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BackgroundCeftriaxone-resistant (CRO-R) Escherichia coli bloodstream infections (BSIs) are common.MethodsThis is a prospective cohort of patients with E coli BSI at 14 United States hospitals between November 2020 and April 2021. For each patient with a CRO-R E coli BSI enrolled, the next consecutive patient with a ceftriaxone-susceptible (CRO-S) E coli BSI was included. Primary outcome was desirability of outcome ranking (DOOR) at day 30, with 50% probability of worse outcomes in the CRO-R group as the null hypothesis. Inverse probability weighting (IPW) was used to reduce confounding.ResultsNotable differences between patients infected with CRO-R and CRO-S E coli BSI included the proportion with Pitt bacteremia score ≥4 (23% vs 15%, P = .079) and the median time to active antibiotic therapy (12 hours [interquartile range {IQR}, 1-35 hours] vs 1 hour [IQR, 0-6 hours]; P < .001). Unadjusted DOOR analyses indicated a 58% probability (95% confidence interval [CI], 52%-63%) for a worse clinical outcome in CRO-R versus CRO-S BSI. In the IPW-adjusted cohort, no difference was observed (54% [95% CI, 47%-61%]). Secondary outcomes included unadjusted and adjusted differences in the proportion of 30-day mortality between CRO-R and CRO-S BSIs (-5.3% [95% CI, -10.3% to -.4%] and -1.8 [95% CI, -6.7% to 3.2%], respectively), postculture median length of stay (8 days [IQR, 5-13 days] vs 6 days [IQR, 4-9 days]; P < .001), and incident admission to a long-term care facility (22% vs 12%, P = .045).ConclusionsPatients with CRO-R E coli BSI generally have poorer outcomes compared to patients infected with CRO-S E coli BSI, even after adjusting for important confounders.

We investigated the ability of several recent clinical viridans group streptococci (VGS) bloodstream isolates (Streptococcus mitis/S. oralis subgroup) from daptomycin (DAP)-naive patients to develop DAP resistance in vitro All strains rapidly developed high-level and stable DAP resistance. Substitutions in two enzymes involved in the cardiolipin biosynthesis pathway were identified, i.e., CdsA (phosphatidate cytidylyltransferase) and PgsA (CDP-diacylglycerol-glycerol-3-phosphate-3-phosphatidyltransferase). These mutations were associated with complete disappearance of phosphatidylglycerol and cardiolipin from cell membranes. DAP interactions with the cell membrane differed in isolates with PgsA versus CdsA substitutions.

Background Non– Enterococcus faecium, non– E. faecalis (NFF) enterococci are a heterogeneous group of clinically pathogenic enterococci that include species with intrinsic low-level vancomycin resistance. Patients with cancer are at increased risk for bacteremia with NFF enterococci, but their clinical and molecular epidemiology have not been extensively described. Methods We conducted a retrospective review of all patients (n = 70) with NFF bacteremia from 2016 to 2022 at a major cancer center. The main outcomes assessed were 30-day mortality, microbiological failure (positive blood cultures for ≥4 days), and recurrence of bacteremia (positive blood culture <14 days after clearance). Whole-genome sequencing was performed on all available NFF (n = 65). Results Patients with hematological malignancies made up 56% of the cohort (77% had leukemia). The majority of solid malignancies (87%) were gastrointestinal in origin. The majority of infections (83%) originated from an intra-abdominal source. The most common NFF species were E. gallinarum (50%) and E. casseliflavus (30%). Most (61%) patients received combination therapy. Bacteremia recurred in 4.3% of patients, there was a 30-day mortality of 23%, and 4.3% had microbiological failure. E. gallinarum and E. casseliflavus isolates were genetically diverse with no spatiotemporal clustering to suggest a single strain. Frequencies of ampicillin resistance (4.3%) and daptomycin resistance (1.9%) were low. Patients with hematologic malignancy had infections with NFF enterococci that harbored more resistance genes than patients with solid malignancy (P =.005). Conclusions NFF bacteremia is caused by a heterogeneous population of isolates and is associated with significant mortality. Hematological malignancy is an important risk factor for infection with NFF resistant to multiple antibiotics. [ABSTRACT FROM AUTHOR]

Acute myeloid leukemia (AML) is associated with poor survival. While clinical prognostic factors of survival have been identified, the contribution of patient-reported symptoms has only received marginal attention. Fatigue is one of the most commonly reported symptoms of AML. There is some evidence that fatigue is associated with shorter survival in hematological malignancies. However, the prognostic effects of fatigue in a homogenous cohort of patients with untreated AML has not been tested. We here report results of a prospective study on the prognostic value of patient-reported fatigue prior to onset of treatment, for 2-year survival in 94 AML patients. Cox regression models controlling for demographic and clinical factors showed that those with severe fatigue (22%) had decreased survival rates (Hr = 2.255, 95% CI = 1.16-5.60, p = 0.019). Further exploration showed that fatigue was associated with increased plasma concentrations of IL-6 and TNF-α, but not with demographic or disease-related factors. In conclusion, we here show for the first time that the experience of severe fatigue prior to remission induction chemotherapy (IC) is prognostic for shorter survival in patients with AML of all ages. These findings point to the importance of interventions aimed at relieving fatigue especially before or in the early phases of treatment in order to improve survival.

BackgroundThe Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development.ResultsIn this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available.ConclusionsOur method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.

BackgroundUnderstanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy.MethodsThe stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses.ResultsOur cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002).ConclusionsThese data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in research and future clinical practice.

After publication of the original article [1] it was brought to our attention that author Samuel A. Shelburne was incorrectly included as Samuel A. Shelbourne. The correct spelling of the name is included in the author list of this erratum and has been updated in the original article.