Researchers at the University of Pittsburgh School of Medicine and Carnegie Mellon University have turned to machine learning and whole-genome sequencing in an effort to improve the detection of infectious disease outbreaks within hospital settings.
In a report published in the journal Clinical Infectious Diseases, the team point to a new way for health systems to identify and then stop hospital-based infectious disease outbreaks in their tracks, cutting costs and saving lives.
“The current method used by hospitals to find and stop infectious disease transmission among patients is antiquated. These practices haven’t changed significantly in over a century,” Lee Harrison, MD, senior author and professor of infectious diseases at Pitt’s School of Medicine and epidemiology at Pitt’s Graduate School of Public Health, said in a statement. “Our process detects important outbreaks that would otherwise fly under the radar of traditional infection prevention monitoring.”
The Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT) combines genomic sequencing and machine learning connected to EHR data. When the sequencing identifies two or more patients in a hospital with nearly identical strains of infection, machine learning quickly mines those patients’ EHRs for commonalities.
Typically, this process requires clinicians to notice that two or more patients shave a similar infection and alert their infection prevention team.
“This is an incredibly labor-intensive process that is often dependent upon busy health care workers noticing a shared infection between patients to begin with,” said lead author Alexander Sundermann, MPH, CIC, FAPIC, a clinical research coordinator and doctoral candidate at Pitt Public Health. “That might work if patients are in the same unit of a hospital, but if those patients are in different units with different health care teams and the only shared link was a visit to a procedure room, the chances of that outbreak being detected before other patients are infected falls dramatically.”
According to the report, from November 2016 to November 2018, UPMC Presbyterian Hospital ran EDS-HAT with a six-month lag for a few select infectious pathogens often linked to healthcare-acquired infections nationwide while also maintaining real-time, traditional infection prevention methods. The team then analyzed how will EDS-HAT performed.
The report said EDS-HAT detected 99 clusters of similar infections in the two years and found at least one potential transmission route in 65.7 percent of the clusters. At the same time, infection prevention used whole-genome sequencing to assist in the investigation of 15 suspected outbreaks.
If EDS-HAT was running in real-time, researchers estimated that as many as 63 transmissions of infectious disease from one patient to another could have been prevented. Additionally, the technology could have saved the hospital as much as $692,000.
Researchers plan to introduce EDS-HAT in real-time at UMPC Presbyterian Hospital to improve future infection prevention and control programs. According to researchers, the original EDS-HAT will soon expand to include sequencing for respiratory viruses, including COVID-19.
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