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 未被识别的新颖的传播途径。尽管取得了这一成功，但仍需要进行更多研究来改进 基于 EDS-HAT 并进一步提高检测和中断医院疫情的能力。例如，医院 流感和 SARS-CoV-2 等呼吸道病毒的爆发已有充分记录，但这一领域 感染预防尚未得到充分研究。 EDS-HAT 中增加呼吸道病毒监测将改善 检测和预防这些代价高昂的 HAI。此外，可以随时获得临床微生物学数据 纳入 EDS-HAT 算法以减少对 WGS 监测的依赖。最后，WGS 监测 完全基于核心单核苷酸多态性 (SNP) 的分析可能会错误地对患者进行聚类。 因此，有必要研究辅助基因组的贡献，以改善 EDS-HAT 的歧视力。在此 R01 续订申请中，我们建议利用以下方面的成功 EDS-HAT 通过开发额外的创新方法来识别和中断医院相关的 传播。在目标 1 中，我们计划使用 WGS 监测和 EHR DM/ML 来研究医院传播 来自呼吸道病毒阳性的回顾性（目标 1a）和前瞻性收集（目标 1b）的呼吸道病毒 标本来自两家大型学术医院 (EDS-HAT RV)，一家用于成人，另一家用于儿童。在目标 2 中，我们 将开发先进的分析方法来创建主要依赖于 DM/ML 的 EDS-HAT 版本 EHR (EDS-HAT Lite)（目标 2a）并提高 WGS 的区分能力，以正确分类患者 是疫情爆发的一部分（目标 2b）。 EDS-HAT RV 和 EDS-HAT Lite 将受到临床和预算影响 进行分析以确定预防的病例数量和避免的医疗费用。这些目标将是 由在传染病流行病学、疫情调查、感染方面具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，并可以作为一个模型 了解如何在医院发现和阻止疫情爆发。