Emmanuel Montassier

Recent changes in bacteremia in patients with cancer: a systematic review of epidemiology and antibiotic resistance.

Bacteremia remains a major cause of life-threatening complication in patients with cancer. Significant changes in the spectrum of microorganisms isolated from blood culture have been reported in cancer patients over the past years. The aim of our systematic review was to inventory the recent trends in epidemiology and antibiotic resistance of microorganisms causing bacteremia in cancer patients. Data for this review was identified by searches of Medline, Scopus and Cochrane Library for indexed articles and abstracts published in English since 2008. The principal search terms were: “antimicrobial resistance”, “bacteremia”, “bacterial epidemiology”, “bloodstream infection”, “cancer patients”, “carbapenem resistance”, “Escherichia coli resistance”, “extended-spectrum β-lactamase producing E. coli”, “febrile neutropenia”, “fluoroquinolone resistance”, “neutropenic cancer patient”, “vancomycin-resistant Enterococcus”, and “multidrug resistance”. Boolean operators (NOT, AND, OR) were also used in succession to narrow and widen the search. Altogether, 27 articles were selected to be analyzed in the review. We found that Gram-negative bacteria were the most frequent pathogen isolated, particularly in studies with minimal use of antibiotic prophylaxis. Another important trend is the extensive emergence of antimicrobial-resistant strains associated with increased risk of morbidity, mortality and cost. This increasing incidence of antibiotic resistance has been reported in Gram-negative bacteria as well as in Gram-positive bacteria. This exhaustive review, reporting the recent findings in epidemiology and antibiotic resistance of bacteremia in cancer patients, highlights the necessity of local continuous surveillance of bacteremia and stringent enforcement of antibiotic stewardship programs in cancer patients.

Systematic review: the role of the gut microbiota in chemotherapy- or radiation-induced gastrointestinal mucositis – current evidence and potential clinical applications

Gastrointestinal mucositis is defined as inflammation and/or ulcers of the gastrointestinal tract occurring as a complication of chemotherapy and radiation therapy, and affects about 50% of all cancer patients.

Chemotherapy-driven dysbiosis in the intestinal microbiome

Chemotherapy is commonly used as myeloablative conditioning treatment to prepare patients for haematopoietic stem cell transplantation (HSCT). Chemotherapy leads to several side effects, with gastrointestinal (GI) mucositis being one of the most frequent. Current models of GI mucositis pathophysiology are generally silent on the role of the intestinal microbiome.

Pretreatment gut microbiome predicts chemotherapy-related bloodstream infection.

BackgroundBacteremia, or bloodstream infection (BSI), is a leading cause of death among patients with certain types of cancer. A previous study reported that intestinal domination, defined as occupation of at least 30 % of the microbiota by a single bacterial taxon, is associated with BSI in patients undergoing allo-HSCT. However, the impact of the intestinal microbiome before treatment initiation on the risk of subsequent BSI remains unclear. Our objective was to characterize the fecal microbiome collected before treatment to identify microbes that predict the risk of BSI.MethodsWe sampled 28 patients with non-Hodgkin lymphoma undergoing allogeneic hematopoietic stem cell transplantation (HSCT) prior to administration of chemotherapy and characterized 16S ribosomal RNA genes using high-throughput DNA sequencing. We quantified bacterial taxa and used techniques from machine learning to identify microbial biomarkers that predicted subsequent BSI.ResultsWe found that patients who developed subsequent BSI exhibited decreased overall diversity and decreased abundance of taxa including Barnesiellaceae, Coriobacteriaceae, Faecalibacterium, Christensenella, Dehalobacterium, Desulfovibrio, and Sutterella. Using machine-learning methods, we developed a BSI risk index capable of predicting BSI incidence with a sensitivity of 90 % at a specificity of 90 % based only on the pretreatment fecal microbiome.ConclusionsThese results suggest that the gut microbiota can identify high-risk patients before HSCT and that manipulation of the gut microbiota for prevention of BSI in high-risk patients may be a useful direction for future research. This approach may inspire the development of similar microbiome-based diagnostic and prognostic models in other diseases.

NINJA-OPS: Fast Accurate Marker Gene Alignment Using Concatenated Ribosomes

The explosion of bioinformatics technologies in the form of next generation sequencing (NGS) has facilitated a massive influx of genomics data in the form of short reads. Short read mapping is therefore a fundamental component of next generation sequencing pipelines which routinely match these short reads against reference genomes for contig assembly. However, such techniques have seldom been applied to microbial marker gene sequencing studies, which have mostly relied on novel heuristic approaches. We propose NINJA Is Not Just Another OTU-Picking Solution (NINJA-OPS, or NINJA for short), a fast and highly accurate novel method enabling reference-based marker gene matching (picking Operational Taxonomic Units, or OTUs). NINJA takes advantage of the Burrows-Wheeler (BW) alignment using an artificial reference chromosome composed of concatenated reference sequences, the “concatesome,” as the BW input. Other features include automatic support for paired-end reads with arbitrary insert sizes. NINJA is also free and open source and implements several pre-filtering methods that elicit substantial speedup when coupled with existing tools. We applied NINJA to several published microbiome studies, obtaining accuracy similar to or better than previous reference-based OTU-picking methods while achieving an order of magnitude or more speedup and using a fraction of the memory footprint. NINJA is a complete pipeline that takes a FASTA-formatted input file and outputs a QIIME-formatted taxonomy-annotated BIOM file for an entire MiSeq run of human gut microbiome 16S genes in under 10 minutes on a dual-core laptop.

Gut microbiome predictors of treatment response and recurrence in primary Clostridium difficile infection.

Clostridium difficile infection (CDI) may not respond to initial therapy and frequently recurs, but predictors of response and recurrence are inconsistent. The impact of specific alterations in the gut microbiota determining treatment response and recurrence in patients with CDI is unknown.

Human intestinal microbiota gene risk factors for antibiotic-associated diarrhea: perspectives for prevention. Risk factors for antibiotic-associated diarrhea.

Antibiotic-associated diarrhea (AAD) is associated with altered intestinal microflora and other symptoms that may lead to possibly death. In critically ill patients, diarrhea increases rates of morbimortality. Assessing diarrhea risks is thus important for clinicians. For this reason, we conducted a hypothesis-generating study focused on AAD to provide insight into methods of prevention. We evaluated the hypothesis of predisposing factors within the resident intestinal microbiota in a cohort of outpatients receiving antibiotherapy. Among the pool of tested variables, only those related to bacterial 16S rRNA genes were found to be relevant. Complex statistical analyses provided further information: amid the bacteria 16S rRNA genes, eight were determined to be essential for diarrhea predisposition and characterized from the most important to the least. Using these markers, AAD risk could be estimated with an error of 2%. This molecular analysis offers new perspectives for clinical applications at the level of prevention.

Systematic review: human gut dysbiosis induced by non‐antibiotic prescription medications

Global prescription drug use has been increasing continuously for decades. The gut microbiome, a key contributor to health status, can be altered by prescription drug use, as antibiotics have been repeatedly described to have both short‐term and long‐standing effects on the intestinal microbiome.

Relationship between hospital antibiotic use and quinolone resistance in Escherichia coli

BACKGROUND The relationship between the hospital use of various classes of antibiotics and resistance of Escherichia coli to quinolones remains debated. Our aim was to study the relationship between the hospital use of 16 classes of antibacterial agents and the incidence of quinolone-resistant E. coli isolates. METHODS Antibiotic use and resistance data were collected from 36 hospitals. Incident rate ratios (IRR) were assessed using negative binomial regression. RESULTS The incidence of quinolone-resistant isolates was independently associated with the consumption of tetracyclines (IRR 1.139, 95% CI 1.030-1.259), first- and second-generation cephalosporins (IRR 1.007, 95% CI 1.002-1.013), third-generation cephalosporins (IRR 1.029, 95% CI 1.010-1.048), and quinolones (IRR 1.007, 95% CI 1.000-1.014). These associations were independent from the type of patient served. CONCLUSIONS The level of hospital use of quinolones influences the incidence of quinolone resistance in E. coli hospital isolates. The consumption of two other classes of antibiotics, cephalosporins and tetracyclines, is also associated with quinolone resistance.

Base excess is an accurate predictor of elevated lactate in ED septic patients

BACKGROUND Prior studies showed that lactate is a useful marker in sepsis. However, lactate is often not routinely drawn or rapidly available in the emergency department (ED). OBJECTIVE The study aimed to determine if base excess (BE), widely and rapidly available in the ED, could be used as a surrogate marker for elevated lactate in ED septic patients. METHODS This was a prospective and observational cohort study. From March 2009 to March 2010, consecutive patients 18 years or older who presented to the ED with a suspected severe sepsis were enrolled in the study. Lactate and BE measurements were performed. We defined, a priori, a clinically significant lactate to be greater than 3 mmol/L and BE less than -4 mmol/L. RESULTS A total of 224 patients were enrolled in the study. The average BE was -4.5 mmol/L (SD, 4.9) and the average lactate was 3.5 mmol/L (SD, 2.9). The sensitivity of a BE less than -4 mmol/L in predicting elevated lactate greater than 3 mmol/L was 91.1% (95% confidence interval, 85.5%-96.6%) and the specificity was 88.6% (95% confidence interval, 83.0%-94.2%). The area under the curve was 0.95. CONCLUSION Base excess is an accurate marker for the prediction of elevated lactate in the ED. The measurement of BE, obtained in a few minutes in the ED, provides a secure and quick method, similar to the electrocardiogram at triage for patients with chest pain, to determine the patients with sepsis who need an early aggressive resuscitation.