Daily Sepsis Research Analysis

OBJECTIVE: Early antibiotics are considered critical for bacterial sepsis treatment, although the benefit of this early timing may differ by the presence of shock. Little evidence exists from low- or middle-income settings. In patients referred from community hospitals to a tertiary center, we tested whether pre-referral antibiotic administration is associated with 28-day survival in sepsis, and whether this association differs by the presence of shock. DESIGN: Secondary analysis of a prospective cohort study that enrolled patients from 2013 to 2017 with a primary diagnosis of infection made by an attending physician and at least three Surviving Sepsis Campaign criteria for sepsis. SETTING: Tertiary care hospital in northeastern Thailand. PATIENTS: A total of 2593 adults with sepsis defined by primary diagnosis of infection and modified Sequential Organ Failure Assessment score greater than or equal to 2 who were referred from community hospitals. INTERVENTIONS: Antibiotics administered at the referring community hospital. MEASUREMENTS AND MAIN RESULTS: The median age was 59 years (interquartile range 44-72), 2233 (86.1%) were transferred the same day as initial presentation, and 1897 (73.2%) received antibiotics prior to referral. Blood cultures grew bacteria in 313 (12.1%). Twenty-eight-day mortality was 18.9%. In the propensity score-matched cohort (n = 722 sepsis without shock, n = 244 septic shock), shock modified the association between pre-referral antibiotics and death (interaction p = 0.001). In patients with septic shock, pre-referral antibiotics were associated with lower hazard of death (hazard ratio [HR], 0.38; 95% CI, 0.19-0.75) but in patients without shock there was no association with hazard of death (HR, 1.36; 95% CI, 0.96-1.92). CONCLUSIONS: In rural Thailand, antibiotic administration prior to referral was associated with lower hazard of death in patients with septic shock. Our findings extend to a resource-limited setting evidence supporting the benefit of early antibiotic administration in septic patients with shock.

BACKGROUND: Sepsis is a life-threatening condition, particularly in intensive care unit (ICU) patients with chronic kidney disease (CKD). However, accurate prediction of in-hospital mortality in this high-risk population remains a clinical challenge. This study aimed to develop and validate machine learning (ML) models to predict in-hospital mortality among ICU patients with sepsis and CKD. METHODS: Patients diagnosed with both sepsis and CKD were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Feature selection was performed using the Boruta algorithm. Multiple ML models were developed, including logistic regression (LR), decision tree, k-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), neural network (NN), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost), along with the Sequential Organ Failure Assessment (SOFA) score for comparison. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and average precision (AP). The best-performing model was externally validated in an independent cohort from the eICU Collaborative Research Database (eICU-CRD) and further interpreted using Shapley Additive Explanations (SHAP). RESULTS: A total of 4,686 ICU patients with sepsis and CKD were included in the development cohort. Among the models, XGBoost demonstrated the best performance with an AUC of 0.911, AP of 0.771, specificity of 96%, and sensitivity of 62%. In the external validation cohort of 3,718 patients, XGBoost also achieved excellent predictive performance with an AUC of 0.855. Model calibration and decision curve analysis confirmed its clinical utility. The top 20 predictors were visualized and ranked based on SHAP values. CONCLUSIONS: Machine learning models, particularly XGBoost, can accurately predict in-hospital mortality in ICU patients with sepsis and CKD. These models may assist clinicians in risk stratification and decision-making for this vulnerable patient population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-025-11949-5.

Sepsis-induced endothelial dysfunction, marked by degradation of the endothelial glycocalyx and activation of endothelial cells, plays a pivotal role in the progression to organ failure and mortality. Biomarkers reflecting glycocalyx damage have demonstrated prognostic potential; however, their associations with clinical outcomes remain variable. We systematically evaluated the prognostic utility of glycocalyx-associated biomarkers (syndecan-1, heparan sulfate, hyaluronate) and the endothelial activation marker endocan in sepsis with respect to mortality, organ dysfunction, and inter-study heterogeneity. We included 23 studies through May 2025 encompassing 4529 patients with sepsis. Two independent reviewers extracted data using standardized protocols, including biomarker concentrations and clinical outcomes such as mortality, multiple organ dysfunction syndrome, and respiratory failure. Risk of bias was assessed using the NOS, with 75% of studies rated as low risk. Elevated syndecan-1 was significantly associated with increased mortality (nine studies, n = 2167; OR 2.04, 95% CI, 1.66-2.51; p < 0.05; I² = 84%). Similarly, elevated endocan predicted mortality with a stronger effect size (six studies, n = 435; OR 5.06, 95% CI, 2.52-10.18; p < 0.05) and low heterogeneity. In contrast, syndecan-1 levels were not significantly associated with multiple organ dysfunction syndrome (OR 2.35, 95% CI, 0.93-5.94; I² = 94%) or respiratory failure (OR 1.05, 95% CI, 0.27-4.02; I² = 91%). The majority of studies were ICU-based (78.3%), primarily adult cohorts (91.3%), with syndecan-1 the most commonly assessed biomarker (65.2%). Syndecan-1 and endocan serve as prognostic biomarkers for mortality in sepsis, with endocan demonstrating greater inter-study consistency.