AI Combined With Genomic Surveillance Beats Humans To Detect Outbreaks Of Infectious Disease In Hospital Settings – COVID-19
Image: Dr Lee Harrison and Alex Sundermann load samples for genomic sequencing (Photo courtesy of Nathan Langer / UPMC)
By coupling machine learning with whole genome sequencing, scientists have dramatically improved the rapid detection of infectious disease outbreaks in hospitals compared to traditional methods of tracking outbreaks.
The process developed by scientists at the University of Pittsburgh School of Medicine (Pittsburgh, PA, USA) and Carnegie Mellon University (Pittsburgh, PA, USA) indicates a way for systems health care organizations to identify and then stop infectious disease outbreaks in hospitals in their tracks, reducing costs and saving lives. The Enhanced Detection System for Healthcare Associated Transmission (EDS-HAT) combines recent development in affordable genomic sequencing with computer algorithms connected to the vast mine of data contained in electronic health records. When sequencing detects that two or more patients in a hospital have nearly identical infection strains, machine learning quickly pulls those patients’ electronic health records for commonalities – whether near hospital beds. , a procedure using the same equipment or a shared healthcare provider – alerting infection control specialists to investigate and stop transmission.
Normally, this process requires clinicians to notice that two or more patients have a similar infection and alert their infection prevention team, who can then review patient charts to try to find how the infection was transmitted. From November 2016 to November 2018, the Presbyterian Hospital of the UPMC managed the EDS-HAT with a six-month lag for a few selected infectious pathogens often associated with nosocomial infections nationwide, while continuing the methods traditional real-time infection prevention. The team then studied the performance of the EDS-HAT. EDS-HAT detected 99 clusters of similar infections during this two-year period and identified at least one potential route of transmission in 65.7% of these clusters. During the same period, Infection Prevention used whole genome sequencing to help investigate 15 suspected outbreaks, two of which revealed genetically related infections. Had EDS-HAT operated in real time, the team estimates that up to 63 transmissions of an infectious disease from one patient to another could have been prevented. It would also have saved the hospital up to $ 692,500.
In one case study, EDS-HAT discovered an outbreak of vancomycin-resistant Enterococcus faecium which it attributed to an interventional radiology procedure involving the injection of a sterile contrast medium which was performed according to instructions. from the manufacturer. Due to detection of the outbreak by EDS-HAT, UPMC alerted the manufacturer to instructions that led to faulty sterilization practices. UPMC plans to introduce real-time EDS-HAT at UPMC Presbyterian Hospital and expects this innovation to benefit other infection prevention and control programs in the future. . And the original EDS-HAT, which primarily focused on drug-resistant bacterial pathogens, will soon be expanded to incorporate sequencing of respiratory viruses, including COVID-19.
“The current method used by hospitals to detect and stop the transmission of infectious diseases in patients is obsolete. These practices have not changed significantly in over a century, ”said senior author Lee Harrison, MD, professor of infectious diseases at Pitt’s School of Medicine and epidemiology at the Graduate School of Public Health. by Pitt. “Our process detects significant outbreaks that would otherwise go under the radar of traditional infection control surveillance. “
University of Pittsburgh School of Medicine
Carnegie Mellon University