Scientists at York University and a national team of collaborators have developed new mathematical models that will help researchers, doctors and policymakers address the challenging public health issue of antibiotic resistance in bacteria. The research, co-led by postdoctoral fellows Josie Hughes and Xi Huo, was published in the journal PLOS ONE.
Jianhong Wu
Drug-resistant bacteria, commonly called superbugs, are a really big issue in healthcare facilities because they can spread easily and cause an outbreak,” says a co-author Jianhong Wu, Canada Research Chair and University Distinguished Research Professor at the Faculty of Science at York University. “As you might imagine, it’s hard to contain these infections when treatments are ineffective. And experts worry that it’s only a matter of time before we run out of effective options to treat most infections.”
The team developed math models that focus on a strategy called “antimicrobial de-escalation,” which is widely used in hospitals but poorly understood in terms of its effects.
When a patient in a hospital has an unknown bacterial infection, her doctor orders lab tests to find out what the infection is and in the meantime gives her a broad-spectrum antibiotic that acts against a wide range of bacteria. When the lab results come back a few days later, the doctor may switch the treatment to a narrow-spectrum antibiotic that targets the culprit bacteria. This alteration of treatment is referred to as antimicrobial de-escalation.
The goal of de-escalation is to reduce the use of precious broad-spectrum antibiotics, so that bacteria are less likely to develop resistance to these drugs. The effects of it can be complex, however. For instance, de-escalation preserves broad-spectrum antibiotic therapies and reduces costs, but it might also increase the emergence of multi-drug resistance bacteria strains and leave patients vulnerable to superinfections in some contexts.
“It can be a tradeoff in some situations, and practitioners need the proper tools to make evidence-based decisions,” explains Wu.
To address this gap, this national team developed math models that quantify and estimate the benefits and unintended consequences of antimicrobial de-escalation. The models address de-escalation for the bacteria Pseudomonas aeruginosa in the setting of an intensive care unit—but they could be adapted for other microbes, drugs and facilities.
The research was part of a larger national and interdisciplinary research project funded by the Canadian Institutes of Health Research and the Natural Sciences & Engineering Research Council of Canda to develop an Antimicrobial Resistance Diversity Index that can guide research and decision-making related to drug resistance.