1 Department of Medical Microbiology, Frenchay Hospital, Bristol BS16 1LE; 2 Infection Unit, East Block, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
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Abstract |
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Keywords: antibiotic resistance , interventions to optimize antibiotic prescribing , intensive care units
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Introduction |
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Cycling or rotation is the scheduled substitution of a class of antibiotics (or a specific member of a class) with a different class (or a specific member of that class) that exhibits a comparable spectrum of activity. This substitution may be followed after a fixed interval by a third, fourth or, indeed, any number of substitutions, but the cycle must be repeated, with re-introduction of the original class/drug. Cycling/rotation should not be confused with scheduled changes or restrictions of antibiotic regimens without repeating the process (a common occurrence among both investigators and reviewers). The duration of each cycle is based on either local susceptibility patterns or a pre-determined time period.
Cycling normally involves replacing an antibiotic belonging to one class with one or more belonging to different classes, as opposed to substituting one member of a class with another member of the same class. Although some investigators have withdrawn one aminoglycoside (gentamicin) and replaced it with another (amikacin), this practice is fraught with difficulties, as drugs belonging to the same class usually share resistance mechanisms. On the other hand, an antibiotic belonging to one class may select for resistance to drugs belonging to one or more unrelated classes as a result of genetic linkage of resistance determinants that encode resistance to multiple classes of antibiotics.
The principle underpinning cycling is that the more frequently an antibiotic is prescribed, the more likely it is that resistance to it will develop. Withdrawal of a class of antibiotics for a pre-determined period will therefore limit the selective pressures exerted by those agents (and hence the emergence of resistance), thereby allowing resistance rates to the withdrawn drug to stabilize, or even fall, during the period of restriction and enabling it to be re-introduced at a later date with its efficacy intact. The outcome of the intervention will depend on the situation before its implementation. For example, if rates of resistance to a particular antibiotic are stable before the intervention is introduced, an increase (or decrease) in the volume of prescribing of that drug will result in a gradual increase (or decrease) in the incidence of resistance to it. However, if the baseline resistance rate was increasing before implementation, a reduction in the use of the antibiotic may or may not lead to an immediate decrease in the rates of resistance; similarly, if the baseline resistance rate was falling, an increase in use may or may not be followed by an increase in the resistance rate.1 An equally important objective of cycling is to avoid the emergence of significant populations of organisms resistant to the substitute drug, and the duration of each cycle should be sufficiently short to ensure that this is the case.
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Literature search |
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Evidence |
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In response to increasing rates of resistance to gentamicin among aerobic Gram-negative bacilli (AGNB), Gerding & Larson9 described four successive changes in the use of aminoglycosides at the Minneapolis Veterans' Affairs Medical Center; the design of the study was an interrupted time series. During an initial 4-month period during which gentamicin was the predominant aminoglycoside in use, the incidences of resistance to gentamicin and amikacin were 12% and 3.8%, respectively (baseline data). During the following 26 months, amikacin replaced gentamicin as the predominant aminoglycoside and the incidence of resistance to gentamicin fell to 6.4% (P < 0.001), whereas that to amikacin was 3.2% (not a statistically significant difference). Gentamicin was re-introduced and over the next 12 months the rate of resistance to it increased to 9.2% (P < 0.001); the rate of resistance to amikacin was 3.9% (not a statistically significant difference) during that period. Finally, amikacin again became the predominant aminoglycoside during the subsequent 12 months and resistance to gentamicin declined to 5.4% (P < 0.001), whereas that to amikacin also fell to 2.8% (P < 0.005). It was not at the outset the intention of the investigators to undertake a study of cycling. The changes were not pre-arranged, but rather evolved in response to emerging patterns of resistance to aminoglycosides and expenditure on these drugs.
Young et al.10 cycled the use of aminoglycosides at the Houston Veterans' Administration Medical Center in response to increasing rates of resistance to gentamicin among AGNB; the study was designed as an interrupted time series. During a 4-month baseline period when gentamicin was the principal aminoglycoside used and amikacin administration was restricted, the incidence of resistance to gentamicin was 14% (the corresponding figure for amikacin being 2.4%). For the next 15 months, amikacin became the predominant aminoglycoside and gentamicin usage was restricted. The incidence of resistance to gentamicin declined to 9.2% (P < 0.005) (the resistance rate for amikacin remaining at 2.2%). During a 21-month follow-up period when all restrictions on aminoglycoside usage were lifted (although, in practice, prescriptions of amikacin approximated the baseline level owing to it being markedly more expensive than gentamicin) the rate of resistance to gentamicin rose to 15.3%; the incidence of resistance to amikacin increased to 4% (P < 0.0000001).
Bradley et al.11 carried out an interrupted time series in which the empirical antibiotic therapy of febrile neutropenic patients was rotated. The impetus for the study was the high incidence of colonization of patients on a haematological malignancy unit with glycopeptide-resistant enterococci (GRE). During an initial 4-month period when baseline data were collected (phase 1), patients received standard therapy, ceftazidime; the incidence of GRE colonization was 57%. For the next 8 months (phase 2) ceftazidime was replaced by piperacillin/tazobactam and aggressive infection control measures were implemented. The rate of colonization with GRE during the first 4 months of phase 2 (phase 2a) was 29% (P < 0.002 compared with phase 1) and that during the second 4 months (phase 2b) was 8% (P < 0.0001 compared with phase 1). During the subsequent final 4 months of the study (phase 3) ceftazidime was re-introduced (the enhanced infection control measures were maintained) and the incidence of colonization with GRE increased to 36% (not a statistically significant difference compared with phase 1). Infections attributed to GRE were observed only during phases 1 and 3. The authors acknowledge that the reduction in the GRE carriage rates during phase 2 could have been, at least in part, the result of improved infection control measures, but concluded that the increase in the incidence of colonization following the re-introduction of ceftazidime, at a time when the colonization rate was very low and the enhanced infection control measures were being maintained, make it likely that the switch to ceftazidime accounted for the change.
The final study12 was a controlled clinical trial which was undertaken to determine whether antibiotic rotation leads to reduced colonization with multidrug-resistant AGNB among patients on a neonatal intensive care unit. During a 1-year period, patients with proven or suspected infections caused by AGNB were assigned to a study or control group. Those in the former group received empirical antibiotic therapy on a monthly rotating basis; the antibiotics which were cycled were gentamicin, piperacillin/tazobactam and ceftazidime. Those in the latter group were given empirical antibiotics according to the preferences of the prescribers. No baseline data were collected. At the end of the study, differences in total antibiotic usage, the incidence of colonization with multidrug-resistant AGNB and the incidences of nosocomial infections and mortality between the two groups were not statistically significant.
In summary, three of the four studies911 demonstrated that cycling was beneficial in terms of a reduced incidence of isolation of antibiotic-resistant bacteria when the drugs that allegedly precipitated the resistance were withdrawn; the fourth study12 failed to identify such a benefit. Only one of the studies12 evaluated the effect of cycling on clinical outcome; no significant difference between the control and intervention groups was reported. Moreover, in all three of the former studies, rates of resistance to the initial (precipitating) antibiotics returned to baseline levels when these drugs were re-instated. The results of these studies do not permit meaningful conclusions regarding the efficacy of cycling to be drawn owing to lack of standardization and multiple methodological flaws. For example:
It is not possible to reach reliable conclusions regarding the efficacy of cycling as a means of controlling antibiotic resistance rates on the basis of the existing published literature. The studies that will need to resolve the issue of efficacy will have to be adequately powered in order to overcome confounding variables and will need to employ high-quality epidemiological tools, sophisticated techniques for determining resistance mechanisms and carrying out molecular typing and effective infection control measures.
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Future studies |
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Mathematical modelling |
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Bonhoeffer et al.15 used mathematical modelling to compare three treatment protocols involving two different antibiotics: periodic cycling of the drugs (cycling); administration of each drug (only one per patient) to equal proportions of the infected host population (5050 or mixed treatment); and simultaneous administration of both drugs to each infected host (combination therapy). They concluded that, when more than one antibiotic is employed, both 5050 treatment and combination therapy are always superior to cycling, irrespective of how frequently the drugs are cycled. The theoretical superiority of mixed antibiotic use over cycling was confirmed by Lo et al.16 who used a stochastic mathematical model to compare the efficacies of the two interventions in terms of preventing colonization with multidrug-resistant bacteria in the ICU setting.
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Conclusion |
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Footnotes |
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References |
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2 . Dominguez, E. A., Smith, T. L., Reed, E. et al. (2000). A pilot study of antibiotic cycling in a hematologyoncology unit. Infection Control and Hospital Epidemiology 21, Suppl., S4S8.[ISI][Medline]
3 . Puzniak, L. A., Mayfield, J., Leet, T. et al. (2001). Acquisition of vancomycin-resistant enterococci during scheduled antimicrobial rotation in an intensive care unit. Clinical Infectious Diseases 33, 1517.[CrossRef][ISI][Medline]
4 . Raymond, D. P., Pelletier, S. J., Crabtree, T. D. et al. (2001). Impact of a rotating empiric antibiotic schedule on infectious mortality in an intensive care unit. Critical Care Medicine 29, 11018.[ISI][Medline]
5 . Saravolatz, L. D., Arking, L., Pohlod, D. et al. (1984). An outbreak of gentamicin-resistant Klebsiella pneumoniae: analysis of control measures. Infection Control 5, 7984.[ISI][Medline]
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Gruson, D., Hilbert, G., Vargas, F. et al. (2000). Rotation and restricted use of antibiotics in a medical intensive care unit. American Journal of Respiratory and Critical Care Medicine 162, 83743.
7 . Moss, W. J., Beers, M. C., Johnson, E. et al. (2002). Pilot study of antibiotic cycling in a pediatric intensive care unit. Critical Care Medicine 30, 187782.[CrossRef][ISI][Medline]
8 . Gruson, D., Hilbert, G., Vargas, F. et al. (2003). Strategy of antibiotic rotation: long-term effect on incidence and susceptibilities of Gram-negative bacilli responsible for ventilator-associated pneumonia. Critical Care Medicine 31, 190814.[CrossRef][ISI][Medline]
9 . Gerding, D. N. & Larson, T. A. (1985). Aminoglycoside resistance in gram-negative bacilli during increased amikacin use: comparison of experience in fourteen United States hospitals with experience in the Minneapolis Veterans Administration Medical Center. American Journal of Medicine 79, Suppl. 1A, 17.
10 . Young, E. J., Sewell, C. M., Koza, M. A. et al. (1985). Antibiotic resistance patterns during aminoglycoside restriction. American Journal of Medical Sciences 290, 2237.[ISI]
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Bradley, S. J., Wilson, A. L., Allen, M. C. et al. (1999). The control of hyperendemic glycopeptide-resistant Enterococcus spp. on a haematology unit by changing antibiotic usage. Journal of Antimicrobial Chemotherapy 43, 2616.
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Toltzis, P., Dul, M., Hoyen, C. et al. (2002). The effect of antibiotic rotation on colonization with antibiotic-resistant bacilli in a neonatal intensive care unit. Pediatrics 110, 70711.
13 . Fridkin, S. K. (2003). Routine cycling of antimicrobial agents as an infection-control measure. Clinical Infectious Diseases 36, 143844.[CrossRef][ISI][Medline]
14 . John, J. F., Jr & Rice, L. B. (2000). The microbial genetics of antibiotic cycling. Infection Control and Hospital Epidemiology 21, Suppl., S22S31.[ISI][Medline]
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Bonhoeffer, S., Lipsitch, M. & Levin, B. R. (1997). Evaluating treatment protocols to prevent antibiotic resistance. Proceedings of the National Academy of Sciences USA 94, 1210611.
16 . Lo, M., Bergstrom, C. T. & Lipsitch, M. (2002). Comparison of antimicrobial cycling and mixing using stochastic mathematical models. In Abstracts of the Forty-second Interscience Conference on Antimicrobial Agents and Chemotherapy, San Diego, CA, 2002. Abstract 1345, p. 326. American Society for Microbiology, Washington, DC, USA.