发已得到充分记录，但是 预防感染被研究了。在EDS帽上添加呼吸病毒监测可以改善 检测和预防这些昂贵的HAI。此外，可以轻松获得的临床微生物学数据可能是 纳入EDS帽算法，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性（SNP）的分析可以错误地聚类患者。 因此，必须进行研究以改善附件基因组的贡献。 EDS帽的歧视力。在此R01续订应用中，我们建议利用 通过开发其他创新方法来识别和中断医院相关 传播。在AIM 1中，我们计划使用WGS监视和EHR DM/ML来研究医院的传播 回顾性呼吸道病毒（AIM 1A）和前瞻性收集（AIM 1B）呼吸病毒阳性 两家大型学术医院（EDS HAT RV）的标本，一个用于成人，另一个用于儿科。在AIM 2中，我们 将开发高级分析方法来创建一个EDS帽的版本，该版本主要依赖于DM/ML EHR（EDS Hat Lite）（AIM 2A），并提高WG的歧视能力，以正确对患者进行分类 是爆发的一部分（AIM 2B）。 EDS HAT RV和EDS HAT LITE将受到临床和预算影响 分析以确定预防案件的数量和避免医疗费用。这些目标将是 由具有传染病专业知识的团队在流行病学，爆发调查，感染方面完成 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。我们拟议的研究将改善患者的安全，并可以作为模型 在医院中如何检测和中断爆发。