Researchers at the University of Pittsburgh have implemented real-time whole-genome sequencing (WGS) to help track and limit healthcare-associated infections (HAIs) in university hospitals. learn.
Presenting at this year’s American Society for Microbiology Conference on Rapidly Applied Next Generation Microbial Sequencing and the Bioinformatics Pipeline (ASM NGS) on Monday, Alexander Sundermann, an infectious disease professor at the University of Pittsburgh, has presented preliminary data demonstrating the feasibility of real-time WGS surveillance and its potential utility in preventing hospital outbreaks. .
Healthcare-associated infections are “very common, costly and fatal,” says Sundermann, adding that in the inpatient setting, up to 10% of hospital patients US hospitals will develop HAIs.
The traditional approach to identifying hospital-related outbreaks can be somewhat daunting, often involving chart review, staff interviews, and a host of other procedures. Then, hospitals routinely deploy “a sink intervention” to prevent outbreaks, Sundermann said, and WGS may not be used until the end to help confirm or refute the outbreak. TWO suspicions and its transmission – an approach the researchers call “the WGS response.”
Meanwhile, genomic surveillance, which involves routine sequencing of pathogen isolates regardless of the presence of an outbreak, has become an emerging approach for infection prevention, he said. note.
Previously, Sundermann and his colleagues developed a WGS and machine learning-based method to identify and prevent HAIs called Advanced Detection System for Healthcare-Related Transmission. (EDS-HAT).
As a proof of concept, the researchers applied EDS-HAT to a retrospective genome surveillance study and sequenced patient samples from suspected healthcare-associated infections. over a two-year period. They assembled sample isolates using a threshold of 15 SNPs for all target pathogens except Clostridioides difficileanalyzed with a threshold of two SNPs.
Results from the pilot showed that out of more than 2,750 unique patient isolates, 297 were genetically related with approximately 100 clusters. Meanwhile, the traditional infection prevention approach only requires sequencing on 15 outbreaks involving about 130 patients, Sundermann said. Only five of these sequenced isolates were genetically related, suggesting that conventional infection control programs missed and misidentified transmission routes.
From a cost-effective point of view, Sundermann said, using Pseudomonas aeruginosa as an example, the cost of a hospital to treat a healthcare-related infection is close to $25,000, while the cost of sequencing an isolate is about $80. “If you stop a Pseudomonas aeruginosa infections, you actually save a lot of money in the long run,” he said.
Based on retrospective research, Sundermann’s team sought to test the utility of the EDS-HAT for real-time WGS monitoring. The team applied this method to culture positive pathogen samples from patients with suspected HAIs at the university hospital. After performing WGS on isolates using the Illumina NextSeq platform, the researchers analyzed the sequencing data using a custom-developed bioinformatics pipeline to determine genetic associations. between the positive sample and the isolates in the existing HAI database.
Once a probable TWO is identified, a preliminary report will be shared with the hospital infection control staff for follow-up investigation and the initiation of any interventions deemed necessary.
Since last November, researchers have sequenced samples from more than 1,500 patients. Of those, 182, or about 11%, clustered into 58 different outbreaks. More than half of the cases in these clusters have epidemiological evidence of nosocomial transmission, Sundermann said. The results also indicated a mean cluster size of three patients, with a median of two. “We want to keep these clusters small, because that means the pathogen might not be spreading anymore,” Sundermann notes.
To date, several TWO identified in research have involved endoscopes, Sundermann said, with one outbreak involving four patients and two different pathogens pointing to one endoscope. In another notable outbreak, a small number of patients from a third-party chronic care facility located in a hospital were affected.
To make the EDS-HAT an effective tool for real-time WGS monitoring, fast turnaround times are “critically important,” emphasized Sundermann. The current average turnaround for the workflow – from required in-hospital cultures to actionable planning based on sequence data – is 16 days, with an average of about two weeks. “We wanted to shorten the time as much as possible,” he said. “Because within those two weeks, there’s transmission happening in the hospital without intervention.”
In addition, Sundermann said a good outcome measure for the effectiveness of the new approach is to investigate how many downstream infections still occur even after WGS surveillance-informed interventions have been implemented. taken to limit outbreaks. So far, a six-month interim analysis of the study found no additional infections after the interventions were implemented, indicating success. However, more analysis at the study’s one-year mark, i.e. next month, will show whether these preliminary results hold, he noted.
In addition, Sundermann said, the team also plans to examine the hospital’s overall HAI ratio after implementing the program, compared with peer hospitals with similar patient capacity but not yet using the program. genome surveillance to prevent infection.
“The TWO ratio is something the hospital will be interested in,” he said. “If the TWO rate drops from this program, our steps are more likely to be widely adopted.”