Researchers use email spam filter-like algorithm to identify C. diff risk

A study published in mBio, the online open-access journal of the American Society for Microbiology, may help create a test to predict which hospital patients are at the highest risk of developing a Clostridium difficile infection and improve infection management.

"Hospital-acquired C. diff infections have bloomed as a problem in the last 10 to 15 years, representing $4.8 billion in added healthcare costs," said Patrick Schloss, PhD, a microbiologist at the University of Michigan in Ann Arbor who led the study. "One of the biggest risk factors for someone acquiring C. diff is exposure to antibiotics. That puts a huge pool of people at risk."

Dr. Schloss worked with one of his former graduate students to test eight antibiotics in 16 different treatment conditions to see how they altered the normal gut microbiota of mice. Then, they measured how those altered communities responded when exposed to C. diff.

To predict each individual mouse's risk of infection, the team built and applied a mathematical algorithm that acts like an email spam filter to their data sets. They found their model could predict whether a mouse, starting with a particular gut bacterial community, would fall ill with C. diff with about 90 percent accuracy.

According to Alyxandria Schubert, PhD, Dr. Schloss' former graduate student and current postdoctoral researcher in the Schloss laboratory, the mathematical model could also work for human patients.

"If we could assess a patient's microbiota from a stool sample — especially if they are getting antibiotics — we could look at what bacteria are missing," said Dr. Schubert. "You could perhaps give patients a probiotic supplement with the goal of restoring their microbiota community structure to a healthy state."



More articles on C. diff:
6 risk factors for increased mortality among children with C. diff infections
How UV disinfection affects C. diff: 3 study findings
Acid-reducing drugs for children increase C. diff infection risks, researchers say

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