Daily Sepsis Research Analysis

Older age, being male, obesity, smoking, and comorbidities (e.g., diabetes, asthma) are associated with an increased risk for severe infections. We hypothesized that there is a conserved common immune dysregulation across these risk factors. We integrated single-cell and bulk transcriptomic data and proteomic data from 12,026 blood samples across 68 cohorts to test this hypothesis. We found that our previously described 42-gene Severe-or-Mild (SoM) signature was associated with each of these risk factors prior to infection. Furthermore, this conserved immune signature was modifiable using immunomodulatory drugs and lifestyle changes. The SoM score predicted the individuals with sepsis who would be harmed by hydrocortisone treatment and individuals with asthma who would not respond to monoclonal antibody treatment. Finally, the SoM score was associated with all-cause mortality. The SoM signature has the potential to redefine the immunologic framing of the baseline immune state and response to chronic, subacute, and acute illnesses.

A 110-amino acid precursor of dermcidin (pre-dermcidin, pre-DCD) with a 19-residue N-terminal leader signal sequence can be secreted by human eccrine sweat glands as a leader-less pro-domain-containing peptide (pro-DCD), which is enzymatically cleaved to generate C-terminal anti-microbial peptides (dermcidin-1, DCD-1) capable of killing various bacteria. Previously, it was unknown whether pro-DCD could be pharmacologically developed as potential therapeutics for lethal sepsis. Here, we demonstrated that pharmacological suppression of pro-DCD with polyclonal antibodies worsened sepsis-induced inflammation and liver injury, whereas supplementation of pro-DCD or its PEGylation derivatives significantly protected against sepsis, even when given 2-24 h after disease onset. These protective effects were associated with a significant reduction in circulating levels of surrogate biomarkers [e.g., Granulocyte Colony Stimulating Factor (G-CSF), Interleukin-6 (IL-6), keratinocytes-derived chemokine (KC), Monocyte Chemoattractant Protein 1 (MCP-1), Macrophage Inflammatory Protein-2 (MIP-2), and Soluble Tumor Necrosis Factor Receptor I (sTNFRI)], tissue injury, and blood bacterial counts. Although pro-DCD or its PEGylation derivatives failed to directly kill bacteria across a wide range of concentrations, they were able to activate microtubule-associated protein 1A/1B-light chain 3 (LC3), a marker of autophagy and phagosome maturation in LC3-associated bacterial phagocytosis. Our findings suggest that pro-DCD-derived agents hold promise as potential therapies for clinical sepsis.

BACKGROUND: Early sepsis diagnosis is essential for initiating prompt treatment, preventing the progression of organ failure, and improving the survival rate of critically ill children. The aim of this study was to develop and validate a machine learning sepsis prediction model for patients admitted to a pediatric intensive care unit (PICU) who met the Phoenix Sepsis Score Criteria using EMR data. METHODS: Data were obtained from two PICUs within the same healthcare system. Readily available variables were used to develop and validate machine learning models predicting the onset of sepsis in critically ill children. RESULTS: A total of 63,875 PICU encounters were included, of which there were 5248 who met the criteria for Phoenix Sepsis. We trained and tested 4 machine learning models using vital signs, laboratory tests, demographic data, medications, and organ dysfunction scores. The Categorical Boosting (CatBoost) model had the best performance with an AUROC of 0.98 (95% CI, 0.98-0.98), and an AUPRC of 0.83 (95% CI, 0.82-0.83). CONCLUSIONS: The implementation of our model capable of predicting the onset of sepsis defined by the Phoenix Sepsis Score criteria may help clinicians recognize and manage children with sepsis more efficiently to reduce morbidity and mortality. IMPACT: Sepsis is a life-threatening condition with high rates of morbidity and mortality in children, especially in pediatric critical care units. However, there is no validated model using readily available variables in the electronic medical record data to identify critically ill patients with sepsis. The use of machine learning and electronic medical health records data to develop a predictive model can automate the identification of patients at high risk for sepsis-related organ dysfunction. The implementation of this tool can improve recognition of sepsis and prevent the progression of sepsis-related organ dysfunction leading to death.