LEUKOCYTE PHENOTYPING IN SEPSIS USING OMICS, FUNCTIONAL ANALYSIS, AND IN SILICO MODELING

The Utility of C-Reactive Protein, Procalcitonin, and Leukocyte Values in Predicting the Prognosis of Patients with Pneumosepsis and Septic Shock

Background and Objectives: The predictive value of changes in C-reactive protein (CRP), procalcitonin, and leukocyte levels, which are commonly used in the diagnosis of infection in sepsis and septic shock, remains a topic of debate. The aim of this study was to evaluate the effectiveness of changes in CRP, procalcitonin, and leukocyte counts on the prognosis of 230 patients admitted to the intensive care unit (ICU) with the diagnosis of sepsis and pneumonia-related septic shock between 1 April 2022 and 31 December 2023, and to investigate whether any of these markers have a superior predictive value over the others in forecasting prognosis. Materials and Methods: This single-center, retrospective, cross-sectional observational study included patients who developed sepsis and septic shock due to community-acquired pneumonia and were admitted to the ICU. Demographic data, 1-month and 90-day mortality rates, length of stay in the ICU, discharge to the ward or an outside facility, need for dialysis after sepsis, need for invasive or noninvasive mechanical ventilation during the ICU stay and the duration of this support, whether patients admitted with sepsis or septic shock required inotropic agent support during their stay in the ICU and whether they received monotherapy or combination therapy with antibiotics during their admission to the ICU, the Comorbidity Index score (CCIS), CURB-65 score (confusion, uremia, respiratory rate, BP, age ≥ 65), and Acute Physiology and Chronic Health Evaluation II (APACHE-II) score were analyzed. Additionally, CRP, procalcitonin, and leukocyte levels were recorded, and univariate and multivariate logistic regression analyses were performed to evaluate their effects on 1- and 3-month mortality outcomes. In all statistical analyses, a p-value of <0.05 was accepted as a significant level. Results: According to multivariate logistic regression analysis, low BMI, male gender, and high CCIS, CURB-65, and APACHE-II scores were found to be significantly associated with both 1-month and 3-month mortality (p < 0.05). Although there was no significant relationship between the first-day levels of leukocytes, CRP, and PCT and mortality, their levels on the third day were observed to be at their highest in both the 1-month and 3-month mortality cases (p < 0.05). Additionally, a concurrent increase in any two or all three of CRP, PCT, and leukocyte values was found to be higher in patients with 3-month mortality compared with those who survived (p = 0.004). Conclusions: In patients with pneumoseptic or pneumonia-related septic shock, the persistent elevation and concurrent increase in PCT, CRP, and leukocyte values, along with male gender, advanced age, low BMI, and high CCIS, CURB-65, and APACHE-II scores, were found to be significantly associated with 3-month mortality.

MAN VERSUS MACHINE: PROVIDER DIRECTED VERSUS PRECISION AUTOMATED CRITICAL CARE MANAGEMENT IN A PORCINE MODEL OF DISTRIBUTIVE SHOCK

ABSTRACT

Background: Critical care management of shock is a labor-intensive process. Precision Automated Critical Care Management (PACC-MAN) is an automated closed-loop system incorporating physiologic and hemodynamic inputs to deliver interventions while avoiding excessive fluid or vasopressor administration. To understand PACC-MAN efficacy, we compared PACC-MAN to provider-directed management (PDM). We hypothesized that PACC-MAN would achieve equivalent resuscitation outcomes to PDM while maintaining normotension with lower fluid and vasopressor requirements. Methods : Twelve swine underwent 30% controlled hemorrhage over 30 min, followed by 45 min of aortic occlusion to generate a vasoplegic shock state, transfusion to euvolemia, and randomization to PACC-MAN or PDM for 4.25 h. Primary outcomes were total crystalloid volume, vasopressor administration, total time spent at hypotension (mean arterial blood pressure <60 mm Hg), and total number of interventions. Results : Weight-based fluid volumes were similar between PACC-MAN and PDM; median and IQR are reported (73.1 mL/kg [59.0-78.7] vs. 87.1 mL/kg [79.4-91.8], P = 0.07). There was no statistical difference in cumulative norepinephrine (PACC-MAN: 33.4 μg/kg [27.1-44.6] vs. PDM: 7.5 [3.3-24.2] μg/kg, P = 0.09). The median percentage of time spent at hypotension was equivalent (PACC-MAN: 6.2% [3.6-7.4] and PDM: 3.1% [1.3-6.6], P = 0.23). Urine outputs were similar between PACC-MAN and PDM (14.0 mL/kg vs. 21.5 mL/kg, P = 0.13). Conclusion : Automated resuscitation achieves equivalent resuscitation outcomes to direct human intervention in this shock model. This study provides the first translational experience with the PACC-MAN system versus PDM.

Distinct functional neutrophil phenotypes in sepsis patients correlate with disease severity

Purpose

Sepsis is a clinical syndrome defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis is a highly heterogeneous syndrome with distinct phenotypes that impact immune function and response to infection. To develop targeted therapeutics, immunophenotyping is needed to identify distinct functional phenotypes of immune cells. In this study, we utilized our Organ-on-Chip assay to categorize sepsis patients into distinct phenotypes using patient data, neutrophil functional analysis, and proteomics.

Methods

Following informed consent, neutrophils and plasma were isolated from sepsis patients in the Temple University Hospital ICU (n=45) and healthy control donors (n=7). Human lung microvascular endothelial cells (HLMVEC) were cultured in the Organ-on-Chip and treated with buffer or cytomix ((TNF/IL-1β/IFNγ). Neutrophil adhesion and migration across HLMVEC in the Organ-on-Chip were used to categorize functional neutrophil phenotypes. Quantitative label-free global proteomics was performed on neutrophils to identify differentially expressed proteins. Plasma levels of sepsis biomarkers and neutrophil extracellular traps (NETs) were determined by ELISA.

Results

We identified three functional phenotypes in critically ill ICU sepsis patients based on ex vivo neutrophil adhesion and migration patterns. The phenotypes were classified as: Hyperimmune characterized by enhanced neutrophil adhesion and migration, Hypoimmune that was unresponsive to stimulation, and Hybrid with increased adhesion but blunted migration. These functional phenotypes were associated with distinct proteomic signatures and differentiated sepsis patients by important clinical parameters related to disease severity. The Hyperimmune group demonstrated higher oxygen requirements, increased mechanical ventilation, and longer ICU length of stay compared to the Hypoimmune and Hybrid groups. Patients with the Hyperimmune neutrophil phenotype had significantly increased circulating neutrophils and elevated plasma levels NETs.

Conclusion

Neutrophils and NETs play a critical role in vascular barrier dysfunction in sepsis and elevated NETs may be a key biomarker identifying the Hyperimmune group. Our results establish significant associations between specific neutrophil functional phenotypes and disease severity and identify important functional parameters in sepsis pathophysiology that may provide a new approach to classify sepsis patients for specific therapeutic interventions.

The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and in silico modeling to identify new therapeutics

Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or gram positive), fungal or viral (such as COVID) infections. However, therapeutics developed in animal models and traditional in vitro sepsis models have had little success in clinical trials, as these models have failed to fully replicate the underlying pathophysiology and heterogeneity of the disease. The current understanding is that the host response to sepsis is highly diverse among patients, and this heterogeneity impacts immune function and response to infection. Phenotyping immune function and classifying sepsis patients into specific endotypes is needed to develop a personalized treatment approach. Neutrophil-endothelium interactions play a critical role in sepsis progression, and increased neutrophil influx and endothelial barrier disruption have important roles in the early course of organ damage. Understanding the mechanism of neutrophil-endothelium interactions and how immune function impacts this interaction can help us better manage the disease and lead to the discovery of new diagnostic and prognosis tools for effective treatments. In this review, we will discuss the latest research exploring how in silico modeling of a synergistic combination of new organ-on-chip models incorporating human cells/tissue, omics analysis and clinical data from sepsis patients will allow us to identify relevant signaling pathways and characterize specific immune phenotypes in patients. Emerging technologies such as machine learning can then be leveraged to identify druggable therapeutic targets and relate them to immune phenotypes and underlying infectious agents. This synergistic approach can lead to the development of new therapeutics and the identification of FDA approved drugs that can be repurposed for the treatment of sepsis.

ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES

ABSTRACT

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.