BOSTON – A mathematical model that looks at different strategies for curbing hospital-acquired infections suggests that antimicrobial cycling and patient isolation may be effective approaches when patients are harboring dual-resistant bacteria.
In an era of “superbugs,” such as methicillin-resistant Staphylococcus aureas (MRSA), and an increasing public awareness and concern over bacterial infections, this type of modeling, if used to develop policies and treatment protocols, may reduce dual drug-resistant infections in hospitals.
The model’s results will be presented by Carlos Castillo-Chavez, an Arizona State University Regents’ Professor on Feb. 17 at the American Association for the Advancement of Science annual meeting. Castillo-Chavez will be honored at the meeting with the 2007 AAAS Mentor Award for his efforts to help underrepresented students earn doctoral degrees in the sciences.
In discussing the mathematical models, he notes that the research is an outgrowth of an undergraduate honors thesis by Karen C. Chow, now a graduate student at ASU, in collaboration with his postdoctoral research associate Xiaohong Wang.
“We deal primarily with the issue of finding ways of slowing down the growing levels of dual resistance to antimicrobials that are the result of their intense use in the treatment of nosocomial (hospital-acquired) infections,” says Castillo-Chavez, a mathematical epidemiologist in ASU’s College of Liberal Arts and Sciences.
“Model simulations were used to compare the effects of antimicrobial cycling, in which antibiotic classes are alternated over time, with mixing programs (random allocation of treatment drugs) in a setting where the goal is that of reducing the prevalence of dual resistance,” Castillo-Chavez says.
“Resistance to multiple drugs cannot be ignored and cycling programs appear more useful in reducing dual resistance than the random mixing regime,” he says. “The early diagnosis and isolation of colonized patients with dual-resistant bacteria turns out to be quite effective at maintaining lower levels of dual resistance in hospitals.”
He notes: “This seems to be the first time that models are used to deal with the evaluation of two distinct methods of reducing the impact of dual resistance in hospitals. Models that focus on reducing the prevalence of pathogens resistant to two types of drugs, excluding the possibility of dual resistance, have been studied in the past. Models were used to show that random allocation treatment regimes might be better than cycling.
“Here, we show that cycling may be useful when dealing with dual resistance – the most worrisome hospital situation,” he says.
“Our theoretical work shows that cycling is better if the goal is to reduce dual antimicrobial resistance. We explore the impact of isolating individuals who have developed dual resistance and found out that isolation, in fact, dramatically reduces the persistence of dual resistance. However, we never win the battle against antimicrobial resistance through the exclusive use of integrated microbial management approaches that focus entirely on the prescription of antibiotics,” he says.
“Focusing on reducing dual resistance results in increases in the levels of individuals experiencing single resistance. In other words, at the end of the day, drugs provide no silver bullet and only policies that reward their judicious use have a shot at slowing down what appears to be a loosing battle,” he says.
“If we insist in the exclusive use of antimicrobials to fight nosocomial infections, then it is only a matter of time before we begin to run out of effective antibiotics.”
The next step, according to Castillo-Chavez, is to connect these models more explicitly to specific studies, and to collaborate with others who are treating patients in hospitals.
Arizona State University
College of Liberal Arts and Sciences
School of Life Sciences
Tempe, Arizona USA
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