Daily Sepsis Research Analysis

BACKGROUND: Sepsis accounts for approximately a third of global mortality, and significant morbidity and economic burden. Whilst the current Sepsis-3 definition has augmented patient identification, supportive care and survival, a lack of clinically relevant animal models has limited our understanding of sepsis disease dynamics over time. Specifically, key knowledge gaps in chronic pathology underpinning the mechanisms leading to organ dysfunction and mortality rates of sepsis survivors have hindered the development of effective therapeutics. Therefore, we developed a new mouse model of abdominal gram-negative sepsis that adheres to Sepsis-3 definitions and expert-led consensus criteria for preclinical sepsis models. RESULTS: We tested multiple live strains of Escherichia coli with only clinical isolates causing lethality. Subsequent standard care including broad-spectrum antibiotics and fluid resuscitation reduced the mortality rate to approximately 24 ± 9.3% (SEM), corroborating clinical observations. Early sepsis disease 12 h post-infection was characterized by cytokine storm, with concentrations of IFN-γ, CCL2, IL-6, IL-17A, IL-1α, IL-10 and M-CSF significantly elevated in multiple tissues up to 7 days post-infection when mice had recovered from objective clinical measures of disease. Furthermore, we observed histological evidence of organ dysfunction in the liver, spleen and kidney at 12 h to 3 days post-infection, validating concurrently increased serum markers of organ damage in our model. Additionally, infected mice treated with standard care exhibited persistent haematological dysfunction, as evidenced by anaemia, thrombocytosis and neutrophilia, at recovery from organ dysfunction 7 days post-infection, features similarly observed in clinical sepsis patients. CONCLUSIONS: Our new abdominal gram-negative murine sepsis model recapitulates key disease outcomes observed in sepsis patients and allows the study of dysfunctional homeostasis in surviving animals.

BACKGROUND: Characterising the host response in sepsis is essential to understand its biological heterogeneity and to inform more precise diagnostic and therapeutic strategies. Existing evidence on sepsis comes predominantly from studies conducted in high-income countries (HICs), despite the highest burden in low- and middle-income countries (LMICs). We aimed to address this gap by identifying shared and pathogen-specific host response patterns among different infectious causes of sepsis in a tertiary care centre in India. METHODS: Patients fulfilling sepsis-3 criteria were enrolled within 24 h of intensive care unit admission in a tertiary care centre in Manipal, Karnataka, India. We measured 27 plasma biomarkers reflecting key pathophysiological domains (endothelial activation and coagulation, organ damage and inflammation, cytokine response, and chemokine release) to delineate host response profiles across sepsis aetiologies. FINDINGS: We included 956 sepsis patients, and a causative pathogen was identified in 54·1% (338 bacterial, 146 viral, 33 polymicrobial). The causative pathogen explained a significant proportion of biomarker variation (34·5% of explained variance; 9·4% of all variances). While bacterial sepsis was associated with stronger host response changes across all domains when compared to viral sepsis, notable variation was found within these microbial categories. INTERPRETATION: These findings highlight the biological heterogeneity of sepsis and the complexity of host-pathogen interactions in a setting with a diverse range of causative organisms. FUNDING: European Union, Amsterdam UMC, and Manipal Academy of Higher Education.

BACKGROUND: To develop and externally validate a machine learning-based model for predicting the risk of acute respiratory distress syndrome (ARDS) in patients with intra-abdominal sepsis. METHODS: Data were obtained from the MIMIC-IV and the eICU-CRD database, including patients diagnosed with intra-abdominal sepsis. ARDS occurrence during intensive care unit (ICU) stay was defined as the primary outcome. Feature selection was performed using a combination of the Boruta algorithm, LASSO regression, and logistic regression. Ten base machine learning algorithms were trained and integrated into a stacked ensemble model. Model performance was systematically evaluated, and interpretability was assessed using SHapley Additive exPlanations (SHAP). External validation was conducted in an independent cohort of patients with intra-abdominal sepsis admitted to the First Affiliated Hospital of Xinjiang Medical University between 2016 and 2024. A web-based risk prediction calculator was subsequently developed to facilitate clinical decision support. RESULTS: Among 1,120 patients included from the MIMIC-IV and eICU-CRD databases, 554 (49.46%) developed ARDS during their ICU stay. Fourteen predictors were retained, including mechanical ventilation, use of vasoactive agents, history of chronic pulmonary disease, Sequential Organ Failure Assessment (SOFA) score, Glasgow Coma Scale (GCS) score, key vital signs, and routine laboratory indicators. The stacking model achieved areas under the receiver operating characteristic curve (AUC) of 0.811 in the development cohort, 0.794 in the internal validation cohort, and 0.756 in the external validation cohort. SHAP analysis identified mechanical ventilation as the most influential predictor, while early vasoactive agents use was associated with a reduced ARDS risk. CONCLUSION: A stacked ensemble model for predicting ARDS risk in patients with intra-abdominal sepsis demonstrated robust performance, stability, and interpretability. This model provides a practical tool for early risk stratification and informed clinical decision-making.