Daily Sepsis Research Analysis

Preterm infants (<37 weeks' gestation) are commonly given broad-spectrum antibiotics due to their risk of severe conditions like necrotising enterocolitis and sepsis. However, antibiotics can disrupt early-life gut microbiota development, potentially impairing gut immunity and colonisation resistance. Probiotics (e.g., certain Bifidobacterium strains) may help restore a healthy gut microbiota. In this study, we investigated the effects of probiotics and antibiotics on the gut microbiome and resistome in two unique cohorts of 34 very-low-birth-weight, human-milk-fed preterm infants - one of which received probiotics. Within each group, some infants received antibiotics (benzylpenicillin and/or gentamicin), while others did not. Using shotgun metagenomic sequencing on 92 longitudinal faecal samples, we reconstructed >300 metagenome-assembled genomes and obtained ~90 isolate genomes via targeted culturomics, allowing strain-level analysis. We also assessed ex vivo horizontal gene transfer (HGT) capacity of multidrug-resistant (MDR) Enterococcus using neonatal gut models. Here we show that probiotic supplementation significantly reduced antibiotic resistance gene prevalence, MDR pathogen load, and restored typical early-life microbiota profile. However, persistent MDR pathogens like Enterococcus, with high HGT potential, underscore the need for continued surveillance. Our findings underscore the complex interplay between antibiotics, probiotics, and HGT in shaping the neonatal microbiome and support further research into probiotics for antimicrobial stewardship in preterm populations.

UNLABELLED: Bloodstream infections cause great mortality and morbidity. Real-time microbiological information has been shown to have an impact on prognosis when combined with an antimicrobial stewardship (AMS) program. dRAST is a rapid commercial antimicrobial susceptibility testing (AST) system based on real-time microscopy. The primary outcome of this trial was to compare positive blood culture's time-to-result (TTR), defined as time from Gram stain to AST report, between dRAST and our standard of care in a real-life clinical setting. Secondary outcomes included microbiological concordance, time to antimicrobial stewardship recommendation (TAMS), and time to change antimicrobial prescription (TCAP) in agreement with AMS recommendation. We performed a prospective, open-label, randomized (1:1) diagnostic clinical trial comparing dRAST and our standard of care commercial MicroScan broth microdilution (MBMD) (MicroScan-Walkaway, Beckman-Coulter, USA) in 277 hospitalized patients with bacteremia. Categorical and continuous variables were compared by chi-squared and Mann-Whitney IMPORTANCE: This study addresses a critical challenge in infectious diseases management by evaluating the efficacy of the dRAST system, a rapid antimicrobial susceptibility testing method, in guiding antimicrobial stewardship programs for patients with bloodstream infections. A randomized diagnostic trial was designed to address this objective. Our findings show that the use of dRAST significantly reduces time-to-result, time to antimicrobial stewardship recommendations, and time to appropriate therapy changes, particularly in critically ill patients. These results underline the potential of rapid diagnostic technologies to optimize patient outcomes and enhance antimicrobial stewardship efforts in real-world clinical settings.

BACKGROUND: Sepsis is a life-threatening condition characterized by dysregulated immune responses and metabolic disturbances. The albumin-to-neutrophil-lymphocyte ratio (ANLR) is a novel composite biomarker integrating nutritional and inflammatory status. However, its prognostic significance in sepsis remains unclear. This study aims to evaluate the association between ANLR and mortality in sepsis patients using both traditional statistical methods and machine learning models. METHODS: A retrospective cohort study was conducted using the MIMIC-IV (v3.1) database. In this study, 6,288 patients diagnosed with sepsis and admitted to the ICU were analyzed, with participants stratified into quartiles according to their ANLR measurements. The primary endpoint was set as 30-day mortality, while 90-day mortality served as a secondary outcome. The association between ANLR and mortality was investigated through Kaplan-Meier survival analysis, Cox regression, and restricted cubic spline (RCS) modeling. Furthermore, the contribution of ANLR relative to other predictors was evaluated by developing machine learning models, with SHapley Additive exPlanations (SHAP) employed to determine variable importance. RESULTS: A higher ANLR was independently associated with improved survival. In the fully adjusted Cox model, elevated ANLR predicted a lower risk of mortality at 30 days (HR 0.68, 95% CI 0.59-0.79, p < 0.001) and at 90 days (HR 0.85, 95% CI 0.76-0.94, p = 0.002). Machine learning analysis identified ANLR as the second most important variable influencing sepsis mortality. ANLR demonstrated superior predictive ability (AUC 0.66) compared to traditional markers, including SOFA, NLR, and albumin. CONCLUSIONS: ANLR is a robust and independent predictor of sepsis-related mortality, outperforming conventional biomarkers. Incorporating ANLR into routine clinical workflows could improve risk assessment and facilitate individualized treatment strategies for patients with severe sepsis. Further prospective studies are needed to validate these findings and explore potential therapeutic implications.