Computer Model May Offer Way to Limit the Spread of MRSA in Hospitals
A new study in the journal Proceedings of the National Academy of Sciences (PNAS) introduces a method that more accurately predicts the likelihood individuals in hospital settings are colonized with MRSA than existing approaches. The new method works by identifying asymptomatic carriers, who are responsible for most of the spread of MRSA in some hospital settings and present a growing problem around the world as antibiotic-resistant staph infections gain ground.
A team of researchers led by Sen Pei, PhD, assistant professor of environmental health sciences at Columbia University Mailman School of Public Health, developed a computer model that uses de-identified electronic healthcare records and laboratory test results to simulate MRSA transmission and predict inpatients’ risk of infection. The model simulates the spread of MRSA through direct contacts, environmental contamination, and community importation.
The researchers tested their method using data from 66 hospitals and other inpatient facilities in Sweden. They found that their method more accurately identifies MRSA colonization than traditional approaches informed by hospitalization history and contract tracing. In addition, targeted interventions, such as the isolation of individuals who the model inferred were high-probability MRSA carriers, were found to better control the spread of infections in model simulations.
Currently, insufficient testing limits hospitals’ ability to track MRSA transmission. Community importation and multiple modes of transmission further complicate the detection of individual carriers.
“Antimicrobial-resistant organisms like MRSA can colonize people without symptoms for long periods of time, significantly contributing to the unchecked spread of infections,” says Pei. “By accounting for the complexities of the disease transmission process, including the asymptomatic spread of infections, our method provides a more accurate, and likely more cost-effective estimation of MRSA risks.”
Future Research and Implementation
Pei says the new method is cost-effective and could be potentially operationalized for use in clinical settings, but would first require further research in those settings. Possible improvements to the method might include the addition of individual risk factors like medical procedures and antibiotic use. The method also can be adapted for use beyond MRSA. “Looking ahead, this framework can be used not just for MRSA but can be adapted for use with other antimicrobial-resistant organisms and infectious diseases,” he says.
MRSA, or methicillin-resistant Staphylococcus aureus, is a type of staph bacteria that has become resistant to the antibiotics commonly used to treat staph infections. Although it can be found anywhere, MRSA is commonly found in healthcare settings, such as hospitals where it can cause severe problems such as bloodstream infections, pneumonia, or surgical site infections. Each year, more than a million Americans acquire MRSA infections while being treated in a hospital. At any given time, about one in every 20 inpatients carry MRSA in healthcare systems. Absent better measures to prevent them, MRSA infections are expected to become more common as antibiotic resistance grows around the world.
The study’s senior author is Jeffrey Shaman, professor of environmental health sciences. Fredrik Liljeros of Stockholm University is a co-author.
This study was supported by the National Institutes of Health (GM110748) and U.S. Centers for Disease Control and Prevention (CK000592), as well as a gift from the Morris-Singer Foundation. Shaman and Columbia University disclose partial ownership of SK Analytics. Shaman discloses consulting for BNI.