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 models 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 ASUs 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 isol
|Contact: Carol Hughes|
Arizona State University