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To evaluate the relationship between antipseudomonal antibiotic consumption and each individual drug resistance rate in Pseudomonas aeruginosa strains causing ICU acquired invasive device-related infections (IDRI).A post hoc analysis was made of the data collected prospectively from the ENVIN-HELICS registry.Intensive Care Units participating in the ENVIN-UCI registry between the years 2007 and 2016 (3-month registry each year).Patients admitted for over 24h.Annual linear and nonlinear trends of resistance rates of P. aeruginosa strains identified in IDRI and days of treatment of each antipseudomonal antibiotic family per 1000 occupied ICU bed days (DOT) were calculated.A total of 15,095 episodes of IDRI were diagnosed in 11,652 patients (6.2% out of a total of 187,100). Pseudomonas aeruginosa was identified in 2095 (13.6%) of 15,432 pathogens causing IDRI. Resistance increased significantly over the study period for piperacillin-tazobactam (P0.001), imipenem (P=0.016), meropenem (P=0.004), ceftazidime (P=0.005) and cefepime (P=0.015), while variations in resistance rates for amikacin, ciprofloxacin, levofloxacin and colistin proved nonsignificant. A significant DOT decrease was observed for aminoglycosides (P0.001), cephalosporins (P0.001), quinolones (P0.001) and carbapenems (P0.001).No significant association was observed between consumption of each antipseudomonal antibiotic family and the respective resistance rates for P. aeruginosa strains identified in IDRI.

Length of stay is one of the key determinants for the risk of nosocomial infections. The distribution of this at-risk time is heavily skewed and depends on discharge or death. This study applied landmark competing risk prediction models to account for a large proportion of short-stay patients and a small proportion of long-stay patients.

Objective:To study the impact of duration of mechanical ventilation, hospitalization and multiple ventilation episodes on the development of pneumonia while accounting for extubation as a competing event.Design:A multicenter data base from a Spanish surveillance network was used to conduct a retrospective analysis of prospectively collected intensive care patients followed from admission to discharge.Setting:Spanish intensive care units (ICUs).Patients:Mechanically ventilated adult patients from 158 ICUs with 45,486 admissions, 48,705 ventilation episodes, and 314,196 ventilator days.Methods:Competing-risk models were applied to account for extubation plus 48 hours as a competing event for acquiring ventilator-associated pneumonia (VAP).Results:Time in the ICU before mechanical ventilation was associated with an increased VAP hazard rate and with longer intubation time. This indirect prolongation of intubation increased the cumulative risk to eventually acquire VAP. For instance, comparing 3–4 versus 0 days, the adjusted VAP hazard ratio was 1.40 (95% confidence interval [CI], 1.19–1.64) and the adjusted extubation hazard ratio was 0.64 (95% CI, 0.61–0.68), which leads to an adjusted VAP subdistribution hazard ratio (sHR) of 2.13 (95% CI, 1.83–2.50). Similarly, due to prolonged intubation, multiple ventilation episodes increase the risk for VAP; the adjusted sHR is 1.52 (95% CI, 1.35–1.72) for the second episode compared to the first episode, and the adjusted sHR is 1.54 (95% CI, 1.03–2.30) for the third episode compared to the first episode. The Kaplan-Meier method produced an upward biased estimated cumulative risk for VAP.Conclusions:A competing-risk analysis is necessary to receive unbiased risk estimates and to quantify the indirect effect of intubation time on the cumulative VAP risk. Our findings may guide physicians to improve medical decisions related to the harms and benefits of the duration of ventilation.

Analysing the determinants and consequences of hospital-acquired infections involves the evaluation of large cohorts. Infected patients in the cohort are often rare for specific pathogens, because most of the patients admitted to the hospital are discharged or die without such an infection. Death and discharge are competing events to acquiring an infection, because these individuals are no longer at risk of getting a hospital-acquired infection. Therefore, the data is best analysed with an extended survival model – the extended illness-death model. A common problem in cohort studies is the costly collection of covariate values. In order to provide efficient use of data from infected as well as uninfected patients, we propose a tailored case-cohort approach for the extended illness-death model. The basic idea of the case-cohort design is to only use a random sample of the full cohort, referred to as subcohort, and all cases, namely the infected patients. Thus, covariate values are only obtained for a small part of the full cohort. The method is based on existing and established methods and is used to perform regression analysis in adapted Cox proportional hazards models. We propose estimation of all cause-specific cumulative hazards and transition probabilities in an extended illness-death model based on case-cohort sampling. As an example, we apply the methodology to infection with a specific pathogen using a large cohort from Spanish hospital data. The obtained results of the case-cohort design are compared with the results in the full cohort to investigate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

ObjectiveCompeting risks are a necessary consideration when analyzing risk factors for nosocomial infections (NIs). In this article, we identify additional information that a competing risks analysis provides in a hospital setting. Furthermore, we improve on established methods for nested case-control designs to acquire this information.MethodsUsing data from 2 Spanish intensive care units and model simulations, we show how controls selected by time-dynamic sampling for NI can be weighted to perform risk-factor analysis for death or discharge without infection. This extension not only enables hazard rate analysis for the competing risk, it also enables prediction analysis for NI.ResultsThe estimates acquired from the extension were in good agreement with the results from the full (real and simulated) cohort dataset. The reduced dataset results averted any false interpretation common in a competing-risks setting.ConclusionsUsing additional information that is routinely collected in a hospital setting, a nested case-control design can be successfully adapted to avoid a competing risks bias. Furthermore, this adapted method can be used to reanalyze past nested case-control studies to enhance their findings.

Background Beta-lactam anti-infective levels after standard dosing have been shown to be subtherapeutic when renal clearance is augmented. Objective To determine if piperacillin and meropenem are found to be in their therapeutic range in infected critically ill patients when administered by continuous intravenous infusion (CII) assisted by a therapeutic drug monitoring (TDM) report issued by the pharmacy service. Methods This prospective non-controlled intervention study evaluated septic patients in an intensive care unit. Patients received a loading dose of meropenem or piperacillin-tazobactam and the antibiotics were afterwards administered by CII. Blood concentrations were determined by high-performance liquid chromatography assays. The adequacy of β-lactam therapy in the cohort subjected to intervention was assessed by determining whether plasma levels during CII were >4 times the informed minimum inhibitory concentration during the first 96 hours of treatment. Results A total of 124 patients were subject to TDM during antibiotic treatment but, for the analysis of the fulfilment of pharmacodynamic requirements, data from 31/124 (25%) were excluded. Of the whole cohort of treatment courses, 57/93 (61.3%) reached the target level. Plasma levels were adequate in 41/70 (58.6%) and 16/23 (69.6%) of the patients treated with piperacillin-tazobactam and meropenem, respectively. Globally, recommendations based on TDM results were followed in 35/93 (37.6%) of the treatment courses. Conclusions The results of the study show that, in critically ill patients with sepsis, there is a significant proportion of treatment courses where target levels are not reached even if the antibiotics are administered by CII and TDM support is provided by the pharmacy service. This TDM support should be offered on a real-time basis to be really useful.

To test the effectiveness of a central venous catheter (CVC) insertion strategy and a hand hygiene (HH) improvement strategy to prevent central venous catheter-related bloodstream infections (CRBSI) in European intensive care units (ICUs), measuring both process and outcome indicators. Adult ICUs from 14 hospitals in 11 European countries participated in this stepped-wedge cluster randomised controlled multicentre intervention study. After a 6 month baseline, three hospitals were randomised to one of three interventions every quarter: (1) CVC insertion strategy (CVCi); (2) HH promotion strategy (HHi); and (3) both interventions combined (COMBi). Primary outcome was prospective CRBSI incidence density. Secondary outcomes were a CVC insertion score and HH compliance. Overall 25,348 patients with 35,831 CVCs were included. CRBSI incidence density decreased from 2.4/1000 CVC-days at baseline to 0.9/1000 (p

In nested case-control studies, incidence density sampling is the time-dependent matching procedure to approximate hazard ratios. The cumulative incidence function can also be estimated if information from the full cohort is used. In the presence of competing events, however, the cumulative incidence function depends on the hazard of the disease of interest and on the competing events hazard. Using hospital-acquired infection as an example (full cohort), we propose a sampling method for nested case-control studies to estimate subdistribution hazard ratios. With further information on the full cohort, the cumulative incidence function for the event of interest can then be estimated as well.

Analysing the determinants and consequences of hospital-acquired infections involves the evaluation of large cohorts. Infected patients in the cohort are often rare for specific pathogens, because most of the patients admitted to the hospital are discharged or die without such an infection. Death and discharge are competing events to acquiring an infection, because these individuals are no longer at risk of getting a hospital-acquired infection. Therefore, the data is best analysed with an extended survival model - the extended illness-death model. A common problem in cohort studies is the costly collection of covariate values. In order to provide efficient use of data from infected as well as uninfected patients, we propose a tailored case-cohort approach for the extended illness-death model. The basic idea of the case-cohort design is to only use a random sample of the full cohort, referred to as subcohort, and all cases, namely the infected patients. Thus, covariate values are only obtained for a small part of the full cohort. The method is based on existing and established methods and is used to perform regression analysis in adapted Cox proportional hazards models. We propose estimation of all cause-specific cumulative hazards and transition probabilities in an extended illness-death model based on case-cohort sampling. As an example, we apply the methodology to infection with a specific pathogen using a large cohort from Spanish hospital data. The obtained results of the case-cohort design are compared with the results in the full cohort to investigate the performance of the proposed method. Copyright © 2016 John WileySons, Ltd.

Introduction: There are limited data about the use of antifungal agents (AF) in critically ill patients and treatment trends since the inclusion of the new generation AF. The use of these agents may have a significant influence on the development of new resistances. Methods: Observational prospective study of the systemic use of AF in patients admitted to Spanish intensive care units (ICU) participating in the ENVIN-HELICS register, from 2006 to 2010. The annual

Purpose To explore the impact of length-biased sampling on the evaluation of risk factors of nosocomial infections (NIs) in point-prevalence studies. Methods We used cohort data with full information including the exact date of the NI and mimicked an artificial 1-day prevalence study by picking a sample from this cohort study. Based on the cohort data, we studied the underlying multistate model which accounts for NI as an intermediate and discharge/death as competing events. Simple formulas are derived to display relationships between risk, hazard, and prevalence odds ratios. Results Due to length-biased sampling, long stay and thus sicker patients are more likely to be sampled. In addition, patients with NIs usually stay longer in hospital. We explored mechanisms that are—due to the design—hidden in prevalence data. In our example, we showed that prevalence odds ratios were usually less pronounced than risk odds ratios but more pronounced than hazard ratios. Conclusions Thus, to avoid misinterpretation, knowledge of the mechanisms from the underlying multistate model is essential for the interpretation of risk factors derived from point-prevalence data.

OBJECTIVES: We provide a case-cohort approach and show that a full competing risk analysis is feasible even in a reduced data set. Competing events for hospital-acquired infections are death or discharge from the hospital because they preclude the observation of such infections. STUDY DESIGN AND SETTING: Using surveillance data of 6,568 patient admissions (full cohort) from two Spanish intensive care units, we propose a case-cohort approach which uses only data from a random sample of the full cohort and all infected patients (the cases). We combine established methodology to study following measures: event-specific as well as subdistribution hazard ratios for all three events (infection, death, and discharge), cumulative hazards as well as incidence functions by risk factor, and also for all three events. RESULTS: Compared with the values from the full cohort, all measures are well approximated with the case-cohort design. For the event of interest (infection), event-specific and subdistribution hazards can be estimated with the full efficiency of the case-cohort design. So, standard errors are only slightly increased, whereas the precision of estimated hazards of the competing events is inflated according to the size of the subcohort. CONCLUSION: The case-cohort design provides an appropriate sampling design for studying hospital-acquired infections in a reduced data set. Potential effects of risk factors on the competing events (death and discharge) can be evaluated. This study was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft) (grant no WO 1746/1-2). M.W. received funding from Deutsche Forschungsgemeinschaft.

SummaryBackground: Sampling from a large cohort in order to derive a subsample that would be sufficient for statistical analysis is a frequently used method for handling large data sets in epidemiological studies with limited resources for exposure measurement. For clinical studies however, when interest is in the influence of a potential risk factor, cohort studies are often the first choice with all individuals entering the analysis.Objectives: Our aim is to close the gap between epidemiological and clinical studies with respect to design and power considerations. Schoenfeld’s formula for the number of events required for a Cox’ proportional hazards model is fundamental. Our objective is to compare the power of analyzing the full cohort and the power of a nested case- control and a case-cohort design.Methods: We compare formulas for power for sampling designs and cohort studies. In our data example we simultaneously apply a nested case-control design with a varying number of controls matched to each case, a case cohort design with varying subcohort size, a random subsample and a full cohort analysis. For each design we calculate the standard error for estimated regression coefficients and the mean number of distinct persons, for whom covariate information is required.Results: The formula for the power of a nested case-control design and the power of a case-cohort design is directly connected to the power of a cohort study using the well known Schoenfeld formula. The loss in precision of parameter estimates is relatively small compared to the saving in resources.Conclusions: Nested case-control and case-cohort studies, but not random subsamples yield an attractive alternative for analyzing clinical studies in the situation of a low event rate. Power calculations can be conducted straightforwardly to quantify the loss of power compared to the savings in the number of patients using a sampling design instead of analyzing the full cohort.

Summary Background Data validation is an essential aspect for the accuracy of a nosocomial infection surveillance registry. Aim To report the results of the first quality control programme in the national surveillance programme of intensive care unit (ICU)-acquired infection in Spain (ENVIN–HELICS registry). Method During 2008, of 13,824 records included in the database, 1500 (10.8%) registries from 20 ICUs were reviewed. These ICUs were selected at random and stratified according to the number of cases included in the registry. The proportion of infected patients, which was 9.6% [95% confidence interval (CI) 8.09–11.16], was maintained during the selection of cases for review. Two physicians were trained for the purpose of the study and undertook the review. Results Overall sensitivity, specificity and positive and negative predictive values of the ENVIN–HELICS registry for the identification of patients with any device-related infection acquired during their ICU stay were 86.0% (95% CI 80.0–92.0), 98.7% (95% CI 82.19–93.6), 87.9% (95% CI 82.19–93.6) and 98.5% (95% CI 97.8–99.2), respectively, with a kappa index of 0.85 (95% CI 0.79–0.92). Secondary bloodstream infection had the lowest sensitivity (59.3%), and intubation-associated pneumonia had the highest sensitivity (86.3%). Conclusion There was good correlation between data reported by the registrars and data validated by auditors, confirming the reliability of the ENVIN–HELICS registry.