A new tool to prevent the spread of hospital-acquired infections in the era of COVID-19

This year, COVID-19 added an additional layer of risk to patients who entered the U.S. healthcare system. With the pandemic still surging nationwide, many people are avoiding hospitals, especially emergency rooms. The general public is now acutely aware of the risks associated with even brief exposure to individuals infected with the SARS-CoV-2 virus. In a recent study, researchers from the National Human Genome Research Institute (NHGRI), Oxford University and other National Institutes of Health centers developed and tested a new method to predict hospital-acquired infections involving five other important pathogens.

Monitoring hospital-acquired infections
Researchers used the amount of time a patient spent in an area with other patients who either had a confirmed case or were suspected of having an infection — called co-presence — as a measure to predict hospital-acquired infections. Credit: Harry Wedel, NHGRI.

“Our data reveal that we can, with high accuracy, detect healthcare-acquired infections in hospital patients if they spend more than 24 hours in the presence of another patient suspected of infection with one of the five pathogens we studied,” said Laura Koehly, Ph.D., chief of the social and behavioral research branch at NHGRI and co-author on the paper.

Our data reveal that we can, with high accuracy, detect healthcare-acquired infections in hospital patients if they spend more than 24 hours in the presence of another patient suspected of infection with one of the five pathogens we studied.

The study was an outcome of the doctoral work by Jeff Rewley, Ph.D., through the NIH Oxford Cambridge Scholars Program. Dr. Rewley was advised by co-authors Laura Koehly, Ph.D.; Christopher Marcum, Ph.D., a staff scientist at NHGRI; and Felix Reed-Tsochas, Ph.D., a professor at Oxford University.

Apart from laboratory tests, contact-tracing is perhaps the most well-known form of detecting acquired infections, but it comes with limitations. Although contact-tracing takes into account that being in the proximity of an infected person increases one's risk of infection, it does not consider the amount of time a person spends with an infected person and its association with the risk of infection.

"We wanted to ask the question of how people around you in a hospital can impact your health, especially when it comes to infections," Dr. Rewley said. "But is there a way we can accurately predict your risk for acquiring a hospital-acquired infection just by using routinely collected hospital data?"

Hospital staff usually test patients for infections when they start showing symptoms, but that could be too late to effectively contain an outbreak. The researchers wondered if, with the help of existing hospital data that detailed where each person moved around the hospital, they could build a tool that detected infections sooner — potentially before symptoms arose and the patient became highly infectious.

The researchers studied administrative data from 133,304 patients who stayed in National Health Service (NHS) hospitals in Oxfordshire, U.K. for at least 48 hours between 2011 and 2015. The NHS, which is a national system, provides readily available data and captures the entire population in a way that U.S. hospitals cannot.

Instead of using the traditional “any contact” vs. “none” tracing method, the researchers used the amount of time a patient spent in an area with other patients who either had a confirmed case or were suspected of having an infection — called co-presence — as a measure to predict hospital-acquired infections. Based on the data, the researchers created a monitoring system that informed hospital administrators if patients’ co-presence with others warranted a test for potential infection.

The researchers cross-referenced each patient with other patients who were suspected of infection and searched for overlaps in their time spent in close proximity. Using the data, the team could predict subsequent infection for bacterial and viral pathogens that commonly cause hospital-acquired infections: methicillin-resistant Staphylococcus aureus (MRSA), Escherichia coli, Pseudomonas aeruginosa, Clostridium difficile and norovirus. These pathogens are usually transmitted either through direct contact, contaminated surfaces or contaminated food and drinks.

"Viruses and bacteria have different transmission patterns, and they can live very differently on various surfaces," said Dr. Rewley. "So we wanted to ask whether our prediction system would work well for different pathogens."

For all five pathogens, the researchers found that patients were most likely to be infected if they spent more than 24 hours in the presence of a potentially infected person. The timing varied for each pathogen, ranging from 29 hours for Clostridium dificile to 59 hours for Esherichia coli.

According to the study results, if healthcare workers had tested patients based on the duration of exposure to known or suspected cases, new cases could be caught up to a day before they actually were detected. The researchers believe that implementing such an approach will help prevent the spread of hospital-acquired infections and vastly reduce the cost burden.

"If done prospectively, hospitals could test and quarantine infected patients earlier. This could be extremely valuable and help making smart and timely decisions with regard to these five serious infections," Dr. Koehly said.

Because several hospitals already use electronic medical records, the team is hopeful that patient records will be easily adapted to include co-presence monitoring. They also anticipate including more measures to increase the effectiveness of the digital tracing method.

By implementing such a tool, researchers believe that healthcare workers can reduce the adverse consequences of infection by preventing potential secondary infections resulting from the original infected patient. The team also hopes that the tool will eventually be applied to a number of other serious healthcare-acquired infections.