Antibiotic cycling to decrease bacterial antibiotic resistance: a 5-year experience on a bone marrow transplant unit

Cadena, José; Taboada, Carlo; Burgess, David S.; Z, J; Lewis, James S.; Freytes, César O.; Patterson, Jan E.

doi:10.1038/sj.bmt.1705704

Cited by 27 publications

(25 citation statements)

References 25 publications

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“…28,29 Antibiotic cycling has not been universally accepted, however, as certain studies have demonstrated shortcomings with the therapy, including an increase in certain undesired pathogens and an increase in hospital costs. 30,31 Our data also agree with data of multiple other investigators who also found that adequate initial therapy was crucial for the successful treatment of VAP. 32 An important finding of our article demonstrates that deescalation therapy for VAP in a critically ill surgical patient population did not lead to increased mortality or RP.…”

Section: Discussionsupporting

confidence: 94%

Does De-Escalation of Antibiotic Therapy for Ventilator-Associated Pneumonia Affect the Likelihood of Recurrent Pneumonia or Mortality in Critically Ill Surgical Patients?

Eachempati

Hydo

Shou

et al. 2009

Journal of Trauma: Injury, Infection &Amp; Critical Care

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Section: Discussionsupporting

confidence: 94%

Does De-Escalation of Antibiotic Therapy for Ventilator-Associated Pneumonia Affect the Likelihood of Recurrent Pneumonia or Mortality in Critically Ill Surgical Patients?

Eachempati

Hydo

Shou

et al. 2009

Journal of Trauma: Injury, Infection &Amp; Critical Care

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“…The p-value as well as the slope of the regression line (red line) with 95% confidence interval of the regression is given in the figure. One study [49] was omitted because of insufficient data. C) Pathogen-specific meta-analysis.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2014

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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.

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“…Most notably while the original model predicts drug mixing or drug combination as the best strategy, our modified model indicates that drug cycling corresponds to the best strategy under otherwise equal conditions. This finding, contingent on further explorations of parameter ranges and assumptions, offers a potential way of reconciling previous contrasting reports of experimentally successful, though theoretically unfavorable, drug cycling therapies [13]–[21], [39]–[42], [69]–[72]. More investigation is necessary to make a mechanist connection between experimentally measurable variables of resistance attenuation and host population models.…”

Section: Discussionmentioning

confidence: 79%

“…A parallel review was less stringent and observed that thirteen out of fourteen studies related to drug cycling reported positive results, such as decrease of either resistance, infection rate or mortality rate, while only one reported purely negative results [14]. Subsequent studies reported positive outcomes for drug cycling [15]–[21]. While one case reported a combination of positive and negative results [21], and another discussed drawbacks of this approach [17], all of them agreed that more research is needed to identify useful strategies to combat resistance.…”

Section: Introductionmentioning

confidence: 99%

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Resource Competition May Lead to Effective Treatment of Antibiotic Resistant Infections

Gomes

Segrè

2013

PLoS ONE

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Drug resistance is a common problem in the fight against infectious diseases. Recent studies have shown conditions (which we call antiR) that select against resistant strains. However, no specific drug administration strategies based on this property exist yet. Here, we mathematically compare growth of resistant versus sensitive strains under different treatments (no drugs, antibiotic, and antiR), and show how a precisely timed combination of treatments may help defeat resistant strains. Our analysis is based on a previously developed model of infection and immunity in which a costly plasmid confers antibiotic resistance. As expected, antibiotic treatment increases the frequency of the resistant strain, while the plasmid cost causes a reduction of resistance in the absence of antibiotic selection. Our analysis suggests that this reduction occurs under competition for limited resources. Based on this model, we estimate treatment schedules that would lead to a complete elimination of both sensitive and resistant strains. In particular, we derive an analytical expression for the rate of resistance loss, and hence for the time necessary to turn a resistant infection into sensitive (tclear). This time depends on the experimentally measurable rates of pathogen division, growth and plasmid loss. Finally, we estimated tclear for a specific case, using available empirical data, and found that resistance may be lost up to 15 times faster under antiR treatment when compared to a no treatment regime. This strategy may be particularly suitable to treat chronic infection. Finally, our analysis suggests that accounting explicitly for a resistance-decaying rate may drastically change predicted outcomes in host-population models.

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Antibiotic cycling to decrease bacterial antibiotic resistance: a 5-year experience on a bone marrow transplant unit

Cited by 27 publications

References 25 publications

Does De-Escalation of Antibiotic Therapy for Ventilator-Associated Pneumonia Affect the Likelihood of Recurrent Pneumonia or Mortality in Critically Ill Surgical Patients?

Does De-Escalation of Antibiotic Therapy for Ventilator-Associated Pneumonia Affect the Likelihood of Recurrent Pneumonia or Mortality in Critically Ill Surgical Patients?

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

Resource Competition May Lead to Effective Treatment of Antibiotic Resistant Infections

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