Daily Sepsis Research Analysis

BACKGROUND: Sepsis, a life-threatening condition with persistently high mortality, involves dysregulated immune responses and programmed cell death (PCD). However, the specific roles and interactions of diverse PCD pathways in sepsis pathogenesis remain incompletely understood. This study aimed to systematically characterize PCD patterns and their clinical relevance in sepsis. METHODS: We integrated three bulk transcriptomic datasets (81 controls, 165 sepsis patients) and one single-cell RNA sequencing (scRNA-seq) dataset (4 controls, 4 early sepsis patients, 52,315 cells) from public databases. Gene set variation analysis (GSVA) quantified activity of 13 PCD pathways. Immune infiltration was assessed via single-sample gene set enrichment analysis (ssGSEA). A cell death-associated signature (CDS) risk score was developed using least absolute shrinkage and selection operator (LASSO) regression. scRNA-seq analysis identified cell-type-specific PCD activation and intercellular communication using Seurat, AUCell, and CellPhoneDB. Additionally, an independent RNA-seq cohort generated from our own sequencing of sepsis patients and healthy controls was used for external validation. RESULTS: Transcriptomic analysis identified 5,591 differentially expressed genes enriched in immune and cell death pathways. Four PCD pathways-ferroptosis, disulfidptosis, NETosis, and entotic cell death-were significantly upregulated in sepsis and strongly correlated with immune cell infiltration, such as activated dendritic cells and neutrophils. The CDS risk score, based on 18 core PCD genes, showed excellent diagnostic accuracy across both public microarray datasets (AUC = 0.961 and 0.844) and our independent high-throughput RNA-seq dataset (AUC = 0.975). scRNA-seq revealed monocytes as dominant effectors, exhibiting heightened activation of ferroptosis, entotic death, and netotic pathways alongside metabolic reprogramming, including enhanced glutathione metabolism and oxidative phosphorylation (OXPHOS). Furthermore, monocyte-centric intercellular communication was dysregulated in sepsis, featuring upregulated MIF-CXCR4, ANXA1-FPR2, and HLA-KIR signaling axes. CONCLUSIONS: By integrating public microarray and single-cell transcriptomic data with independent high-throughput sequencing validation, this study analysis identifies ferroptosis, disulfidptosis, netotic death, and entotic death as key dysregulated PCD pathways in sepsis, with monocytes serving as central hubs integrating PCD, metabolic reprogramming, and immune communication. The CDS risk score provides a robust diagnostic and stratification tool. Targeting monocyte-driven PCD-metabolism-communication networks offers promising avenues for precision immunotherapy in sepsis.

BACKGROUND: Optimal target of mean arterial pressure (MAP) remains controversial in sepsis management. Critical closing pressure (Pcc), the arterial pressure at which blood flow ceases, is the key determinant of vascular waterfall phenomenon. Tissue perfusion pressure (TPP), the difference between MAP and Pcc, represents the driving pressure for arterial blood flow. We evaluated the prognostic value of Pcc and TPP for improving risk stratification in sepsis. METHODS: This retrospective cohort study included adult patients with sepsis in 18 hospitals between August 2013 to October 2022 from two independent datasets (the SEPSIS-EDT registry and the critical care database of PUMCH). Pcc was estimated via linear regression of hourly MAP against product of heart rate and pulse pressure, while TPP was calculated as MAP minus Pcc. Patients were categorized into four groups based on the optimal thresholds for mean Pcc and TPP within 24 hours of sepsis diagnosis: Low TPP-Low Pcc, Low TPP-High Pcc, High TPP-Low Pcc, and High TPP-High Pcc. Clinical outcomes included mortality rates and development of acute kidney injury (AKI) within two and seven days of sepsis diagnosis. External validation was performed using MIMIC-IV cohort. RESULTS: A total of 6,769 patients (mean age 61; 61.0% men) were included. ICU mortality was highest in the Low TPP-Low Pcc group and lowest in the High TPP-High Pcc group (35.1% vs. 20.1%; risk difference: 15.0%, 95% confidence interval: 10.2-19.8%). Similar patterns were observed for other outcomes. After adjustment for MAP, increased Pcc with concomitant reduced TPP showed a significant U-shaped association with both mortality and AKI development (P < 0.001). The findings were consistent in the MIMIC-IV cohort. CONCLUSION: While MAP remains central to sepsis management, Pcc and TPP provide complementary prognostic information. Incorporating these parameters into clinical assessment may improve risk stratification and optimize blood pressure management.

OBJECTIVES: This systematic review evaluates artificial intelligence (AI)-based predictive models developed for early sepsis detection in adult hospitalized patients. It explores model types, input features, validation strategies, performance metrics, clinical integration, and implementation challenges. DATA SOURCES: A systematic search was conducted across PubMed, Scopus, Web of Science, Google Scholar, and CENTRAL for studies published between January 2015 and March 2025. STUDY SELECTION: Eligible studies included those developing or validating AI models for adult inpatient sepsis prediction using electronic health record data and reporting at least one performance metric (area under the curve [AUC], sensitivity, specificity, or F1 score). Studies focusing on pediatric populations, lacking quantitative evaluation, or unpublished in peer-reviewed journals were excluded. DATA EXTRACTION: Data extraction followed preferred reporting items for systematic reviews and meta-analyses guidelines. Extracted variables included study design, patient population, model type, input features, validation approach, and performance outcomes. DATA SYNTHESIS: A total of 52 studies met the inclusion criteria. Most used retrospective designs, with limited prospective or real-time clinical validation. Commonly used algorithms included random forests, neural networks, support vector machines, and deep learning architectures (long short-term memory, convolutional neural network). Input data varied from structured sources (vital signs, laboratory values, demographics) to unstructured clinical notes processed via natural language processing. Reported AUC values ranged from 0.79 to 0.96, indicating strong predictive performance across models. CONCLUSIONS: AI models demonstrate significant promise for early sepsis detection, outperforming conventional scoring systems in many cases. However, generalizability, interpretability, and clinical implementation remain major challenges. Future research should emphasize externally validated, explainable, and scalable AI solutions integrated into real-time clinical workflows.