title: Cycling empirical antibiotic therapy in hospitals: meta-analysis and models.
abstract: The rise of resistance together with the shortage of new broad-spectrum antibiotics underlines the urgency of optimizing the use of available drugs to minimize disease burden. Theoretical studies suggest that coordinating empirical usage of antibiotics in a hospital ward can contain the spread of resistance. However, theoretical and clinical studies came to different conclusions regarding the usefulness of rotating first-line therapy (cycling). Here, we performed a quantitative pathogen-specific meta-analysis of clinical studies comparing cycling to standard practice. We searched PubMed and Google Scholar and identified 46 clinical studies addressing the effect of cycling on nosocomial infections, of which 11 met our selection criteria. We employed a method for multivariate meta-analysis using incidence rates as endpoints and find that cycling reduced the incidence rate/1000 patient days of both total infections by 4.95 [9.43-0.48] and resistant infections by 7.2 [14.00-0.44]. This positive effect was observed in most pathogens despite a large variance between individual species. Our findings remain robust in uni- and multivariate metaregressions. We used theoretical models that reflect various infections and hospital settings to compare cycling to random assignment to different drugs (mixing). We make the realistic assumption that therapy is changed when first line treatment is ineffective, which we call "adjustable cycling/mixing". In concordance with earlier theoretical studies, we find that in strict regimens, cycling is detrimental. However, in adjustable regimens single resistance is suppressed and cycling is successful in most settings. Both a meta-regression and our theoretical model indicate that "adjustable cycling" is especially useful to suppress emergence of multiple resistance. While our model predicts that cycling periods of one month perform well, we expect that too long cycling periods are detrimental. Our results suggest that "adjustable cycling" suppresses multiple resistance and warrants further investigations that allow comparing various diseases and hospital settings.
authors: Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Division of Global Health Equity, Brigham and Women's Hospital/Harvard Medical School, Boston, Massachusetts, United States of America.
authors: Pia
authors: P
authors: Abel zur Wiesch
authors: Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.
authors: Roger
authors: R
authors: Kouyos
authors: Division of Infectious Diseases, Brigham and Women's Hospital/Harvard Medical School, Boston, Massachusetts, United States of America.
authors: Sören
authors: S
authors: Abel
authors: Department of Psychiatry and Psychology, School for Mental Health and Neuroscience, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maastricht, The Netherlands.
authors: Wolfgang
authors: W
authors: Viechtbauer
authors: Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.
authors: Sebastian
authors: S
authors: Bonhoeffer
mesh_terms: D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D001424:Bacterial Infections / Q000188:drug therapy* / Q000453:epidemiology / Q000382:microbiology; D016903:Drug Monitoring; D024881:Drug Resistance, Bacterial; D024901:Drug Resistance, Multiple, Bacterial; D036262:Empirical Research; D006090:Gram-Negative Bacteria / Q000187:drug effects* / Q000254:growth & development; D006094:Gram-Positive Bacteria / Q000187:drug effects* / Q000254:growth & development; D006757:Hospital Units; D006801:Humans; D015994:Incidence; D008954:Models, Biological; D013997:Time Factors
chemical_list: D000900:Anti-Bacterial Agents