Enhanced Detection System for Healthcare-Associated Transmission of Infection

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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.