Daily Ards Research Analysis

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a life-threatening condition associated with high mortality rates. Despite advancements in critical care, reliable early prediction methods for ARDS-related mortality remain elusive. Accurate risk assessment is crucial for timely intervention and improved patient outcomes. Machine learning (ML) techniques have emerged as promising tools for mortality prediction in patients with ARDS, leveraging complex clinical datasets to identify key prognostic factors. However, the efficacy of ML-based models remains uncertain. This systematic review aims to assess the value of ML models in the early prediction of ARDS mortality risk and to provide evidence supporting the development of simplified, clinically applicable ML-based scoring tools for prognosis. OBJECTIVE: This study systematically reviewed available literature on ML-based ARDS mortality prediction models, primarily aiming to evaluate the predictive performance of these models and compare their efficacy with conventional scoring systems. It also sought to identify limitations and provide insights for improving future ML-based prediction tools. METHODS: A comprehensive literature search was conducted across PubMed, Web of Science, the Cochrane Library, and Embase, covering publications from inception to April 27, 2024. Studies developing or validating ML-based ARDS mortality predicting models were considered for inclusion. The methodological quality and risk of bias were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses were performed to explore heterogeneity in model performance based on dataset characteristics and validation approaches. RESULTS: In total, 21 studies involving a total of 31,291 patients with ARDS were included. The meta-analysis demonstrated that ML models achieved relatively high predictive performance. In the training datasets, the pooled concordance index (C-index) was 0.84 (95% CI 0.81-0.86), while for in-hospital mortality prediction, the pooled C-index was 0.83 (95% CI 0.81-0.86). In the external validation datasets, the pooled C-index was 0.81 (95% CI 0.78-0.84), and the corresponding value for in-hospital mortality prediction was 0.80 (95% CI 0.77-0.84). ML models outperformed traditional scoring tools, which demonstrated lower predictive performance. The pooled area under the receiver operating characteristic curve (ROC-AUC) for standard scoring systems was 0.7 (95% CI 0.67-0.72). Specifically, 2 widely used clinical scoring systems, the Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score II (SAPS-II), demonstrated ROC-AUCs of 0.64 (95% CI 0.62-0.67) and 0.70 (95% CI 0.66-0.74), respectively. CONCLUSIONS: ML-based models exhibited superior predictive accuracy over conventional scoring tools, suggesting their potential use in early ARDS mortality risk assessment. However, further research is needed to refine these models, improve their interpretability, and enhance their clinical applicability. Future efforts should focus on developing simplified, efficient, and user-friendly ML-based prediction tools that integrate seamlessly into clinical workflows. Such advancements may facilitate the early identification of high-risk patients, enabling timely interventions and personalized, risk-based prevention strategies to improve ARDS outcomes.

BACKGROUND: Alveolar‒capillary barrier disruption is a hallmark of acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). The contribution of necroptosis to the compromised alveolar-barrier in ALI remains unclear. Mesenchymal stem cells (MSCs) may contribute to tissue repair in ALI and ARDS. Here we evaluated the efficacy and explored the molecular mechanisms of menstrual blood-derived endometrial stem cells (MenSCs) and MenSC-derived extracellular vesicles (MenSC-EVs) in ALI-induced alveolar epithelial barrier dysfunction. METHODS: Human lung epithelial cells were stimulated with endotoxin and treated with MenSCs or MenSC-EVs, and their barrier properties were evaluated. Lipopolysaccharide (LPS)-injured mice were treated with MenSCs or MSC-EVs, and the degree of lung injury and the alveolar epithelial barrier of the lung tissue were assessed. RESULTS: We found that MenSCs reduced lung injury and restored alveolar-barrier integrity in lung tissue. In vitro, MenSCs reduced paracellular permeability and restored barrier integrity in human lung epithelial cells. MenSC-EVs replicated all these MenSC-mediated changes. Mechanistic research revealed that MenSCs inhibited MAPK signaling and necroptosis. JNK inhibition SP600125, and ERK inhibition U0126 or inhibition of necroptosis with Nec-1 or GSK872 diminished the beneficial anti-epithelial barrier dysfunction effects of MenSCs or MenSC-EVs. CONCLUSIONS: Our results suggest that human menstrual blood-derived endometrial stem cells mitigate lung injury and improve alveolar barrier properties by inhibiting MAPK-mediated necroptosis through extracellular vesicles, supporting the application of MenSCs or MenSC-derived extracellular vesicles to treat ALI or ARDS.

BACKGROUND: The pre-existing diagnosis of cirrhosis is a complicating factor in the progression and prognosis of sepsis; however, the unique epidemiology, sepsis characteristics, and underlying mechanisms of immune dysregulation in sepsis among patients with cirrhosis remain incompletely understood. Our primary objective was to identify clinical outcomes and biological characteristics that differ between patients with and without cirrhosis among critically ill patients with sepsis. METHODS: We analyzed data from a prospective cohort of critically ill patients presenting to single center with sepsis. Subjects were followed for 6 days for the development of acute respiratory distress syndrome (ARDS) and acute kidney injury (AKI), and 30 days for mortality. Inflammatory, endothelial, and coagulopathic proteins were measured in plasma collected at ICU admission in a subset of patients. We determined associations of cirrhosis with outcomes using multivariable logistic regression adjusting for pre-specified confounders. We tested differences in plasma protein levels by cirrhosis diagnosis using the Wilcoxon Rank-sum test. RESULTS: We enrolled 2962 subjects, 371 (13%) of whom had a pre-existing diagnosis of cirrhosis. Patients with cirrhosis had higher severity of illness scores, were more likely to have an abdominal source of sepsis, and had more significant clinically measured coagulation abnormalities relative to patients without cirrhosis. In multivariate analysis, cirrhosis was associated with higher AKI risk (adjusted OR 1.65; 95% CI 1.21 to 2.26; P = 0.002), and 30-day mortality (adjusted OR 1.38; 95% CI 1.05 to 1.82; P = 0.022). There was no significant difference in risk for ARDS (adjusted OR 1.02; 95% CI 0.69 to 1.50; P = 0.92). Cirrhosis was associated with higher plasma levels of angiopoietin-2 (P < 0.001), von Willebrand factor (P < 0.001), and soluble thrombomodulin (P < 0.001), as well as lower levels of interleukin (IL)-10 (P < 0.001), IL-1β (P = 0.008), and IL-1RA (P = 0.036). There were no significant differences in levels of IL-6 (P = 0.30). CONCLUSIONS: We identified associations between pre-existing cirrhosis and endothelial injury, AKI, and mortality in sepsis. Patients with pre-existing cirrhosis who develop sepsis may display a unique phenotype of endothelial dysfunction that requires unique targeted approaches.