Dysbiosis of bifidobacteria and Clostridium cluster XIVa in the cystic fibrosis fecal microbiota.

BACKGROUND: Recurrent antimicrobial interventions and disease-related intestinal dysfunction are suspected to contribute to the dysbiosis of the gastrointestinal microbial ecosystem in patients with cystic fibrosis (CF). The present study set out to detect and identify microbial discriminants in the gut microbiota composition that are associated with CF-related intestinal dysbiosis. METHODS: An in-depth description of CF-associated gut dysbiosis was obtained by screening denaturing gradient gel electrophoresis (DGGE) fingerprints for potentially discriminating bacterial species, and quantification by means of real-time PCR analyses using group-specific primers. RESULTS: A total of 8 DGGE band-classes assigned to the genus Bifidobacterium (n=3), and members of Clostridium clusters XIVa (n=3) and IV (n=2), were significantly (p<0.05) underrepresented in samples of patients with CF. Real-time PCR analyses confirmed a significantly lower abundance and temporal stability of bifidobacteria and Clostridium cluster XIVa in the fecal microbiota of patients with CF. CONCLUSION: This study is the first to report specific microbial determinants of dysbiosis in patients with CF.

A simple unified approach for estimating natural direct and indirect effects.

An important problem within both epidemiology and many social sciences is to break down the effect of a given treatment into different causal pathways and to quantify the importance of each pathway. Formal mediation analysis based on counterfactuals is a key tool when addressing this problem. During the last decade, the theoretical framework for mediation analysis has been greatly extended to enable the use of arbitrary statistical models for outcome and mediator. However, the researcher attempting to use these techniques in practice will often find implementation a daunting task, as it tends to require special statistical programming. In this paper, the authors introduce a simple procedure based on marginal structural models that directly parameterize the natural direct and indirect effects of interest. It tends to produce more parsimonious results than current techniques, greatly simplifies testing for the presence of a direct or an indirect effect, and has the advantage that it can be conducted in standard software. However, its simplicity comes at the price of relying on correct specification of models for the distribution of mediator (and exposure) and accepting some loss of precision compared with more complex methods. Web Appendixes 1 and 2, which are posted on the Journal's Web site (http://aje.oupjournals.org/), contain implementation examples in SAS software (SAS Institute, Inc., Cary, North Carolina) and R language (R Foundation for Statistical Computing, Vienna, Austria).

On model selection and model misspecification in causal inference.

Standard variable selection procedures, primarily developed for the construction of outcome prediction models, are routinely applied when assessing exposure effects in observational studies. We argue that this tradition is sub-optimal and prone to yield bias in exposure effect estimators as well as their corresponding uncertainty estimators. We weigh the pros and cons of confounder-selection procedures and propose a procedure directly targeting the quality of the exposure effect estimator. We further demonstrate that certain strategies for inferring causal effects have the desirable features (a) of producing (approximately) valid confidence intervals, even when the confounder-selection process is ignored, and (b) of being robust against certain forms of misspecification of the association of confounders with both exposure and outcome.

Perceptions of appropriateness of care among European and Israeli intensive care unit nurses and physicians.

CONTEXT: Clinicians in intensive care units (ICUs) who perceive the care they provide as inappropriate experience moral distress and are at risk for burnout. This situation may jeopardize patient quality of care and increase staff turnover. OBJECTIVE: To determine the prevalence of perceived inappropriateness of care among ICU clinicians and to identify patient-related situations, personal characteristics, and work-related characteristics associated with perceived inappropriateness of care. DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional evaluation on May 11, 2010, of 82 adult ICUs in 9 European countries and Israel. Participants were 1953 ICU nurses and physicians providing bedside care. MAIN OUTCOME MEASURE: Perceived inappropriateness of care, defined as a specific patient-care situation in which the clinician acts in a manner contrary to his or her personal and professional beliefs, as assessed using a questionnaire designed for the study. RESULTS: Of 1651 respondents (median response rate, 93% overall; interquartile range, 82%-100% [medians 93% among nurses and 100% among physicians]), perceived inappropriateness of care in at least 1 patient was reported by 439 clinicians overall (27%; 95% CI, 24%-29%), 300 of 1218 were nurses (25%), 132 of 407 were physicians (32%), and 26 had missing answers describing job title. Of these 439 individuals, 397 reported 445 situations associated with perceived inappropriateness of care. The most common reports were perceived disproportionate care (290 situations [65%; 95% CI, 58%-73%], of which "too much care" was reported in 89% of situations, followed by "other patients would benefit more" (168 situations [38%; 95% CI, 32%-43%]). Independently associated with perceived inappropriateness of care rates both among nurses and physicians were symptom control decisions directed by physicians only (odds ratio [OR], 1.73; 95% CI, 1.17-2.56; P = .006); involvement of nurses in end-of-life decision making (OR, 0.76; 95% CI, 0.60-0.96; P = .02); good collaboration between nurses and physicians (OR, 0.72; 95% CI, 0.56-0.92; P = .009); and freedom to decide how to perform work-related tasks (OR, 0.72; 95% CI, 0.59-0.89; P = .002); while a high perceived workload was significantly associated among nurses only (OR, 1.49; 95% CI, 1.07-2.06; P = .02). Perceived inappropriateness of care was independently associated with higher intent to leave a job (OR, 1.65; 95% CI, 1.04-2.63; P = .03). In the subset of 69 ICUs for which patient data could be linked, clinicians reported received inappropriateness of care in 207 patients, representing 23% (95% CI, 20%-27%) of 883 ICU beds. CONCLUSION: Among a group of European and Israeli ICU clinicians, perceptions of inappropriate care were frequently reported and were inversely associated with factors indicating good teamwork.

Cross-sectional and longitudinal comparisons of the predominant fecal microbiota compositions of a group of pediatric patients with cystic fibrosis and their healthy siblings.

Although only poorly documented, it can be assumed that intensive antibiotic treatments of chronic lung infections in patients with cystic fibrosis (CF) also affect the diversity and metabolic functioning of the gastrointestinal microbiota and potentially lead to a state of dysbiosis. A better knowledge of the differences in gut microbiota composition and stability between patients with CF and healthy subjects could lead to optimization of current antibiotic therapies and/or development of add-on therapies. Using conventional culturing and population fingerprinting by denaturing gradient gel electrophoresis (DGGE) of 16S rRNA amplicons, we compared the predominant fecal microbiota of 21 patients with CF and 24 healthy siblings in a cross-sectional study. General medium counts, as well as counts on media specific for lactic acid bacteria, clostridia, Bifidobacterium spp., Veillonella spp., and Bacteroides-Prevotella spp., were consistently higher in sibling samples than in CF samples, whereas the reverse was found for enterobacterial counts. DGGE fingerprinting uncovered large intersubject variations in both study groups. On the other hand, the cross-sectional data indicated that the predominant fecal microbiota of patients and siblings had comparable species richness. In addition, a longitudinal study was performed on 7 or 8 consecutive samples collected over a 2-year period from two patients and their respective siblings. For these samples, DGGE profiling indicated an overall trend toward lower temporal stability and lower species richness in the predominant fecal CF microbiota. The observed compositional and dynamic perturbations provide the first evidence of a general dysbiosis in children with CF compared to their siblings.

Attributable mortality of ventilator-associated pneumonia: a reappraisal using causal analysis.

RATIONALE: Measuring the attributable mortality of ventilator-associated pneumonia (VAP) is challenging and prone to different forms of bias. Studies addressing this issue have produced variable and controversial results. OBJECTIVES: We estimate the attributable mortality of VAP in a large multicenter cohort using statistical methods from the field of causal inference. METHODS: Patients (n = 4,479) from the longitudinal prospective (1997-2008) French multicenter Outcomerea database were included if they stayed in the intensive care unit (ICU) for at least 2 days and received mechanical ventilation (MV) within 48 hours after ICU admission. A competing risk survival analysis, treating ICU discharge as a competing risk for ICU mortality, was conducted using a marginal structural modeling approach to adjust for time-varying confounding by disease severity. MEASUREMENTS AND MAIN RESULTS: Six hundred eighty-five (15.3%) patients acquired at least one episode of VAP. We estimated that 4.4% (95% confidence interval, 1.6-7.0%) of the deaths in the ICU on Day 30 and 5.9% (95% confidence interval, 2.5-9.1%) on Day 60 are attributable to VAP. With an observed ICU mortality of 23.3% on Day 30 and 25.6% on Day 60, this corresponds to an ICU mortality attributable to VAP of about 1% on Day 30 and 1.5% on Day 60. CONCLUSIONS: Our study on the attributable mortality of VAP is the first that simultaneously accounts for the time of acquiring VAP, informative loss to follow-up after ICU discharge, and the existence of complex feedback relations between VAP and the evolution of disease severity. In contrast to the majority of previous reports, we detected a relatively limited attributable ICU mortality of VAP.

Adjusting for time-varying confounding in the subdistribution analysis of a competing risk.

Despite decades of research in the medical literature, assessment of the attributable mortality due to nosocomial infections in the intensive care unit (ICU) remains controversial, with different studies describing effect estimates ranging from being neutral to extremely risk increasing. Interpretation of study results is further hindered by inappropriate adjustment (a) for censoring of the survival time by discharge from the ICU, and (b) for time-dependent confounders on the causal path from infection to mortality. In previous work (Vansteelandt et al. Biostatistics 10:46-59), we have accommodated this through inverse probability of treatment and censoring weighting. Because censoring due to discharge from the ICU is so intimately connected with a patient's health condition, the ensuing inverse weighting analyses suffer from influential weights and rely heavily on the assumption that one has measured all common risk factors of ICU discharge and mortality. In this paper, we consider ICU discharge as a competing risk in the sense that we aim to infer the risk of 'ICU mortality' over time that would be observed if nosocomial infections could be prevented for the entire study population. For this purpose we develop marginal structural subdistribution hazard models with accompanying estimation methods. In contrast to subdistribution hazard models with time-varying covariates, the proposed approach (a) can accommodate high-dimensional confounders, (b) avoids regression adjustment for post-infection measurements and thereby so-called collider-stratification bias, and (c) results in a well-defined model for the cumulative incidence function. The methods are used to quantify the causal effect of nosocomial pneumonia on ICU mortality using data from the National Surveillance Study of Nosocomial Infections in ICU's (Belgium).

Determinants and impact of multidrug antibiotic resistance in pathogens causing ventilator-associated-pneumonia.

INTRODUCTION: The idea that multidrug resistance (MDR) to antibiotics in pathogens causing ventilator-associated pneumonia (VAP) is an independent risk factor for adverse outcome is still debated. We aimed to identify the determinants of MDR versus non-MDR microbial aetiology in VAP and assessed whether MDR versus non-MDR VAP was independently associated with increased 30-day mortality. METHODS: We performed a retrospective analysis of a prospectively registered cohort of adult patients with microbiologically confirmed VAP, diagnosed at a university hospital intensive care unit during a three-year period. Determinants of MDR as compared with non-MDR microbial aetiology and impact of MDR versus non-MDR aetiology on mortality were investigated using multivariate logistic and competing risk regression analysis. RESULTS: MDR pathogens were involved in 52 of 192 episodes of VAP (27%): methicillin-resistant Staphylococcus aureus in 12 (6%), extended-spectrum beta-lactamase producing Enterobacteriaceae in 28 (15%), MDR Pseudomonas aeruginosa and other non-fermenting pathogens in 12 (6%). Multivariable logistic regression identified the Charlson index of comorbidity (odds ratio (OR) = 1.38, 95% confidence interval (CI) = 1.08 to 1.75, p = 0.01) and previous exposure to more than two different antibiotic classes (OR = 5.11, 95% CI = 1.38 to 18.89, p = 0.01) as predictors of MDR aetiology. Thirty-day mortality after VAP diagnosis caused by MDR versus non-MDR was 37% and 20% (p = 0.02), respectively. A multivariate competing risk regression analysis showed that renal replacement therapy before VAP (standardised hazard ratio (SHR) = 2.69, 95% CI = 1.47 to 4.94, p = 0.01), the Charlson index of comorbidity (SHR = 1.21, 95% CI = 1.03 to 1.41, p = 0.03) and septic shock on admission to the intensive care unit (SHR = 1.86, 95% CI = 1.03 to 3.35, p = 0.03), but not MDR aetiology of VAP, were independent predictors of mortality. CONCLUSIONS: The risk of MDR pathogens causing VAP was mainly determined by comorbidity and prior exposure to more than two antibiotics. The increased mortality of VAP caused by MDR as compared with non-MDR pathogens was explained by more severe comorbidity and organ failure before VAP.