Daily Sepsis Research Analysis

Sepsis, a syndrome of life-threatening organ dysfunction caused by dysregulated host responses to infection, exhibits profound pathobiological heterogeneity, hindering the development of effective therapies. Current subtyping approaches, often reliant on single-omics data or unsupervised clustering, yield poorly reproducible and therapeutically misaligned classifications. Here, we introduce a goal-directed subgroup identification (GD-SI) framework that optimizes patient stratification for differential treatment responses, integrating longitudinal multi-omics data (transcriptomic, proteomic, metabolomic, phenomic) from 1327 subjects across 43 hospitals. While supervised multi-omics integration frameworks (e.g., DIABLO) effectively capture shared biological signals, our approach anchors subgroup discovery directly to treatment-effect optimization. This strategy achieves substantial cross-omic concordance and, crucially, generalizes to predict differential treatment response across international critical care databases. Patients stratified by GD-SI-derived benefit scores for restrictive versus liberal fluid resuscitation exhibited marked survival differences, with similar advantages observed for ulinastatin immunomodulation. External validations in MIMIC-IV and ZiGongDB confirm prognostic generalizability. This framework reconciles biological heterogeneity with clinical actionability, offering a scalable infrastructure for precision trial design and personalized sepsis management. Our findings underscore the translational potential of omics-driven, goal-directed stratification to overcome decades of therapeutic stagnation in critical care.

Sepsis is a leading cause of hospital mortality, and its significant heterogeneity complicates prognosis and stratification. To address this challenge, we developed an explainable artificial intelligence prognostic model (SepsisFormer, a transformer-based neural network) and an automated risk-stratification tool (SMART) for sepsis. In a multi-center retrospective study of 12,408 sepsis patients, SepsisFormer achieved high predictive accuracy (AUC: 0.9301, sensitivity: 0.9346, and specificity: 0.8312). SMART (AUC: 0.7360) surpassed most established scoring systems. Seven coagulation-inflammatory routine laboratory measurements and patient age were identified to classify patients' four risk levels (mild, moderate, severe, dangerous) and two subphenotypes (CIS1 and CIS2), each with distinct clinical characteristics and mortality rates. Notably, patients with moderate/severe levels or CIS2 derive more significant benefits from anticoagulant treatment. Our work, therefore, offers a set of simple, real-time executable tools for sepsis heterogeneity, demonstrating the potential to enhance sepsis clinical practice globally, particularly in resource-constrained healthcare settings.

Sepsis-induced brain dysfunction (SIBD), a critical determinant of mortality and long-term neurological sequelae in sepsis patients. The mechanistic understanding of SIBD has been limited by conventional single-modality approaches, which fail to capture the complex oxygenation-metabolism interplay. Here, we present an integrated photoacoustic-metabolomic platform that combines high-resolution photoacoustic imaging with targeted metabolomics to comprehensively assess cerebral oxygenation and metabolic alterations during sepsis. Our imaging system provides high spatiotemporal resolution, enabling mapping of key parameters, including cerebral oxygen saturation (sO2), oxygen extraction fraction (OEF), and the spatial heterogeneity of oxygen metabolism. By integrating these imaging capabilities with region-specific metabolomic profiling, we uncover a dynamic relationship between sepsis-driven oxygenation disruptions and metabolic reprogramming. Specifically, we demonstrate that sepsis induces heterogeneous cortical hypoxia, dysregulated OEF dynamics, and a metabolic shift marked by enhanced glycolysis and suppressed pentose phosphate pathway activity in high-OEF regions. This multimodal platform not only advances our understanding of the pathophysiology of SIBD but also offers a powerful tool for early diagnosis, personalized therapeutic strategies, highlighting the promise in bedside monitoring and precision medicine applications in sepsis management.