Sepsis-associated Acute Respiratory Distress Syndrome Research Articles

Elevated Circulating Cell-Free Hemoglobin Drives Endothelial Glycocalyx Destruction and Inflammation in Sepsis

Rationale: Endothelial injury is a hallmark of sepsis-induced acute respiratory distress syndrome (ARDS). Endothelial barrier integrity is regulated by the endothelial glycocalyx, a matrix of proteoglycans and glycosaminoglycans (GAGs) that lines the vascular lumen. In sepsis, this glycocalyx is degraded by heparanase, which cleaves heparan sulfate, the primary glycocalyx GAG. Glycocalyx breakdown is linked to worse sepsis outcomes, but the mechanisms inducing heparanase activity in sepsis are unknown. We previously found that plasma levels of hemoglobin released from red blood cells (cell-free hemoglobin, CFH) are elevated in clinical sepsis and correlate with higher rates of ARDS and death. CFH also worsens endothelial injury and microvascular permeability in murine sepsis. We therefore hypothesized that CFH upregulates heparanase production to drive glycocalyx shedding and inflammation in sepsis. Methods: To test the association between CFH levels, heparanase expression, and glycocalyx degradation, circulating levels of heparanase, heparan sulfate, and total GAGs were measured in 77 sepsis patients and compared across CFH quartiles. In mice with intraabdominal polymicrobial sepsis with elevated CFH (CS [cecal slurry] + intravenous [IV] CFH), plasma heparanase, total GAGs, and syndecan-1 were quantified. Whole lung and plasma pro-inflammatory cytokines were also determined to investigate the impact of CFH on inflammation in sepsis. Results: In sepsis patients, higher circulating CFH levels were associated with increased plasma heparanase (p=0.0304), total GAGs (p=0.0178), and heparan sulfate (p=0.0112). CFH also exacerbated glycocalyx degradation in murine sepsis; circulating syndecan-1 (p=0.0152) and total GAGs (p=0.0094) were elevated in animals that received CS+IV CFH compared to CS alone. This effect may be mediated by upregulation of active heparanase, which was higher in the plasma of CS+IV CFH treated animals (p=0.1333). CFH also worsened illness severity, as CS+IV CFH animals had worse sepsis severity scores (p=0.0074). Whole lung tumor necrosis factor-alpha (TNF-a; p=0.0129) and interferon-gamma (IFN-g; p=0.0264) transcript levels were increased in the CS+IV CFH group, indicating greater lung inflammation. Systemic inflammation was also enhanced in the CS+IV CFH animals, demonstrated by higher plasma TNF-a (p<0.001) and IFN-g (p=0.0221) concentrations. Conclusions: In sepsis, CFH is associated with upregulation of heparanase and endothelial glycocalyx degradation, which may disrupt the endothelial barrier, causing downstream organ injury. Future studies to define the molecular pathways that underlie these findings and their effect on endothelial permeability will be an important next step towards identifying novel treatment targets to improve outcomes for patients with sepsis-associated ARDS. Funding F30HL170483, R01HL164937, R01HL158906, R01HL150783, R01HL125371. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Plasma renin as a novel prognostic biomarker of sepsis-associated acute respiratory distress syndrome

Sepsis-associated acute respiratory distress syndrome (ARDS) is a life-threatening condition in critical care medicine for which there is a substantial need for early prognostic biomarkers of outcome. The present study seeks to link plasma renin levels and 30-day mortality in sepsis-associated ARDS patients treated at our institution. The Registry of Critical Illness (RoCI) prospectively enrolled patients from the intensive care units (ICU) within a single academic medical center, and a convenience sample of patients with sepsis-associated ARDS was analyzed from this cohort. This study was approved by the Mass General Brigham Institutional Review Boards (IRB) as part of the RoCI, and all procedures performed were in accordance with the ethical standards of the institutional board. From April 2012 to February 2019, a cohort of 32 adult sepsis-associated ARDS patients with 500 µL of plasma samples available on Day 0 and Day 3 of their ICU stay were enrolled. Renin levels were measured twice, on Day 0 and Day 3 via the direct renin enzyme-linked immunosorbent assay (ELISA EIA-525) by DRG diagnostics. Day 0 and Day 3 renin were statistically evaluated via logistic regression to predict 30-day mortality. Direct renin levels of 64 samples were assayed from 32 sepsis-associated ARDS patients (50% male; mean ± SD, 55 ± 13.8 years old). The 30-day hospital mortality rate was 59.4%. Patients who died within 30 days of admission were more likely to have an elevated Day 3 Renin (Odds ratio [OR] = 6, 95% CI 1.25–28.84) and have received vasopressors (OR = 13.33, 95% CI 1.43–123.95). Adjusting for vasopressor use as a proxy for septic shock status, patients with an Elevated Day 3 Renin had a 6.85 (95% CI 1.07–43.75) greater odds of death than those with Low-Normal Day 3 Renin. Patients with sustained Elevated Renin levels from Day 0 to Day 3 had the highest risk of death in a 30-day window. In this study, we found that renin may be a novel biomarker that has prognostic value for patients with sepsis-associated ARDS. Future studies evaluating renin levels in patients with sepsis-associated ARDS are needed to validate these findings.

Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data

BackgroundAcute respiratory distress syndrome (ARDS) is a fatal outcome of severe sepsis. Machine learning models are helpful for accurately predicting ARDS in patients with sepsis at an early stage. ObjectiveWe aim to develop a machine-learning model for predicting ARDS in patients with sepsis in the intensive care unit (ICU). MethodsThe initial clinical data of patients with sepsis admitted to the hospital (including population characteristics, clinical diagnosis, complications, and laboratory tests) were used to predict ARDS, and screen out the crucial variables. After comparing eight different algorithms, namely, XG boost, logistic regression, light GBM, random forest, GaussianNB, complement NB, support vector machine (SVM), and K nearest neighbors (KNN), rebuilding a prediction model with the best one. When remodeling with the best algorithm, 10% was randomly selected to test, and the remaining was trained for cross-validation. Using the area under the curve (AUC), sensitivity, accuracy, specificity, positive and negative predictive value, F1 score, kappa value, and clinical decision curve to evaluate the model's performance. Eventually, the application in the model illustrated by the SHAP package. ResultsTen critical features were screened utilizing the lasso method, namely, PaO2/PAO2, A-aDO2, PO2(T), CRP, gender, PO2, RDW, MCH, SG, and chlorine. The prior ranking of variables demonstrated that PaO2/PAO2 was the most significant variable. Among the eight algorithms, the performance of the Gaussian NB algorithm was significantly better than that of the others. After remodeling with the best algorithm, the AUC in the training and validation sets were 0.777 and 0.770, respectively, and the algorithm performed well in the test set (AUC = 0.781, accuracy = 78.6%, sensitivity = 82.4%, F1 score = 0.824). A comparison of the overlap factors with those of previous models revealed that the model we developed performs better. ConclusionSepsis-associated ARDS can be accurately predicted early via a machine learning model based on existing clinical data. These findings are helpful for accurate identification and improvement of the prognosis in patients with sepsis-associated ARDS.

Clinical significance of platelet mononuclear cell aggregates in patients with sepsis and acute respiratory distress syndrome.

The diagnosis of sepsis combined with acute respiratory distress syndrome (ARDS) has increased owing to the enhanced awareness among medical professionals and the continuous development of modern medical technologies, while early diagnosis of ARDS still lacks specific biomarkers. One of the main pathogenic mechanisms of sepsis-associated ARDS involves the actions of various pathological injuries and inflammatory factors, such as platelet and white blood cells activation, leading to an increase of surface adhesion molecules. These adhesion molecules further form platelet-white blood cell aggregates, including platelet-mononuclear cell aggregates (PMAs). PMAs has been identified as one of the markers of platelet activation, here we hypothesize that PMAs might play a potential biomarker for the early diagnosis of this complication. To investigate the expression of PMAs in the serum of patients with sepsis complicated by ARDS and its clinical significance. We selected 72 hospitalized patients diagnosed with sepsis as the study population between March 2019 and March 2022. Among them, 30 patients with sepsis and ARDS formed the study group, while 42 sepsis patients without ARDS comprised the control group. After diagnosis, venous blood samples were immediately collected from all patients. Flow cytometry was employed to analyze the expression of PMAs, platelet neutrophil aggregates (PNAs), and platelet aggregates (PLyAs) in the serum. Additionally, the Acute Physiology and Chronic Health Evaluation (APACHE) II score was calculated for each patient, and receiver operating characteristic curves were generated to assess diagnostic value. The study found that the levels of PNAs and PLyAs in the serum of the study group were higher than those in the control group, but the difference was not statistically significant (P > 0.05). However, the expression of PMAs in the serum of the study group was significantly upregulated (P < 0.05) and positively correlated with the APACHE II score (r = 0.671, P < 0.05). When using PMAs as a diagnostic indicator, the area under the curve value was 0.957, indicating a high diagnostic value (P < 0.05). Furthermore, the optimal cutoff value was 8.418%, with a diagnostic sensitivity of 0.819 and specificity of 0.947. In summary, the serum levels of PMAs significantly increase in patients with sepsis and ARDS. Therefore, serum PMAs have the potential to become a new biomarker for clinically diagnosing sepsis complicated by ARDS.

STAT3-Mediated Ferroptosis is Involved in Sepsis-Associated Acute Respiratory Distress Syndrome.

Sepsis-induced acute respiratory distress syndrome (ARDS) poses a grave danger to life, resulting from sepsis-induced multi-organ failure. Although ferroptosis, a form of iron-dependent lipid peroxidative cell death, has been associated with sepsis-induced ARDS, the specific mechanisms are not fully understood. In this study, we utilized WGCNA, PPI, friends analysis, and six machine learning techniques (Lasso, SVM, RFB, XGBoost, AdaBoost, and LightGBM) to pinpoint STAT3 as a potential diagnostic marker. A significant increase in monocyte and neutrophil levels was observed in patients with sepsis-induced ARDS, as revealed by immune infiltration analyses, when compared to controls. Moreover, there was a positive correlation between STAT3 expression and the level of infiltration. Single-cell analysis uncovered a notable disparity in B-cell expression between sepsis and sepsis-induced ARDS. Furthermore, in vitro experiments using LPS-treated human bronchial epithelial cells (BEAS-2B) and THP1 cells demonstrated a significant increase in STAT3 phosphorylation expression. Additionally, the inhibition of STAT3 phosphorylation by Stattic effectively prevented LPS-induced ferroptosis in both BEAS-2B and THP1 cells. This indicates that the activation of STAT3 phosphorylation promotes ferroptosis in human bronchial epithelial cells in response to LPS. In summary, this research has discovered and confirmed STAT3 as a potential biomarker for the diagnosis and treatment of sepsis-induced ARDS.

Efficacy observation of erythropoietin on sepsis complicated with acute respiratory distress syndrome

This study is to evaluate the efficacy of erythropoietin in treating sepsis-associated acute respiratory distress syndrome (ARDS). One hundred patients with sepsis-related ARDS were randomized into the placebo group and Erythropoietin (EPO) group. Patients in the placebo group received saline as placebo on the standard therapy, while the EPO group received recombinant human erythropoietin injections on the standard therapy. It was found that the heart rate and mean arterial pressure did not differ significantly between days 7 and 14 after treatment initiation (p &gt; 0.05). The partial pressure of oxygen (PaO2) and oxygenation index levels measured on days 7 and 14 were significantly higher than the placebo group and partial pressure of carbon dioxide (PaCO2) was significantly lower than the placebo group (p &lt; 0.05). Lung capacity and functional residual capacity (and FRC) increased significantly in tumor necrosis factor-α (TNF-α), interleukin-10 (IL-10), and C-reactive protein (CRP) concentrations (p &lt; 0.05). In the EPO group, the duration of mechanical ventilation was significantly shorter and the mortality rate was significantly reduced (Log-Rank test, χ2 = 4.651, p = 0.031). The results confirm that EPO significantly improves lung function and blood gas parameters, reduces serum levels of inflammatory markers, and reduces the risk of death in sepsis-induced ARDS patients, highlighting the potential therapeutic role of EPO in the management of this disease.

Advances of presepsin in sepsis-associated ARDS.

This article reviews the correlation between presepsin and sepsis and the resulting acute respiratory distress syndrome (ARDS). ARDS is a severe complication of sepsis. Despite the successful application of protective mechanical ventilation, restrictive fluid therapy, and neuromuscular blockade, which have effectively reduced the morbidity and mortality associated with ARDS, the mortality rate among patients with sepsis-associated ARDS remains notably high. The challenge lies in the prediction of ARDS onset and the timely implementation of intervention strategies. Recent studies have demonstrated significant variations in presepsin (PSEP) levels between patients with sepsis and those without, particularly in the context of ARDS. Moreover, these studies have revealed substantially elevated PSEP levels in patients with sepsis-associated ARDS compared to those with nonsepsis-associated ARDS. Consequently, PSEP emerges as a valuable biomarker for identifying patients with an increased risk of sepsis-associated ARDS and to predict in-hospital mortality.

Exposure to ambient air pollutants and acute respiratory distress syndrome risk in sepsis.

Exposures to ambient air pollutants may prime the lung enhancing risk of acute respiratory distress syndrome (ARDS) in sepsis. Our objective was to determine the association of short-, medium-, and long-term pollutant exposures and ARDS risk in critically ill sepsis patients. We analyzed a prospective cohort of 1858 critically ill patients with sepsis, and estimated short- (3 days), medium- (6 weeks), and long- (5 years) term exposures to ozone, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), particulate matter < 2.5μm (PM2.5), and PM < 10μm (PM10) using weighted averages of daily levels from monitors within 50km of subjects' residences. Subjects were followed for 6days for ARDS by the Berlin Criteria. The association between each pollutant and ARDS was determined using multivariable logistic regression adjusting for preselected confounders. In 764 subjects, we measured plasma concentrations of inflammatory proteins at presentation and tested for an association between pollutant exposure and protein concentration via linear regression. ARDS developed in 754 (41%) subjects. Short- and long-term exposures to SO2, NO2, and PM2.5 were associated with ARDS risk (SO2: odds ratio (OR) for the comparison of the 75-25th long-term exposure percentile 1.43 (95% confidence interval (CI) 1.16, 1.77); p < 0.01; NO2: 1.36 (1.06, 1.74); p = 0.04, PM2.5: 1.21 (1.04, 1.41); p = 0.03). Long-term exposures to these three pollutants were also associated with plasma interleukin-1 receptor antagonist and soluble tumor necrosis factor receptor-1 concentrations. Short and long-term exposures to ambient SO2, PM2.5, and NO2 are associated with increased ARDS risk in sepsis, representing potentially modifiable environmental risk factors for sepsis-associated ARDS.

Xanthine oxidoreductase gene polymorphisms are associated with high risk of sepsis and organ failure

BackgroundSepsis and associated organ failures confer substantial morbidity and mortality. Xanthine oxidoreductase (XOR) is implicated in the development of tissue oxidative damage in a wide variety of respiratory and cardiovascular disorders including sepsis and sepsis-associated acute respiratory distress syndrome (ARDS). We examined whether single nucleotide polymorphisms (SNPs) in the XDH gene (encoding XOR) might influence susceptibility to and outcome in patients with sepsis.MethodsWe genotyped 28 tag SNPs in XDH gene in the CELEG cohort, including 621 European American (EA) and 353 African American (AA) sepsis patients. Serum XOR activity was measured in a subset of CELEG subjects. Additionally, we assessed the functional effects of XDH variants utilizing empirical data from different integrated software tools and datasets.ResultsAmong AA patients, six intronic variants (rs206805, rs513311, rs185925, rs561525, rs2163059, rs13387204), in a region enriched with regulatory elements, were associated with risk of sepsis (P < 0.008–0.049). Two out of six SNPs (rs561525 and rs2163059) were associated with risk of sepsis-associated ARDS in an independent validation cohort (GEN-SEP) of 590 sepsis patients of European descent. Two common SNPs (rs1884725 and rs4952085) in tight linkage disequilibrium (LD) provided strong evidence for association with increased levels of serum creatinine (Padjusted<0.0005 and 0.0006, respectively), suggesting a role in increased risk of renal dysfunction. In contrast, among EA ARDS patients, the missense variant rs17011368 (I703V) was associated with enhanced mortality at 60-days (P < 0.038). We found higher serum XOR activity in 143 sepsis patients (54.5 ± 57.1 mU/mL) compared to 31 controls (20.9 ± 12.4 mU/mL, P = 1.96 × 10− 13). XOR activity was associated with the lead variant rs185925 among AA sepsis patients with ARDS (P < 0.005 and Padjusted<0.01). Multifaceted functions of prioritized XDH variants, as suggested by various functional annotation tools, support their potential causality in sepsis.ConclusionsOur findings suggest that XOR is a novel combined genetic and biochemical marker for risk and outcome in patients with sepsis and ARDS.

Mechanism of Xuebijing Injection in treatment of sepsis-associated ARDS based on network pharmacology and in vitro experiment

The aim of this study was to investigate the effect and molecular mechanism of Xuebijing Injection in the treatment of sepsis-associated acute respiratory distress syndrome(ARDS) based on network pharmacology and in vitro experiment. The active components of Xuebijing Injection were screened and the targets were predicted by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). The targets of sepsis-associated ARDS were searched against GeneCards, DisGeNet, OMIM, and TTD. Weishengxin platform was used to map the targets of the main active components in Xuebijing Injection and the targets of sepsis-associated ARDS, and Venn diagram was established to identify the common targets. Cytoscape 3.9.1 was used to build the &quot;drug-active components-common targets-disease&quot; network. The common targets were imported into STRING for the building of the protein-protein interaction(PPI) network, which was then imported into Cytoscape 3.9.1 for visualization. DAVID 6.8 was used for Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment of the common targets, and then Weishe-ngxin platform was used for visualization of the enrichment results. The top 20 KEGG signaling pathways were selected and imported into Cytoscape 3.9.1 to establish the KEGG network. Finally, molecular docking and in vitro cell experiment were performed to verify the prediction results. A total of 115 active components and 217 targets of Xuebijing Injection and 360 targets of sepsis-associated ARDS were obtained, among which 63 common targets were shared by Xuebijing Injection and the disease. The core targets included interleukin-1 beta(IL-1β), IL-6, albumin(ALB), serine/threonine-protein kinase(AKT1), and vascular endothelial growth factor A(VEGFA). A total of 453 GO terms were annotated, including 361 terms of biological processes(BP), 33 terms of cellular components(CC), and 59 terms of molecular functions(MF). The terms mainly involved cellular response to lipopolysaccharide, negative regulation of apoptotic process, lipopolysaccharide-mediated signaling pathway, positive regulation of transcription from RNA polyme-rase Ⅱ promoter, response to hypoxia, and inflammatory response. The KEGG enrichment revealed 85 pathways. After diseases and generalized pathways were eliminated, hypoxia-inducible factor-1(HIF-1), tumor necrosis factor(TNF), nuclear factor-kappa B(NF-κB), Toll-like receptor, and NOD-like receptor signaling pathways were screened out. Molecular docking showed that the main active components of Xuebijing Injection had good binding activity with the core targets. The in vitro experiment confirmed that Xuebijing Injection suppressed the HIF-1, TNF, NF-κB, Toll-like receptor, and NOD-like receptor signaling pathways, inhibited cell apoptosis and reactive oxygen species generation, and down-regulated the expression of TNF-α, IL-1β, and IL-6 in cells. In conclusion, Xuebijing Injection can regulate apoptosis and response to inflammation and oxidative stress by acting on HIF-1, TNF, NF-κB, Toll-like receptor, and NOD-like receptor signaling pathways to treat sepsis-associated ARDS.

BMSC-Derived Exosomes Alleviate Sepsis-Associated Acute Respiratory Distress Syndrome by Activating the Nrf2 Pathway to Reverse Mitochondrial Dysfunction.

Type II alveolar epithelial cell (AECII) apoptosis is one of the most vital causes of sepsis-induced acute respiratory distress syndrome (ARDS). Recent evidence has proved that bone mesenchymal stem cell-derived exosomes (BMSC-exos) can effectively reduce sepsis-induced ARDS. However, the function and molecular mechanism of BMSC-exos in sepsis-induced AECII apoptosis remain to be elucidated. In the present study, a more significant number of AECII apoptosis, high mitochondrial fission p-Drp1 protein levels, and low levels of mitochondrial biogenesis-related PGC1α, Tfam, and Nrf1 proteins accompanied with ATP content depression were confirmed in AECIIs in response to sepsis. Surprisingly, BMSC-exos successfully recovered mitochondrial biogenesis, including the upregulated expression of PGC1α, Tfam, Nrf1 proteins, and ATP contents, and prohibited p-Drp1-mediated mitochondrial fission by promoting Nrf2 expression. However, the aforementioned BMSC-exo reversal of mitochondrial dysfunction in AECIIs can be blocked by Nrf2 inhibitor ML385. Finally, BMSC-exos ameliorated the mortality rate, AECII apoptosis, inflammatory cytokine storm including HMGB1 and IL-6, and pathological lung damage in sepsis mice, which also could be prevented by ML385. These findings reveal a new mechanism of BMSC-exos in reversing mitochondrial dysfunction to alleviate AECII apoptosis, which may provide novel strategies for preventing and treating sepsis-induced ARDS.

MACHINE LEARNING MODELS FOR PREDICTING ACUTE KIDNEY INJURY IN PATIENTS WITH SEPSIS-ASSOCIATED ACUTE RESPIRATORY DISTRESS SYNDROME.

Background: Acute kidney injury (AKI) is a prevalent and serious complication among patients with sepsis-associated acute respiratory distress syndrome (ARDS). Prompt and accurate prediction of AKI has an important role in timely intervention, ultimately improving the patients' survival rate. This study aimed to establish machine learning models to predict AKI via thorough analysis of data derived from electronic medical records. Method: The data of eligible patients were retrospectively collected from the Medical Information Mart for Intensive Care III database from 2001 to 2012. The primary outcome was the development of AKI within 48 hours after intensive care unit admission. Four different machine learning models were established based on logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The performance of all predictive models was evaluated using the area under receiver operating characteristic curve, precision-recall curve, confusion matrix, and calibration plot. Moreover, the discrimination ability of the machine learning models was compared with that of the Sequential Organ Failure Assessment (SOFA) model. Results; Among 1,085 sepsis-associated ARDS patients included in this research, 375 patients (34.6%) developed AKI within 48 hours after intensive care unit admission. Twelve predictive variables were selected and further used to establish the machine learning models. The XGBoost model yielded the most accurate predictions with the highest area under receiver operating characteristic curve (0.86) and accuracy (0.81). In addition, a novel shiny application based on the XGBoost model was established to predict the probability of developing AKI among patients with sepsis-associated ARDS. Conclusions: Machine learning models could be used for predicting AKI in patients with sepsis-associated ARDS. Accordingly, a user-friendly shiny application based on the XGBoost model with reliable predictive performance was released online to predict the probability of developing AKI among patients with sepsis-associated ARDS.

Decreased urine output is associated with increased in-hospital mortality from sepsis-associated acute respiratory distress syndrome

The relationship between urine output (UO) and in-hospital mortality in patients with sepsis-associated acute respiratory distress syndrome (ARDS) has not been elucidated. The demographic and clinical characteristics of patients from the intensive care unit with sepsis-associated ARDS in the Medical Information Mart for Intensive Care-IV database were collected, and binomial logistic regression was performed to determine whether UO was an independent risk factor for in-hospital death. Using the Logistic Organ Dysfunction System (LODS) and Sequential Organ Failure Assessment (SOFA) as a reference, receiver operating characteristic (ROC) curves were drawn to analyze the efficacy of UO in predicting in-hospital mortality, and the Kaplan-Meier curve was drawn with the optimal cut-off value of the ROC curve. Decision curve analysis (DCA) was performed to assess the clinical net benefit of UO in predicting in-hospital mortality. UO was an independent risk factor for in-hospital mortality in patients with sepsis-associated ARDS. The area under the ROC (AUC) for UO in predicting in-hospital mortality was 0.712, which was comparable to LODS and SOFA. The patients were grouped by the optimal UO cut-off value (1515 mL/day) identified by the ROC curve. The results showed that the median in-hospital survival time for the low-UO group was 20.565 days, and that of the high-UO group was 84.670 days. The risk of in-hospital death of the low-UO group was 3.0792 times that of the high-UO group. DCA showed that when using UO to predict in-hospital mortality, the clinical net benefit was higher than LODS or SOFA at almost all available threshold probabilities, particularly when the threshold probability was between 0.2 and 0.4. As a result, UO showed moderate efficacy in predicting in-hospital mortality, and when used to predict the in-hospital mortality of patients with sepsis-related ARDS, its clinical net benefit was higher than that of LODS or SOFA.

Screening study of peripheral blood biomarkers in sepsis-associated ARDS

Objective: Acute respiratory distress syndrome (ARDS) is a serious and rapidly progressive complication of sepsis with a very high mortality rate. Screening for reliable biomarkers can assist in the diagnosis and treatment of the disease, and the development of personalized immunotherapy regimens may provide great clinical benefit. Method: The clinical data of 110 patients with sepsis treated in our medical unit from November 2018 to January 2020 were reviewed through a single-center retrospective analytic study. Using the Statistical Package for Social Science (SPSS, version 26) and the software GraphPad Prism 7, statistical analyses were performed and graphs were generated. Mean (standard deviation [SD]) and median (interquartile range [IQR]) should be used for description of normally and non-normally distributed data, respectively. The normal and non-normal distributed quantitative variables were respectively assessed by independent T-test or Chi-square test. P-value <0. 05 noted statistical significance. Results: (1) Finally, 32 patients were enrolled in the study, among 32 patients with sepsis-associated ARDS, there were 13 cases in the survival group and 19 cases in the death group. There were 10 males and 3 females in the survival group, with an age of 61. 77±14. 296 years. There were 13 males and 6 females in the death group, with an age of 66. 11±11. 455 years. There was no statistically significant difference between the two groups in terms of age and gender (P>0. 05). (2) The difference in PaO2/FiO2 between the two groups was statistically significant (P<0. 05). Their AUC was 0. 8057, and the sensitivity and specificity were 89. 47% and 69. 23% respectively. (3) By comparison of median scatter plots, cytokines IL-6, IL-6/IL-10, admission NLR, and discharge NLR were higher in the death group compared with survival. IL-10 and IFN-γ were lower compared with survival. (4) The results of logistic regression analysis showed risk factors affecting the prognosis of sepsis-associated ARDS:PaO2/FiO2 (P=0. 019, OR:12. 289, 95% CI:1. 512 - 99. 8);Protective factor: IFN-γ(P=0. 03, OR:0. 161, 95% CI:0. 031 - 0. 834 ). Conclusion: (1) PaO2/FiO2 can be used to assess the prognosis of septic ARDS patients; (2) cytokine assay may be a guide to determine the prognosis of septic ARDS; (3) IFN-γmay be a protective factor in the course of septic ARDS patients.

Using machine learning for the early prediction of sepsis-associated ARDS in the ICU and identification of clinical phenotypes with differential responses to treatment.

Background: An early diagnosis model with clinical phenotype classification is key for the early identification and precise treatment of sepsis-associated acute respiratory distress syndrome (ARDS). This study aimed to: 1) build a machine learning diagnostic model for patients with sepsis-associated ARDS using easily accessible early clinical indicators, 2) conduct rapid classification of clinical phenotypes in this population, and 3) explore the differences in clinical characteristics, outcomes, and treatment responses of different phenotypes. Methods: This study is based on data from the Telehealth Intensive Care Unit (eICU) and Medical Information Mart for Intensive Care IV (MIMIC-IV). We trained and tested the early diagnostic model of sepsis-associated ARDS patients in the eICU. We used key predictive indicators to cluster sepsis-associated ARDS patients and determine the characteristics and clinical outcomes of different phenotypes, as well to explore the differences of in-hospital mortality among different the positive end-expiratory pressure (PEEP) levels in different phenotypes. These results are verified in MIMIC-IV to evaluate whether they are repeatable. Results: Among the diagnostic models constructed in 19,249 sepsis patients and 5,947 sepsis-associated ARDS patients, the AdaBoost (Decision Tree) model achieved the best performance with an area under the receiver operating characteristic curve (AUC) of 0.895, which is higher than that of the traditional Logistic Regression model (Z = -2.40,p = 0.013), and the accuracy of 70.06%, sensitivity of 78.11% and specificity of 78.74%. We simultaneously identified three sepsis-associated ARDS phenotypes. Cluster 0 (n = 3,669) had the lowest in-hospital mortality rate (6.51%) and fewer laboratory abnormalities (lower WBC (median:15.000K/mcL), lower blood glucose (median:158.000mg/dl), lower creatinine (median:1.200mg/dl), lower lactic acid (median:3.000mmol/L); p < 0.001). Cluster 1 (n = 1,554) had the highest in-hospital mortality rate (75.29%) and the most laboratory abnormalities (higher WBC (median:18.300K/mcL), higher blood glucose (median:188.000mg/dl), higher creatinine (median:2.300mg/dl), higher lactic acid (median:3.900mmol/L); p < 0.001). Cluster 2 (n = 724) had the most complex condition, with a moderate in-hospital mortality rate (29.7%) and the longest intensive care unit stay. In Clusters 0 and 1, patients with high PEEP had higher in-hospital mortality rate than those with low PEEP, but the opposite trend was seen in Cluster 2. These results were repeatable in 11,935 patients with sepsis and 2,699 patients with sepsis-associated ARDS patients in the MIMIC-IV cohort. Conclusion: A machine learning diagnostic model of sepsis-associated ARDS patients was established. Three phenotypes with different clinical features and outcomes were clustered, and these had different therapeutic responses. These findings are helpful for the early and rapid identification of sepsis-associated ARDS patients and their precise individualized treatment.

Role of CD8+ T cell exhaustion in the progression and prognosis of acute respiratory distress syndrome induced by sepsis: a prospective observational study

BackgroundCD8+ T cells are important for protective immunity against intracellular pathogens. Excessive amounts of antigen and/or inflammatory signals often lead to the gradual deterioration of CD8+ T cell function, a state called “exhaustion”. However, the association between CD8+ T cell exhaustion and acute respiratory distress syndrome (ARDS) has not been studied. This study was conducted to elucidate how CD8+ T cells and inhibitory receptors were related to the clinical prognosis of ARDS.MethodsA prospective observational study in an emergency department enrolled patients who were diagnosed with sepsis-associated ARDS according to the sepsis-3 criteria and Berlin definition. Peripheral blood samples were collected within 24 h post recruitment. CD8+ T cell count, proliferation ratio, cytokine secretion, and the expression of coinhibitory receptors were assayed.ResultsSixty-two patients with ARDS met the inclusion criteria. CD8+ T cell counts and proliferation rates were dramatically decreased in non-surviving ARDS patients. Increasing programmed cell death 1 (PD-1) expression on the CD8+ T cell surface was seen in patients with worse organ function, while an increasing level of T cell immunoglobulin mucin-3 (Tim-3) was associated with a longer duration of the shock. Kaplan–Meier analysis showed that low CD8+ T cell percentages and increased inhibitory molecule expression were significantly associated with a worse survival rate.ConclusionsCD8+ T cells and coinhibitory receptors are promising independent prognostic markers of sepsis-induced ARDS, and increased CD8+ T cell exhaustion is significantly correlated with poor prognosis.

APELIN-13 AMELIORATES LPS-INDUCED ENDOTHELIAL-TO-MESENCHYMAL TRANSITION AND POST-ACUTE LUNG INJURY PULMONARY FIBROSIS BY SUPPRESSING TRANSFORMING GROWTH FACTOR-Β1 SIGNALING.

The pathophysiology of acute respiratory distress syndrome (ARDS) involves cytokine storms, alveolar-capillary barrier destruction, and fibrotic progression. Pulmonary interstitial fibrosis is an important factor affecting the prognosis of ARDS patients. Endothelial-to-mesenchymal transition (EndMT) plays an important role in the development of fibrotic diseases, and the occurrence of EndMT has been observed in experimental models of LPS-induced acute lung injury (ALI). Apelin is an endogenous active polypeptide that plays an important role in maintaining endothelial cell homeostasis and inhibiting fibrotic progression in various diseases. However, whether apelin attenuates EndMT in ALI and post-ALI pulmonary fibrosis remains unclear. We analyzed the serum levels of apelin-13 in patients with sepsis-associated ARDS to examine its possible clinical value. A murine model of LPS-induced pulmonary fibrosis and an LPS-challenged endothelial cell injury model were used to analyze the protective effect and underlying mechanism of apelin-13. Mice were treated with apelin-13 by i.p. injection, and human pulmonary microvascular endothelial cells were incubated with apelin-13 in vitro . We found that the circulating apelin-13 levels were significantly elevated in sepsis-associated ARDS patients compared with healthy controls. Our study also confirmed that LPS induced EndMT progression and pulmonary fibrosis, which were characterized by decreased CD31 expression and increased α-smooth muscle actin expression and collagen deposition. LPS also stimulated the production of transforming growth factor β1 and activated the Smad signaling pathway. However, apelin-13 treatment significantly attenuated these changes. Our findings suggest that apelin-13 may be a novel biomarker in patients with sepsis-associated ARDS. These results demonstrate that apelin-13 ameliorates LPS-induced EndMT and post-ALI pulmonary fibrosis by suppressing transforming growth factor β1 signaling.

TLR7 Mediates Acute Respiratory Distress Syndrome in Sepsis by Sensing Extracellular miR-146a.

TLR7 (Toll-like receptor 7), the sensor for single-stranded RNA, contributes to systemic inflammation and mortality in murine polymicrobial sepsis. Recent studies show that extracellular miR-146a-5p serves as a TLR7 ligand and plays an important role in regulating host innate immunity. However, the role of miR-146a-5p and TLR7 signaling in pulmonary inflammation, endothelial activation, and sepsis-associated acute respiratory distress syndrome remains unclear. Here, we show that intratracheal administration of exogenous miR-146a-5p in mice evokes lung inflammation, activates endothelium, and increases endothelial permeability via TLR7-dependent mechanisms. TLR7 deficiency attenuates pulmonary barrier dysfunction and reduces lung inflammatory response in a murine sepsis model. Moreover, the impact of miR-146a-5p-TLR7 signaling on endothelial activation appears to be a secondary effect because TLR7 is undetectable in the human pulmonary artery and microvascular endothelial cells (ECs), which show no response to direct miR-146a-5p treatment invitro. Both conditioned media of miR-146a-5p-treated macrophages (Mϕ) and septic sera of wild-type mice induce a marked EC barrier disruption invitro, whereas Mϕ conditioned media or septic sera of TLR7-/- mice do not exhibit such effect. Cytokine array and pathway enrichment analysis of the Mϕ conditioned media and septic sera identify TNFα (tumor necrosis factor α) as the main downstream effector of miR-146a-5p-TLR7 signaling responsible for the EC barrier dysfunction, which is further supported by neutralizing anti-TNFα antibody intervention. Together, these data demonstrate that TLR7 activation elicits pulmonary inflammation and endothelial barrier disruption by sensing extracellular miR-146a-5p and contributes to sepsis-associated acute respiratory distress syndrome.

Serum human epididymis secretory protein 4 correlates with sepsis-associated acute respiratory distress syndrome and 28-day mortality in critically ill patients.

Acute respiratory distress syndrome (ARDS) is a severe disease with high mortality, and its primary cause is sepsis. The aim of this study was to detect and evaluate the role of Human epididymis protein 4 (HE4) in sepsis-related ARDS. One hundred and twenty-three critically ill sepsis patients with/without ARDS and 102 healthy controls were enrolled in this study. Blood samples were collected upon admission for quantitative testing of HE4 by chemiluminescent microparticle immunoassay (CMIA). ROC curve analysis and Spearman's correlation analysis were conducted to determine the diagnostic and prognostic value of HE4. Compared with controls, the serum HE4 concentrations of sepsis patients were elevated, and levels in sepsis patients with ARDS were significantly higher (all p < 0.0001). Moreover, HE4 concentrations were strongly correlated with the clinical severity characteristics of sepsis patients, and ROC curve suggested that the AUC of HE4 applied to discriminate sepsis-ARDS patients from sepsis patients was 0.903. HE4 was also found to be a prognostic biomarker of clinical severity and 28-day mortality among critically ill sepsis patients. Logistic regression analysis showed that HE4 was an independent factor for diagnosis of ARDS. Meanwhile, ROC curve analysis showed that the cut-off value of serum HE4 to discriminate 28-day mortality from sepsis patients (AUC: 0.782) was 646.5pmol/L. The concentration of serum HE4 in patients with sepsis-related ARDS was markedly increased and was significantly correlated with mortality, which suggests that serum HE4 could be a promising diagnostic and prognostic biomarker for ARDS in sepsis patients.

Apelin-13 Attenuates Lipopolysaccharide-Induced Inflammatory Responses and Acute Lung Injury by Regulating PFKFB3-Driven Glycolysis Induced by NOX4-Dependent ROS.

PurposeAcute lung injury (ALI) is a life-threatening condition with limited therapeutic options. Macrophage inflammation plays a key role in the development of ALI. Abnormal glycolysis of macrophages contributes to the inflammatory response. However, the role of macrophage glycolysis in ALI still requires investigation. Apelin-13 has been shown to protect against ALI, whereas the underlying mechanisms remain unclear. In this study, we explored the effect of apelin-13 on lipopolysaccharide (LPS)-induced inflammation and ALI via regulation of glycolysis by modulating redox homeostasis in macrophages.MethodsSerums from 34 patients with sepsis and 13 healthy volunteers were analyzed. In vivo, the protective effect of apelin-13 against LPS-induced ALI was evaluated using a mouse model of LPS-induced ALI. In vitro, mouse bone marrow macrophages (BMDMs) were pretreated with the antioxidant, NADPH oxidase (NOX) 4 (NOX4) small-interfering RNA (siRNA), the 6-phosphofructo-2 -kinase/fructose- 2,6-biphosphatase 3 (PFKFB3) siRNA, or the PFKFB3 overexpression plasmid before exposure to LPS.ResultsSerum apelin-13 levels were significantly elevated in patients with sepsis and sepsis-associated acute respiratory distress syndrome (ARDS) (P<0.0001). In vivo, apelin-13 suppressed LPS-induced ALI and inflammatory cytokine production (P<0.05). Furthermore, apelin-13 reduced hydrogen peroxide (H2O2) content, NOX4 protein levels, and glycolysis. In vitro, LPS stimulation elevated NOX4 protein levels and reactive oxygen species (ROS) production (P<0.05). These changes resulted in the accumulation of glycolysis in BMDMs. Treatment with antioxidant or NOX4 siRNA inhibited LPS-induced glycolysis and inflammatory cytokine production (P<0.05). Moreover, in vitro experiments revealed that PFKFB3 regulates the release of pro-inflammatory cytokines by modulating glycolysis. In contrast, the action of apelin-13 opposed the effects of LPS.ConclusionIn conclusion, apelin-13 protects against LPS-induced inflammatory responses and ALI by regulating PFKFB3-driven glycolysis induced by NOX4-dependent ROS.