Enhanced Detection System for Healthcare-Associated Transmission of Infection

摘要 尽管最近在减少医疗相关感染（HAI）的发生率方面取得了进展，但 疾病控制和预防中心估计，2011年美国急性护理医院发生了722，000例HAI， 造成七万五千人死亡目前在医院检测疾病暴发的方法是初级的， 完全错过了一些爆发，并导致对其他爆发的检测出现重大延误。 有两大 医疗保健的发展，有可能彻底改变医疗保健相关的疾病爆发， 在医院中识别和控制细菌病原体。一是 《平价医疗法案》要求使用 电子病历（EMR），这导致了其 广泛用于医疗保健。 第二，成本 细菌全基因组测序（WGS）已经大幅下降，这使得它的使用感染 方案越来越可行。在本申请中，我们建议建立和评估 医疗保健协会传播的增强检测系统（EDS-HAT） 匹兹堡医疗中心（UPMC）EDS-HAT使用WGS和EMR分析的组合， 加强爆发检测。我们的具体目标是1a）：确定EDS-HAT识别HAT的效用 通过常规感染预防实践未发现的感染，1b）：提高效率，减少感染， 通过使用EMR限制WGS的使用，2a）：测量HAI的减少， 2b）：估计预防的感染和死亡人数， 成本由EDS-HAT避免。对于目标1a，将在常规感染时回顾性进行EDS-HAT 预防实践（当怀疑爆发时要求进行分子分型）继续进行，从而允许 两种方法的直接比较。对于目标1b，我们将提高效率，降低成本， EDS-HAT通过使用EMR的机器学习和数据挖掘来选择用于WGS的分离株。对于目标2a，我们 将真实的实时监测实施EDS-HAT前后HAI率的变化， 发生在第三年年初。最后，对于目标2b，我们将进行临床和预算影响分析， 确定EDS-HAT的总体影响。为了实现这些目标，我们组建了一个团队， 传染病、暴发调查、感染预防、微生物基因组学和基因组学方面的专业知识 流行病学，机器学习和数据挖掘，经济分析和建模，流行病学， 生物统计学EDS-HAT可能会大幅减少感染、死亡和医疗费用， 可以作为医院如何检测HAT的模型。

ABSTRACT Despite recent progress in reducing the incidence of healthcare-associated infections (HAIs), the Centers for Disease Control and Prevention estimated that 722,000 HAIs occurred in U.S. acute care hospitals in 2011, resulting in 75,000 deaths. Current methods for detecting outbreaks in hospitals are rudimentary and likely to miss some outbreaks altogether and result in substantial delays in detection of others. There are two major developments in healthcare that have the potential to revolutionize how healthcare associated outbreaks of bacterial pathogens are identified and controlled in hospitals. First, the Affordable Care Act mandates use of the electronic medical record (EMR), which has led to its widespread use in healthcare. Second, the costs of bacterial whole genome sequencing (WGS) have declined substantially, which is making its use by infection programs increasingly feasible. In this application, we propose to establish and evaluate the impact of the Enhanced Detection System for Healthcare Association Transmission (EDS-HAT) at the University of Pittsburgh Medical Center (UPMC). EDS-HAT uses a combination of WGS and analysis of the EMR for enhanced outbreak detection. Our specific aims are to 1a): Determine the utility of EDS-HAT to identify HAT that is not identified through routine infection prevention practice, 1b): Improve the efficiency and reduce the cost of EDS-HAT by using the EMR to restrict the use of WGS, 2a): Measure reductions in HAIs following implementation of EDS-HAT, and 2b): Estimate the number of infections and deaths prevented and healthcare costs averted by EDS-HAT. For Aim 1a, EDS-HAT will be performed retrospectively while routine infection prevention practice (requests for molecular typing when an outbreak is suspected) continues, thus allowing a direct comparison of the two approaches. For Aim 1b, we will improve the efficiency and reduce the cost of EDS-HAT by using machine learning and data mining of the EMR to select isolates for WGS. For Aim 2a, we will monitor changes in HAI rates both before and after implementation of EDS-HAT in real time, which will occur at the beginning of year 3. Finally, for Aim 2b, we will perform clinical and budget impact analyses to determine the overall impact of EDS-HAT. To accomplish these aims, we have assembled a team with expertise in infectious diseases, outbreak investigation, infection prevention, microbial genomics and genomic epidemiology, machine learning and data mining, economic analysis and modeling, epidemiology, and biostatistics. EDS-HAT will likely lead to substantial reductions in infections, deaths, and healthcare costs and can serve as a model for how HAT is detected in hospitals.