# Enhanced Detection System for Healthcare-Associated Transmission of Infection

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $777,646

## Abstract

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 a...

## Key facts

- **NIH application ID:** 10297306
- **Project number:** 2R01AI127472-06
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Lee H Harrison
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $777,646
- **Award type:** 2
- **Project period:** 2016-09-26 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10297306

## Citation

> US National Institutes of Health, RePORTER application 10297306, Enhanced Detection System for Healthcare-Associated Transmission of Infection (2R01AI127472-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10297306. Licensed CC0.

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