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 ß-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.

Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review.

OBJECTIVE: Measuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI. METHODS: Systematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995-2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics. RESULTS: 57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances. CONCLUSIONS: Administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative.