Daily Sepsis Research Analysis

Sepsis, a severe infection, often leads to an overwhelming inflammatory response. Transfer RNA (tRNA)-derived small RNAs (tsRNAs), a emerging type of small RNAs, is crucial in various biological activities. Nevertheless, the connection between tsRNAs and sepsis is still unknown. We attempt to uncover the functions that these small RNAs play in sepsis. Our studies in humans, cells, and animal models revealed a significant downregulation of tiRNA-Glu-TTC-003 in the plasma of sepsis patients, in vitro macrophage inflammation models, and in the plasma and tissues of mice subjected to cecal ligation and puncture (CLP). Subsequent experiments revealed that the administration of tiRNA-Glu-TTC-003 agomir augmented the survival rate of CLP mice, mitigated organ damage, and attenuated inflammatory responses. In cellular experiments, we observed that overexpression of tiRNA-Glu-TTC-003 ameliorated the inflammatory state of cells and inhibited the expression of inflammation-related factors in M1 macrophages. Additionally, through target gene prediction and screening, we found that tiRNA-Glu-TTC-003 may interact with triggering receptor expressed on myeloid cells 2 (TREM2) to exert its functions. In THP-1 cells, the application of tiRNA-Glu-TTC-003 mimics resulted in an upregulation of TREM2 at both mRNA and protein levels, alongside a downregulation of Toll-like receptor 4 (TLR4) and its downstream effector, myeloid differentiation factor 88 (MyD88). In conclusion, tiRNA-Glu-TTC-003 demonstrates significant anti-inflammatory and protective effects in CLP mice and macrophage inflammation models. These findings suggest that tiRNA-Glu-TTC-003 may be a major factor in the inflammatory response of sepsis and provide a new idea for future treatment.

BACKGROUND: Venous thromboembolism (VTE) is a common and severe complication in intensive care unit (ICU) patients with sepsis. Conventional risk stratification tools lack sepsis-specific features and may inadequately capture complex, nonlinear interactions among clinical variables. OBJECTIVE: This study aimed to develop and validate an interpretable machine learning (ML) model for the early prediction of VTE in ICU patients with sepsis. METHODS: This multicenter retrospective study used data from the Medical Information Mart for Intensive Care IV database for model development and internal validation, and an independent cohort from Changshu Hospital for external validation. Candidate predictors were selected through univariate analysis, followed by least absolute shrinkage and selection operator regression. Retained variables were used in multivariable logistic regression to identify independent predictors, which were then used to develop 9 ML models, including categorical boosting, decision tree, k-nearest neighbor, light gradient boosting machine, logistic regression, multilayer perceptron, naive Bayes, random forest, and support vector machine. Performance was evaluated by discrimination (area under the curve [AUC]), calibration, and clinical use (decision curve analysis). A subgroup analysis stratified by the Sequential Organ Failure Assessment score was conducted in the external cohort to assess model stability across sepsis severity levels. Model interpretability was assessed using Shapley Additive Explanations (SHAP) to quantify the contribution of features to the predicted risk. RESULTS: A total of 25,197 patients from the Medical Information Mart for Intensive Care IV cohort and 328 patients from the external cohort were included, with VTE incidences of 844 out of 25,197 (3.4%) and 30 out of 328 (9.2%), respectively. The light gradient boosting machine model performed best, achieving an AUC of 0.956 in internal validation. Despite the higher VTE incidence and clinical severity in the external validation, the model maintained robust generalization with an AUC of 0.786. Notably, the model achieved enhanced discriminative ability in the severe sepsis subgroup (Sequential Organ Failure Assessment score >6) with an AUC of 0.816, compared with 0.769 in the mild to moderate sepsis subgroup. Calibration curves indicated strong agreement between predicted and observed outcomes, and decision curve analysis showed superior net benefit across clinically relevant thresholds. SHAP analysis identified central venous catheterization, serum chloride and bicarbonate levels, arterial catheterization, and prolonged partial thromboplastin time as the most influential predictors. Partial dependence plots revealed both linear and nonlinear associations between these variables and VTE risk. Individual-level force plots further enhanced interpretability by visualizing personalized risk profiles. CONCLUSIONS: We developed a high-performing and interpretable ML model for predicting VTE in ICU patients with sepsis. The model demonstrated robustness across cohorts and enhanced performance in the severe sepsis population. By integrating diverse clinical data and leveraging SHAP for transparent explanations, this tool may support personalized prophylaxis and early diagnostic strategies.

BACKGROUND: Sepsis remains a significant public health concern, with evidence of significant racial and ethnic disparities in outcomes. OBJECTIVES: This study investigates how racial and ethnic disparities in severe sepsis and septic shock outcomes may have changed following the implementation of the 2015 Severe Sepsis and Septic Shock Early Management (SEP-1) Bundle. METHODS: This was a retrospective analysis of a patient cohort from the 2013-2017 National Inpatient Sample datasets. ICD codes from the SEP-1 manual were used to identify eligible patients with severe sepsis or septic shock. Mortality rates, length of stay, and total costs were examined as primary outcomes using multivariable logistic and linear regression models, and an event study design was used to estimate changes in these outcomes post-SEP-1 implementation. Racial and ethnic disparities were assessed pre- and post-SEP-1 implementation, and differences in post-SEP-1 time trends in each outcome were compared across groups. RESULTS: At baseline, racial and ethnic minorities, particularly Black patients, demonstrated significantly higher mortality rates, lengths of stay, and costs compared to White patients. Following SEP-1 implementation, there were overall reductions in mortality and costs; however, racial and ethnic disparities remained statistically unchanged. The event study analysis indicated a statistically significant decline in mortality rates post-SEP-1 bundle, and the benefits were experienced equally across all racial and ethnic groups. CONCLUSIONS: Despite the introduction of the SEP-1 guidelines leading to some improvements in severe sepsis and septic shock outcomes, racial and ethnic disparities in mortality, length of stay, and costs remained statistically significant.