The effects of antibiotic cycling and mixing on acquisition of antibiotic resistant bacteria in the ICU: A post-hoc individual patient analysis of a prospective cluster-randomized crossover study

International audienceBackground Repeated rotation of empiric antibiotic treatment strategies is hypothesized to reduce antibiotic resistance. Clinical rotation studies failed to change unit-wide prevalence of antibiotic resistant bacteria (ARB) carriage, including an international cluster-randomized crossover study. Unit-wide effects may differ from individual effects due to “ecological fallacy”. This post-hoc analysis of a cluster-randomized crossover study assesses differences between cycling and mixing rotation strategies in acquisition of carriage with Gram-negative ARB in individual patients.Methods This was a controlled cluster-randomized crossover study in 7 ICUs in 5 European countries. Clinical cultures taken as routine care were used for endpoint assessment. Patients with a first negative culture and at least one culture collected in total were included. Community acquisitions (2 days of admission or less) were excluded. Primary outcome was ICU-acquisition of Enterobacterales species with reduced susceptibility to: third- or fourth generation cephalosporins or piperacillin-tazobactam, and Acinetobacter species and Pseudomonas aeruginosa with reduced susceptibility for piperacillin-tazobactam or carbapenems. Cycling (altering first-line empiric therapy for Gram-negative bacteria, every other 6-weeks), to mixing (changing antibiotic type every empiric antibiotic course). Rotated antibiotics were third- or fourth generation cephalosporins, piperacillin-tazobactam and carbapenems.Results For this analysis 1,613 admissions were eligible (855 and 758 during cycling and mixing, respectively), with 16,437 microbiological cultures obtained. Incidences of acquisition with ARB during ICU-stay were 7.3% (n = 62) and 5.1% (n = 39) during cycling and mixing, respectively (p-value 0.13), after a mean of 17.7 (median 15) and 20.8 (median 13) days. Adjusted odds ratio for acquisition of ARB carriage during mixing was 0.62 (95% CI 0.38 to 1.00). Acquired carriage with ARB were Enterobacterales species (n = 61), Pseudomonas aeruginosa (n = 38) and Acinetobacter species (n = 20), with no statistically significant differences between interventions.Conclusions There was no statistically significant difference in individual patients’ risk of acquiring carriage with Gram-negative ARB during cycling and mixing. These findings substantiate the absence of difference between cycling and mixing on the epidemiology of Gram-negative ARB in ICU

The effects of antibiotic cycling and mixing on antibiotic resistance in intensive care units : a cluster-randomised crossover trial

Background: Whether antibiotic rotation strategies reduce prevalence of antibiotic-resistant, Gram-negative bacteria in intensive care units (ICUs) has not been accurately established. We aimed to assess whether cycling of antibiotics compared with a mixing strategy (changing antibiotic to an alternative class for each consecutive patient) would reduce the prevalence of antibiotic-resistant, Gram-negative bacteria in European intensive care units (ICUs). Methods: In a cluster-randomised crossover study, we randomly assigned ICUs to use one of three antibiotic groups (third-generation or fourth-generation cephalosporins, piperacillin–tazobactam, and carbapenems) as preferred empirical treatment during 6-week periods (cycling) or to change preference after every consecutively treated patient (mixing). Computer-based randomisation of intervention and rotated antibiotic sequence was done centrally. Cycling or mixing was applied for 9 months; then, following a washout period, the alternative strategy was implemented. We defined antibiotic-resistant, Gram-negative bacteria as Enterobacteriaceae with extended-spectrum β-lactamase production or piperacillin–tazobactam resistance, and Acinetobacter spp and Pseudomonas aeruginosa with piperacillin–tazobactam or carbapenem resistance. Data were collected for all admissions during the study. The primary endpoint was average, unit-wide, monthly point prevalence of antibiotic-resistant, Gram-negative bacteria in respiratory and perineal swabs with adjustment for potential confounders. This trial is registered with ClinicalTrials.gov, number NCT01293071. Findings: Eight ICUs (from Belgium, France, Germany, Portugal, and Slovenia) were randomly assigned and patients enrolled from June 27, 2011, to Feb 16, 2014. 4069 patients were admitted during the cycling periods in total and 4707 were admitted during the mixing periods. Of these, 745 patients during cycling and 853 patients during mixing were present during the monthly point-prevalence surveys, and were included in the main analysis. Mean prevalence of the composite primary endpoint was 23% (168/745) during cycling and 22% (184/853) during mixing (p=0·64), yielding an adjusted incidence rate ratio during mixing of 1·039 (95% CI 0·837–1·291; p=0·73). There was no difference in all-cause in-ICU mortality between intervention periods. Interpretation: Antibiotic cycling does not reduce the prevalence of carriage of antibiotic-resistant, Gram-negative bacteria in patients admitted to the ICU. Funding: European Union Seventh Framework Programme

The effects of antibiotic cycling and mixing on antibiotic resistance in intensive care units : a cluster-randomised crossover trial

Background: Whether antibiotic rotation strategies reduce prevalence of antibiotic-resistant, Gram-negative bacteria in intensive care units (ICUs) has not been accurately established. We aimed to assess whether cycling of antibiotics compared with a mixing strategy (changing antibiotic to an alternative class for each consecutive patient) would reduce the prevalence of antibiotic-resistant, Gram-negative bacteria in European intensive care units (ICUs). Methods: In a cluster-randomised crossover study, we randomly assigned ICUs to use one of three antibiotic groups (third-generation or fourth-generation cephalosporins, piperacillin–tazobactam, and carbapenems) as preferred empirical treatment during 6-week periods (cycling) or to change preference after every consecutively treated patient (mixing). Computer-based randomisation of intervention and rotated antibiotic sequence was done centrally. Cycling or mixing was applied for 9 months; then, following a washout period, the alternative strategy was implemented. We defined antibiotic-resistant, Gram-negative bacteria as Enterobacteriaceae with extended-spectrum β-lactamase production or piperacillin–tazobactam resistance, and Acinetobacter spp and Pseudomonas aeruginosa with piperacillin–tazobactam or carbapenem resistance. Data were collected for all admissions during the study. The primary endpoint was average, unit-wide, monthly point prevalence of antibiotic-resistant, Gram-negative bacteria in respiratory and perineal swabs with adjustment for potential confounders. This trial is registered with ClinicalTrials.gov, number NCT01293071. Findings: Eight ICUs (from Belgium, France, Germany, Portugal, and Slovenia) were randomly assigned and patients enrolled from June 27, 2011, to Feb 16, 2014. 4069 patients were admitted during the cycling periods in total and 4707 were admitted during the mixing periods. Of these, 745 patients during cycling and 853 patients during mixing were present during the monthly point-prevalence surveys, and were included in the main analysis. Mean prevalence of the composite primary endpoint was 23% (168/745) during cycling and 22% (184/853) during mixing (p=0·64), yielding an adjusted incidence rate ratio during mixing of 1·039 (95% CI 0·837–1·291; p=0·73). There was no difference in all-cause in-ICU mortality between intervention periods. Interpretation: Antibiotic cycling does not reduce the prevalence of carriage of antibiotic-resistant, Gram-negative bacteria in patients admitted to the ICU. Funding: European Union Seventh Framework Programme