Research Article: Cycling Empirical Antibiotic Therapy in Hospitals: Meta-Analysis and Models

Date Published: June 26, 2014

Publisher: Public Library of Science

Author(s): Pia Abel zur Wiesch, Roger Kouyos, Sören Abel, Wolfgang Viechtbauer, Sebastian Bonhoeffer, Claus O. Wilke.


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.

Partial Text

The emergence and spread of antibiotic resistance threatens our ability to treat bacterial infections and is a substantial danger for public health world-wide [1]. Resistant strains are especially prevalent in hospitals, where the high usage of antibiotics facilitates emergence and spread of resistant strains. Globally, 8% of hospital stays result in nosocomial infections [1]. It has been estimated that 70% of these are caused by single- or multiple-resistant bacteria [2]. Compared to infections by susceptible bacteria, those caused by resistant strains often increase mortality, morbidity and costs [3]. While treatment can be tailored to the pathogen and its resistance profile once cultures are available, treatment typically needs to be initiated immediately. This treatment phase is called empirical therapy. In single hospitals or wards, population-wide empirical treatment of patients can be coordinated, and several such strategies have been proposed to fight resistance [4]–[10]. Here, we focus on the comparison of two strategies on which most clinical and theoretical studies have focused so far: The first is “cycling” i.e. scheduled changes of the predominant antibiotic in a whole ward or hospital. The second is “mixing” i.e. the random assignment of patients to different antibiotics, such that at any given time point multiple antibiotics are employed in approximately equal proportions. Mixing has been seen as the strategy closest to the current usage patterns in most wards [5]. Theoretical models predict that, when different antibiotics are employed at comparable average frequencies, mixing should outperform cycling since the pathogen is subject to greater environmental heterogeneity when transmitted from host to host [5], [6]. Clinical studies addressing the general usefulness of cycling have come to contradictory results. Not only has no clear pattern emerged from these studies, but also some studies report divergent outcomes for different pathogen species. A qualitative meta-analysis [10] has argued that cycling could be beneficial for preserving drug susceptibility in Pseudomonas aeruginosa. However, neither a quantitative pathogen specific meta-analysis, nor a theoretical explanation of potential benefits of cycling is available to date.

Unfortunately, morbidity and mortality attributable to resistant nosocomial infections are only known for a few pathogens [12]. Therefore, we need measurable proxies for disease burden. Hospital-acquired infections with both susceptible and resistant pathogens increase morbidity and mortality. In both our meta-analyses and the epidemiological model, we follow the total number of patients infected with either resistant or susceptible strains. Moreover, it has been shown that patient outcome is worse when receiving inappropriate therapy, i.e. being assigned to an ineffective initial treatment [13], [14]. In our epidemiological model, we can quantify inappropriately treated patients. However, there are no matched data for resistance profile and antibiotic therapy provided in any of the clinical studies. We chose the incidence rate of resistant infections as second endpoint in the meta-analysis, because having a resistant infection increases the probability of inappropriate therapy.

The question when to use cycling or mixing has been controversially debated [10], and clinical and theoretical studies came to different conclusions [5]–[7], [9], [10], [61]. There are two possible explanations for this divergence between theory and clinical observations and potentially between different pathogens: We might not have sufficient data on population-wide resistance emergence. Alternatively, specific settings or differences in pathogen biology might not have been adequately captured in theoretical models so far.




0 0 vote
Article Rating
Notify of
Inline Feedbacks
View all comments