Asymptomatic Colonisers Drive the Spread of Drug-resistant Infections in Hospitals
The computer model improves on traditional methods like contact tracing by inferring asymptomatic carriers in the spread of antibiotic-resistant infections
A new analytical tool can improve a hospital’s ability to limit the spread of antibiotic-resistant infections over traditional methods like contact tracing, according to a new study led by researchers at Columbia University Mailman School of Public Health and published in the peer-reviewed journal Nature Communications. The method infers the presence of asymptomatic carriers of drug-resistant pathogens in the hospital setting, which are otherwise invisible.
Antimicrobial resistance (AMR) is an urgent threat to human health. In 2019, 5 million deaths were associated with an AMR infection globally.
The inference framework developed by Columbia Mailman School researchers is the first to combine several data sources – patient mobility data, clinical culture tests, electronic health records, and whole-genome sequence data – to predict the spread of an AMR infection in the hospital setting. In the study, the researchers used five years of real-world data from a New York City hospital. They focused on carbapenem-resistant Klebsiella pneumoniae (CRKP), an AMR bacterium with a high mortality rate. The framework draws on the four data sources to model the spread of CRKP infections, from individual to individual over time.
Levels of CRKP colonisation in healthcare facilities vary by location but can reach up to 22 percent of patients. However, hospitals do not routinely screen for CRKP, and surveillance relies on testing patients who are either symptomatic or suspected of coming into contact with symptomatic patients, overlooking asymptomatic colonisers.
“Many antimicrobial-resistant organisms colonise people without causing disease for long periods of time, during which these agents can spread unnoticed to other patients, healthcare workers, and even the general community,” says the study’s first author, Sen Pei, PhD, assistant professor of environmental health sciences at Columbia Mailman School. “Our inference framework better accounts for these hidden carriers.”
The researchers used the inference framework to estimate CRKP infection probabilities despite limited data on infections. They found that combining the four data sources led to more accurate carrier identification. Furthermore, using data simulations, they found that the framework was more successful at preventing the spread of infections after isolating carriers than traditional approaches based on an individual’s time in the hospital, the number of people they came in contact with, and/or whether the people they came in contact with were identified as having infections.
Using the inference model, isolating 1% of patients on the first day of each week (10–13 patients per week) reduces 16% of positive cases and 15% of colonisation; isolating 5% of patients on the first day of each week (50–65 patients per week) reduces 28% of positive cases and 23% of colonisation. For comparison, using contact tracing – a typical approach in clinical settings (ie, screening close contacts of positive patients) – isolating 1% of patients reduces 10% of positive cases and 8% of colonisation; isolating 5 percent of patients reduces 20% of positive cases and 16% of colonisation.
The new study builds on a study in PNAS that introduced a method that more accurately predicts the likelihood that individuals in hospital settings are colonised with methicillin-resistant Staphylococcus aureus (MRSA) than existing approaches. The new study is a significant advance over the previous study because it now includes patient-level electronic health records and whole-genome sequence data, which allows more precise identification of silent spreaders. While the inference model improves on traditional methods, it remains challenging to eliminate AMR pathogens in hospitals due to their widespread community circulation, limited hospital surveillance, and high false-negative rates in clinical culture tests. However, there is room for improvement; a future study aims to look at the spread of AMR using ultra-dense sequencing.
