Decontamination strategies and bloodstream infections with antibiotic-resistant microorganisms in ventilated patients : a randomized clinical trial

Importance: The effects of chlorhexidine (CHX) mouthwash, selective oropharyngeal decontamination (SOD), and selective digestive tract decontamination (SDD) on patient outcomes in ICUs with moderate to high levels of antibiotic resistance are unknown. Objective: To determine associations between CHX 2%, SOD, and SDD and the occurrence of ICU-acquired bloodstream infections with multidrug-resistant gram-negative bacteria (MDRGNB) and 28-day mortality in ICUs with moderate to high levels of antibiotic resistance. Design, setting, and participants: Randomized trial conducted from December 1, 2013, to May 31, 2017, in 13 European ICUs where at least 5% of bloodstream infections are caused by extended-spectrum [beta]-lactamase-producing Enterobacteriaceae. Patients with anticipated mechanical ventilation of more than 24 hours were eligible. The final date of follow-up was September 20, 2017. Interventions: Standard care was daily CHX 2% body washings and a hand hygiene improvement program. Following a baseline period from 6 to 14 months, each ICU was assigned in random order to 3 separate 6-month intervention periods with either CHX 2% mouthwash, SOD (mouthpaste with colistin, tobramycin, and nystatin), or SDD (the same mouthpaste and gastrointestinal suspension with the same antibiotics), all applied 4 times daily. Main outcomes and measures: The occurrence of ICU-acquired bloodstream infection with MDRGNB (primary outcome) and 28-day mortality (secondary outcome) during each intervention period compared with the baseline period. Results: A total of 8665 patients (median age, 64.1 years5561 men [64.2%]) were included in the study (2251, 2108, 2224, and 2082 in the baseline, CHX, SOD, and SDD periods, respectively). ICU-acquired bloodstream infection with MDRGNB occurred among 144 patients (154 episodes) in 2.1%, 1.8%, 1.5%, and 1.2% of included patients during the baseline, CHX, SOD, and SDD periods, respectively. Absolute risk reductions were 0.3% (95% CI, -0.6% to 1.1%), 0.6% (95% CI, -0.2% to 1.4%), and 0.8% (95% CI, 0.1% to 1.6%) for CHX, SOD, and SDD, respectively, compared with baseline. Adjusted hazard ratios were 1.13 (95% CI, 0.68-1.88), 0.89 (95% CI, 0.55-1.45), and 0.70 (95% CI, 0.43-1.14) during the CHX, SOD, and SDD periods, respectively, vs baseline. Crude mortality risks on day 28 were 31.9%, 32.9%, 32.4%, and 34.1% during the baseline, CHX, SOD, and SDD periods, respectively. Adjusted odds ratios for 28-day mortality were 1.07 (95% CI, 0.86-1.32), 1.05 (95% CI, 0.85-1.29), and 1.03 (95% CI, 0.80-1.32) for CHX, SOD, and SDD, respectively, vs baseline. Conclusions and relevance: Among patients receiving mechanical ventilation in ICUs with moderate to high antibiotic resistance prevalence, use of CHX mouthwash, SOD, or SDD was not associated with reductions in ICU-acquired bloodstream infections caused by MDRGNB compared with standard care

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

Immunophenotypes of anti-SARS-CoV-2 responses associated with fatal COVID-19

Background The relationship between anti-SARS-CoV-2 humoral immune response, pathogenic inflammation, lymphocytes and fatal COVID-19 is poorly understood. Methods A longitudinal prospective cohort of hospitalised patients with COVID-19 (n=254) was followed up to 35 days after admission (median, 8 days). We measured early anti-SARS-CoV-2 S1 antibody IgG levels and dynamic (698 samples) of quantitative circulating T-, B- and natural killer lymphocyte subsets and serum interleukin-6 (IL-6) response. We used machine learning to identify patterns of the immune response and related these patterns to the primary outcome of 28-day mortality in analyses adjusted for clinical severity factors. Results Overall, 45 (18%) patients died within 28 days after hospitalisation. We identified six clusters representing discrete anti-SARS-CoV-2 immunophenotypes. Clusters differed considerably in COVID-19 survival. Two clusters, the anti-S1-IgGlowestTlowestBlowestNKmodIL-6mod, and the anti-S1-IgGhighTlowBmodNKmodIL-6highest had a high risk of fatal COVID-19 (HR 3.36–21.69; 95% CI 1.51–163.61 and HR 8.39–10.79; 95% CI 1.20–82.67; p≤0.03, respectively). The anti-S1-IgGhighestTlowestBmodNKmodIL-6mod and anti-S1-IgGlowThighestBhighestNKhighestIL-6low cluster were associated with moderate risk of mortality. In contrast, two clusters the anti-S1-IgGhighThighBmodNKmodIL-6low and anti-S1-IgGhighestThighestBhighNKhighIL-6lowest clusters were characterised by a very low risk of mortality. Conclusions By employing unsupervised machine learning we identified multiple anti-SARS-CoV-2 immune response clusters and observed major differences in COVID-19 mortality between these clusters. Two discrete immune pathways may lead to fatal COVID-19. One is driven by impaired or delayed antiviral humoral immunity, independently of hyper-inflammation, and the other may arise through excessive IL-6-mediated host inflammation response, independently of the protective humoral response. Those observations could be explored further for application in clinical practice