Risk factors for acute respiratory distress syndrome in sepsis patients: a retrospective study from a tertiary hospital in China

Predicting mortality and risk factors of sepsis related ARDS using machine learning models

Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic. Accurate prediction of in-hospital mortality of patients is crucial to optimize treatment and improve prognosis under the new global definition of ARDS. Our study aimed to use machine learning models to develop models that can effectively predict the in-hospital mortality of patients with sepsis related ARDS, calculate the mortality, and to identify related risk factors under the new global definition of ARDS. Based on MIMIC database, our study included 3470 first-time admission records of patients with sepsis related ARDS. After excluding 4 patients under the age of 18, 75 patients with less than 24 h stay in ICU, and 5 cases with missing indicators > 30%, finally 3386 cases were retained. The variance inflation factor (VIF) analysis was used to test the collinearity of the explanatory variables. The data were divided into the training set and the test set according to the ratio of 7:3. Six models, extreme gradient boosting (XGBoost), light gradient boosting (LightGBM), random forest (RF), classification and regression tree (CART), naive bayes (NB) and logistic regression (LR), were designed for training and testing. In the training set, XGBoost (AUROC = 0.951, 95% CI 0.942-0.961), LR (AUROC = 0.835, 95% CI 0.817-0.854), RF (AUROC = 1.0, 95% CI 1.0-1.0), LightGBM (AUROC = 1.0, 95% CI 1.0-1.0), CART (AUROC = 0.831, 95% CI 0.811-0.852), NB (AUROC = 0.793, 95% CI 0.772-0.814). In the test set, XGBoost (AUROC = 0.833, 95% CI 0.804-0.861), LR (AUROC = 0.82695% CI 0.796-0.856), RF (AUROC = 0.846, 95% CI 0.818-0.874), LightGBM (AUROC = 0.827, 95% CI 0.798-0.856), CART (AUROC = 0.753, 95% CI 0.718-0.787), NB (AUROC = 0.799, 95% CI 0.768-0.831). The RF model has the best performance on the test set. Further analyze the feature importance ranking and partial dependence plots of random forest model. Acute physiology and chronic health evaluation III (APACHE III), bicarbonate, anion gap and non-invasive blood pressure systolic were identified as the four most important risk characteristics. In this study, a variety of machine learning models have been successfully constructed to predict the in-hospital mortality of patients with sepsis related ARDS, among which the RF model performs well. Key risk factors identified include APACHE III, bicarbonate, anion gap and non-invasive blood pressure systolic. The identification of these factors helps clinicians to assess patients' conditions more accurately and develop personalized treatment plans, thereby improving the survival rate and prognosis quality of patients under the new global definition of ARDS.

Chest radiographs in acute respiratory distress syndrome: an Achilles' heel of the Berlin criteria?

Background

Despite the high mortality and economic burden associated with the acute respiratory distress syndrome (ARDS), the role of chest radiograph (CXR) in ARDS diagnosis and prognosis remains uncertain. The purpose of this study is to elucidate clinical characteristics that distinguish ARDS patients from those without ARDS, especially in patients where CXRs are indicative of ARDS.

Methods

Secondary analysis of a prospective observational study with 454 postoperative septic patients under mechanical ventilation (MV). Patients were stratified in two groups depending on whether they met the Berlin criteria for ARDS. Primary outcome was identification of clinical characteristics differentiating patients with ARDS confirmed by CXR from non-ARDS patients. Secondary outcome was 60-day in-hospital mortality of postoperative sepsis-induced ARDS.

Results

One hundred thirty-nine patients (30.6%) had CXRs compatible with ARDS, although ARDS was confirmed in only 45 patients (9.9%). Emergency surgery (OR 6.6), abdominal source of infection (OR 6.0), pneumonia (OR 8.2), and higher lactate (OR 3.9) were clinical features associated with ARDS development confirmed by CXR. ARDS was an independent risk factor for 60-day mortality (OR 1.8).

Conclusion

Although CXR criteria for ARDS diagnosis could be replaced in future definitions, its importance for ARDS diagnosis should not be underestimated.

Autophagy-related biomarkers identified in sepsis-induced ARDS through bioinformatics analysis

While dysregulated autophagy has been linked to acute respiratory distress syndrome (ARDS) development in sepsis, the exact regulatory mechanisms driving this process remain unclear. This study systematically investigated autophagy-related genes in sepsis-induced ARDS using integrative bioinformatics, including weighted gene coexpression network analysis (WGCNA), differential gene expression analysis (DEGs), receiver operating characteristic (ROC) curve analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein‒protein interaction (PPI) network analysis, and immune infiltration analysis. Hub genes were further validated by qPCR in Beas-2B cells receiving lipopolysaccharide (LPS) stimulation. We identified 18 autophagy-related DEGs with diagnostic potential for sepsis-induced ARDS. These DEGs were linked to endocytosis, protein kinase inhibition, and enigmatic Ficolin-1-rich granules. The downregulated hallmark signaling pathways involved apoptosis, complement, IL-2/STAT5, and KRAS signaling. Immune infiltration analysis revealed alterations in 7 immune cell subsets, including CD8 + T-cell exhaustion, natural killer cell reduction, and the type 1 helper T-cell response. When Beas-2B cells were treated with LPS, we discovered that 6 out of the 18 hub genes were significantly downregulated. Our findings provide novel insights into autophagy-mediated ARDS pathogenesis in sepsis. The hub genes represent promising candidates for clinical biomarker development and therapeutic targeting, which necessitates further validation.

Acute respiratory distress syndrome (ARDS): from mechanistic insights to therapeutic strategies

Acute respiratory distress syndrome (ARDS) is a clinical syndrome of acute hypoxic respiratory failure caused by diffuse lung inflammation and edema. ARDS can be precipitated by intrapulmonary factors or extrapulmonary factors, which can lead to severe hypoxemia. Patients suffering from ARDS have high mortality rates, including a 28-day mortality rate of 34.8% and an overall in-hospital mortality rate of 40.0%. The pathophysiology of ARDS is complex and involves the activation and dysregulation of multiple overlapping and interacting pathways of systemic inflammation and coagulation, including the respiratory system, circulatory system, and immune system. In general, the treatment of inflammatory injuries is a coordinated process that involves the downregulation of proinflammatory pathways and the upregulation of anti-inflammatory pathways. Given the complexity of the underlying disease, treatment needs to be tailored to the problem. Hence, we discuss the pathogenesis and treatment methods of affected organs, including 2019 coronavirus disease (COVID-19)-related pneumonia, drowning, trauma, blood transfusion, severe acute pancreatitis, and sepsis. This review is intended to provide a new perspective concerning ARDS and offer novel insight into future therapeutic interventions.

Clinical significance of lactate-to-albumin ratio in patients with influenza A virus-induced acute respiratory distress syndrome: a single-center retrospective study

BACKGROUND

The lactate-to-albumin ratio (LAR) is predictive of disease prognosis in some cases. However, the clinical significance of LAR in patients with influenza A virus-induced acute respiratory distress syndrome (ARDS) has yet to be explored. This study aims to investigate whether LAR can be used as a predictor of influenza A virus-induced ARDS.

METHODS

In this single-center retrospective study, we enrolled 105 patients with influenza A virus pneumonia into the study and divided the patients into an ARDS group (74 patients) and a non-ARDS group (31 patients) during hospitalization. Clinical characteristics and laboratory data were collected within 24 h after admission. We explored the risk factors for ARDS using logistic regression analysis. The predictive performance of potential risk factors for ARDS and ARDS-associated complications were evaluated by receiver operating characteristic (ROC) curves, and Pearson's correlation analysis was used to evaluate the correlations between risk factors and clinical and laboratory variables.

RESULTS

LAR was an independent predictor for the development of ARDS in patients with influenza A virus pneumonia and was significantly predictive for ARDS. LAR's area under the curve (AUC) was higher than that of lactate and albumin alone; its AUC was 0.878, with a sensitivity of 71.6% and a specificity of 96.8%. The optimal ROC threshold for distinguishing ARDS from non-ARDS cases was 44.81 × 10- 3. Correlation analysis indicated that LAR was positively associated with duration of invasive ventilation, and APACHE II and SOFA scores in ARDS patients but was negatively associated with PaO2/FiO2 (p < 0.001). Subsequent ROC curve analysis determined that LAR was a robust predictor for the 14-day invasive ventilation (AUC = 0.924), septic shock (AUC = 0.860), and hepatic injury (AUC = 0.905) in hospitalized ARDS patients. It also showed a promising predictive value for 28-day mortality (AUC = 0.881).

CONCLUSION

LAR strongly predicted ARDS development in patients with influenza A virus pneumonia. It showed a significant correlation with disease severity and provided promising predictive efficiency for extrapulmonary complications and 28-day mortality in patients with influenza A virus-induced ARDS.

Risk factors for acute respiratory distress syndrome in sepsis patients: A meta-analysis

Background

Acute Respiratory Distress Syndrome (ARDS) is a critical complication of sepsis, associated with high morbidity and mortality. Identifying risk factors for ARDS among sepsis patients is essential for early intervention and improving outcomes.

Methods

We conducted a comprehensive meta-analysis, reviewing studies that examined the association between various risk factors and ARDS development in sepsis patients. Databases such as PubMed, EMBASE, Cochrane Library, Medline, CINAHL, and Web of Science were searched up to January 2024, without language restrictions. Eligible studies included observational cohorts and case-control studies. Pooled odds ratios (ORs) and standardized mean differences (SMDs) were calculated using a random-effects model. Heterogeneity was assessed through I2 statistics, and publication bias was evaluated via the Luis Furuya-Kanamori (LFK) index.

Results

15 studies with more than 40,000 participants were analyzed. Significant risk factors for ARDS included pulmonary infection (OR: 2.696, 95 % CI: 1.655 to 4.390), septic shock (OR: 2.627, 95 % CI: 1.850 to 3.731), and pancreatitis (OR: 3.734, 95 % CI: 2.958 to 4.712). No significant associations were found between the development of ARDS in septic patients and the following risk factors: sex (OR: 1.106, 95%CI: 0.957-1.279), smoking status (OR: 1.214, 95%CI: 0.835-1.765), or steroid use (OR: 0.901, 95%CI: 0.617-1.314). APACHE-II and SOFA scores were predictive of ARDS development, emphasizing their utility in clinical assessments.

Conclusion

Pulmonary infection, septic shock, and pancreatitis significantly increase ARDS risk in sepsis patients. Our findings advocate for targeted management of these risk factors to mitigate ARDS development, emphasizing the importance of personalized care in sepsis management.

Pharmacological effects of specialized pro-resolving mediators in sepsis-induced organ dysfunction: a narrative review

Sepsis is a life-threatening syndrome of organ dysfunction, characterized by uncontrolled inflammatory response and immune dysregulation, often leading to multiple organ failure and even death. Specialized pro-resolving mediators (SPMs), which are typically thought to be formed via consecutive steps of oxidation of polyenoic fatty acids, have been shown to suppress inflammation and promote timely resolution of inflammation. They are mainly divided into four categories: lipoxins, resolvins, protectins, and maresins. The SPMs may improve the prognosis of sepsis by modulating the immune and inflammatory balance, thereby holding promise for clinical applications. However, their biosynthetic and pharmacological properties are very complex. Through a literature review, we aim to comprehensively elucidate the protective mechanisms of different SPMs in sepsis and its organ damage, in order to provide sufficient theoretical basis for the future clinical translation of SPMs.

Single-center nomogram model for sepsis complicated by acute lung injury

OBJECTIVE

To construct and validate a nomogram model for predicting sepsis complicated by acute lung injury (ALI).

METHODS

The healthcare records of 193 sepsis patients hospitalized at The Affiliated Tai'an City Central Hospital of Qingdao University from January 2022 to December 2023 were retrospectively reviewed. Among these patients, 69 were in the ALI group and 124 in the non-ALI group. A nomogram prediction model was constructed using logistic regression analysis. Its predictive performance was evaluated through various measures, including the area under the curve (AUC), calibration curve, decision curve, sensitivity, specificity, accuracy, recall rate, and precision rate.

RESULTS

The predictive factors included the neutrophil/lymphocyte ratio (NLR), oxygenation index (PaO2/FiO2), tumor necrosis factor-α (TNF-α), and acute physiology and chronic health evaluation II (APACHE II). The nomogram training set achieved an AUC of 0.959 (95% CI: 0.924-0.995), an accuracy of 92.59%, a recall of 96.70%, and a precision of 92.63%. In the validation set, the AUC was 0.938 (95% CI: 0.880-0.996), with an accuracy of 89.66%, a recall of 93.94%, and a precision of 88.57%. The calibration curve demonstrated that the prediction results were consistent with the actual findings. The decision curve indicated that the model has clinical applicability.

CONCLUSION

NLR, PaO2/FiO2, TNF-α, and APACHE II are closely associated with ALI in sepsis patients. A nomogram model based on these four variables shows strong predictive performance and may be used as a clinical decision-support tool to help physicians better identify high-risk groups.

MALAT1 predicts the prognosis of severe community-acquired pneumonia in pediatric patients

BACKGROUND

To evaluate the role of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in the prognosis of severe community-acquired pneumonia (CAP) in children.

METHODS

According to the median MALAT1 value of 3.2 at baseline, 93 pediatric patients with severe CAP were divided into low (n = 46, median MALAT1 level = 1.9) or high (n = 47, median MALAT1 level = 4.5) MALAT1 groups. Another 93 age-, gender-, and body mass index (BMI)-matched healthy individuals were included in the control group using the propensity-score matching (PSM) method. A multivariate Cox proportional hazards model was used to explore the association of MALAT1 level with the 28-day mortality after controlling for potential confounding factors.

RESULTS

The MALAT1 expressions were significantly higher in the patients with severe CAP compared with those in the healthy controls (3.2 vs. 0.9, P < 0.01). The receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) was 0.927 when the cut-off value of MALAT1 was 1.5. Moreover, the MALAT1 expressions were substantially lower in survivals than non-survivals (3.8 vs. 2.6, P < 0.01), and the multivariate Cox regression analysis indicated a positive association between MALAT1 levels and mortality risk (HR = 3.32; 95% CI: 1.05-10.47; P = 0.04).

CONCLUSION

MALAT1 might be a promising marker for predicting the prognosis of severe CAP in pediatric patients.

Compartmentalization of the inflammatory response during bacterial sepsis and severe COVID-19

Acute infections cause local and systemic disorders which can lead in the most severe forms to multi-organ failure and eventually to death. The host response to infection encompasses a large spectrum of reactions with a concomitant activation of the so-called inflammatory response aimed at fighting the infectious agent and removing damaged tissues or cells, and the anti-inflammatory response aimed at controlling inflammation and initiating the healing process. Fine-tuning at the local and systemic levels is key to preventing local and remote injury due to immune system activation. Thus, during bacterial sepsis and Coronavirus disease 2019 (COVID-19), concomitant systemic and compartmentalized pro-inflammatory and compensatory anti-inflammatory responses are occurring. Immune cells (e.g., macrophages, neutrophils, natural killer cells, and T-lymphocytes), as well as endothelial cells, differ from one compartment to another and contribute to specific organ responses to sterile and microbial insult. Furthermore, tissue-specific microbiota influences the local and systemic response. A better understanding of the tissue-specific immune status, the organ immunity crosstalk, and the role of specific mediators during sepsis and COVID-19 can foster the development of more accurate biomarkers for better diagnosis and prognosis and help to define appropriate host-targeted treatments and vaccines in the context of precision medicine.

Smoking on the risk of acute respiratory distress syndrome: a systematic review and meta-analysis

BACKGROUND

The relationship between smoking and the risk of acute respiratory distress syndrome (ARDS) has been recognized, but the conclusions have been inconsistent. This systematic review and meta-analysis investigated the association between smoking and ARDS risk in adults.

METHODS

The PubMed, EMBASE, Cochrane Library, and Web of Science databases were searched for eligible studies published from January 1, 2000, to December 31, 2023. We enrolled adult patients exhibiting clinical risk factors for ARDS and smoking condition. Outcomes were quantified using odds ratios (ORs) for binary variables and mean differences (MDs) for continuous variables, with a standard 95% confidence interval (CI).

RESULTS

A total of 26 observational studies involving 36,995 patients were included. The meta-analysis revealed a significant association between smoking and an increased risk of ARDS (OR 1.67; 95% CI 1.33-2.08; P < 0.001). Further analysis revealed that the associations between patient-reported smoking history and ARDS occurrence were generally similar to the results of all the studies (OR 1.78; 95% CI 1.38-2.28; P < 0.001). In contrast, patients identified through the detection of tobacco metabolites (cotinine, a metabolite of nicotine, and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), a metabolite of tobacco products) showed no significant difference in ARDS risk (OR 1.19; 95% CI 0.69-2.05; P = 0.53). The smoking group was younger than the control group (MD - 7.15; 95% CI - 11.58 to - 2.72; P = 0.002). Subgroup analysis revealed that smoking notably elevated the incidence of ARDS with extrapulmonary etiologies (OR 1.85; 95% CI 1.43-2.38; P < 0.001). Publication bias did not affect the integrity of our conclusions. Sensitivity analysis further reinforced the reliability of our aggregated outcomes.

CONCLUSIONS

There is a strong association between smoking and elevated ARDS risk. This emphasizes the need for thorough assessment of patients' smoking status, urging healthcare providers to vigilantly monitor individuals with a history of smoking, especially those with additional extrapulmonary risk factors for ARDS.

Post-operative sepsis-induced acute respiratory distress syndrome: risk factors for a life-threatening complication

Introduction

Prevalence and mortality of the acute respiratory distress syndrome (ARDS) in intensive care units (ICU) are unacceptably high. There is scarce literature on post-operative sepsis-induced ARDS despite that sepsis and major surgery are conditions associated with ARDS. We aimed to examine the impact of post-operative sepsis-induced ARDS on 60-day mortality.

Methods

We performed a secondary analysis of a prospective observational study in 454 patients who underwent major surgery admitted into a single ICU. Patients were stratified in two groups depending on whether they met criteria for ARDS. Primary outcome was 60-day mortality of post-operative sepsis-induced ARDS. Secondary outcome measures were potential risk factors for post-operative sepsis-induced ARDS, and for 60-day mortality.

Results

Higher SOFA score (OR 1.1, 95% CI 1.0-1.3, p = 0.020) and higher lactate (OR 1.9, 95% CI 1.2-2.7, p = 0.004) at study inclusion were independently associated with ARDS. ARDS patients (n = 45) had higher ICU stay [14 (18) vs. 5 (11) days, p < 0.001] and longer need for mechanical ventilation [6 (14) vs. 1 (5) days, p < 0.001] than non-ARDS patients (n = 409). Sixty-day mortality was higher in ARDS patients (OR 2.7, 95% CI 1.1-6.3, p = 0.024). Chronic renal failure (OR 4.0, 95% CI 1.2-13.7, p = 0.026), elevated lactate dehydrogenase (OR 1.7, 95% CI 1.1-2.7, p = 0.015) and higher APACHE II score (OR 2.7, 95% CI 1.3-5.4, p = 0.006) were independently associated with 60-day mortality.

Conclusion

Post-operative sepsis-induced ARDS is associated with higher 60-day mortality compared to non-ARDS post-operative septic patients. Post-operative septic patients with higher severity of illness have a greater risk of ARDS and worse outcomes. Further investigation is needed in post-operative sepsis-induced ARDS to prevent ARDS.

Significance of sTREM-1 and sST2 combined diagnosis for sepsis detection and prognosis prediction

The diagnosis of sepsis still lacks a practical and reliable gold standard. The purpose of this study was to confirm the effect of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) combined with soluble suppression of tumorigenicity 2 (sST2) in the diagnosis of sepsis through the correlation between sTREM-1, sST2, and sequential organ failure assessment (SOFA) scores. Baseline data of 91 patients with sepsis in the intensive care unit were collected, sTREM-1 and sST2 were detected, and the correlation between markers and SOFA score was analyzed. Besides, the prognostic value of baseline and postadmission indicators for sepsis was analyzed with death as the outcome. The results showed that the expressions of sST2 and sTREM-1 in death group and survival group were higher than those in the survival group (p < 0.05). Correlation analysis showed that sST2, sTREM-1, and the joint diagnosis model had a high correlation with SOFA score (p < 0.05), but poor correlation with Acute Physiology and Chronic Health Evaluation Ⅱ score (p > 0.05). Among them, joint diagnosis model has the highest correlation. Receiver operating characteristic curve analysis showed that combined diagnosis had higher area under curve values. sTREM-1/sST2 can be better used in the diagnosis of sepsis than the single biomarker detection, and the combination of the above two biomarkers has potential application value in the detection and prognosis prediction of sepsis.

A Systematic Review and Meta-Analysis of Independent Predictors for Acute Respiratory Distress Syndrome in Patients Presenting With Sepsis

The current meta-analysis was conducted to determine the predictors of acute respiratory distress syndrome (ARDS) in patients with sepsis. The present meta-analysis was conducted in accordance with the MOOSE (Meta-analysis of Observational Studies in Epidemiology) guidelines. We conducted a systematic search using the PubMed, Cochrane Library, and EMBASE databases for studies published between 1 January 2000 and 28 February 2023 that assessed the predictors of ARDS in patients with sepsis. We used key terms such as "predictors," "acute respiratory distress syndrome," and "sepsis" to search for relevant articles. Our search was limited to human studies published in English. A total of six studies were included in this meta-analysis. Of the six studies, four were retrospective and two were prospective. The pooled incidence of ARDS was 11.27%. We identified six factors with a consistent and statistically significant association with ARDS, including sequential organ failure assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation (APACHE) II score, pulmonary sepsis, smoking, pancreatitis, and C-reactive protein. Age, diabetes, and chronic obstructive pulmonary disease (COPD) were not found to be significantly associated with ARDS in this patient population. It is important for healthcare providers to consider these predictors when assessing patients with sepsis and septic shock to identify those at high risk for developing ARDS and implement appropriate preventive measures.

A prediction model for predicting the risk of acute respiratory distress syndrome in sepsis patients: a retrospective cohort study

BACKGROUND

The risk of death in sepsis patients with acute respiratory distress syndrome (ARDS) was as high as 20-50%. Few studies focused on the risk identification of ARDS among sepsis patients. This study aimed to develop and validate a nomogram to predict the ARDS risk in sepsis patients based on the Medical Information Mart for Intensive Care IV database.

METHODS

A total of 16,523 sepsis patients were included and randomly divided into the training and testing sets with a ratio of 7:3 in this retrospective cohort study. The outcomes were defined as the occurrence of ARDS for ICU patients with sepsis. Univariate and multivariate logistic regression analyses were used in the training set to identify the factors that were associated with ARDS risk, which were adopted to establish the nomogram. The receiver operating characteristic and calibration curves were used to assess the predictive performance of nomogram.

RESULTS

Totally 2422 (20.66%) sepsis patients occurred ARDS, with the median follow-up time of 8.47 (5.20, 16.20) days. The results found that body mass index, respiratory rate, urine output, partial pressure of carbon dioxide, blood urea nitrogen, vasopressin, continuous renal replacement therapy, ventilation status, chronic pulmonary disease, malignant cancer, liver disease, septic shock and pancreatitis might be predictors. The area under the curve of developed model were 0.811 (95% CI 0.802-0.820) in the training set and 0.812 (95% CI 0.798-0.826) in the testing set. The calibration curve showed a good concordance between the predicted and observed ARDS among sepsis patients.

CONCLUSION

We developed a model incorporating thirteen clinical features to predict the ARDS risk in patients with sepsis. The model showed a good predictive ability by internal validation.

Attenuation of the Severity of Acute Respiratory Distress Syndrome by Pomiferin through Blocking Inflammation and Oxidative Stress in an AKT/Foxo1 Pathway-Dependent Manner

Acute respiratory distress syndrome (ARDS) gives rise to uncontrolled inflammatory response and oxidative stress, causing very high mortality globally. Pomiferin is a kind of prenylated isoflavonoid extracted from Maclura pomifera, owning anti-inflammatory and antioxidant properties. However, the functions and possible mechanisms of pomiferin in lipopolysaccharide- (LPS-) induced ARDS remain unknown. C57BL/6 mice were injected with LPS (5 mg/kg) intratracheally to induce an in vivo ARDS model while RAW264.7 macrophages were stimulated with LPS (100 ng/ml) to induce an in vitro model. Our data demonstrated that pomiferin (20 mg/kg) significantly improved pulmonary function and lung pathological injury in mice with ARDS, apart from increasing survival rate. Meanwhile, pomiferin treatment also inhibited LPS-induced inflammation as well as oxidative stress in lung tissues. LPS stimulation significantly activated AKT/Foxo1 signal pathway in lung tissues, which could be reversed after pomiferin treatment. In vitro experiments further showed that 10, 20, and 50 μM of pomiferin could enhance cell viability of RAW264.7 macrophages stimulated with LPS. What is more, 3-deoxysappanchalcone (3-DE), one AKT agonist, was used to active AKT in RAW264.7 macrophages. The results further showed that 3-DE could abolish pomiferin-elicited protection in LPS-treated RAW264.7 macrophages, evidenced by activated inflammation and oxidative stress. Taken together, our study showed that pomiferin could exert an ARDS-protective effect by blocking the AKT/Foxo1 signal pathway to inhibit LPS-induced inflammatory response and oxidative injury, which may serve as a potential candidate for the treatment of ARDS in the future.

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

Background

CD8⁺ 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.

Methods

A 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.

Results

Sixty-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.

Conclusions

CD8⁺ 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-022-00733-2.