Daily Ards Research Analysis

Cough is essential to airway defence following aspiration. Using a relevant animal model, we set out to identify the mechanisms by which gastric fluid evokes coughing, the vagal afferents responsible for initiating this reflex and the ion channels directly activated by components of gastric fluid. We studied gastric fluid and citric acid evoked cough reflexes in guinea-pigs and, in parallel, their ability to activate airway vagal afferent nerves. Additionally, we utilized a single cell RT-PCR approach to determine the expression of acid sensitive ion channels by the vagal afferent neurones regulating cough. We observed that gastric fluid evoked coughing following direct application to the tracheal and laryngeal mucosa of anaesthetized guinea-pigs. An acidic pH of the gastric fluid was essential to its ability to evoke coughing, and the tussive actions of gastric fluid were mimicked by citric acid. The vagal afferent nerves regulating cough expressed mRNA for the acid-sensitive ion channels (ASICs) ASIC1, ASIC2 and ASIC3. The coughing evoked by gastric fluid and by acid and the vagal afferent nerve discharge evoked by protons were prevented by the ASIC inhibitors diminazene and diclofenac but not by transient receptor potential vanilloid 1 blockade. Based on these results, we conclude that airway mucosal afferent neuronal ASIC channels are essential to airway defence against aspiration of gastric fluid. We speculate that dysfunction of the reflex pathways initiated by vagal afferent neurone ASIC channel engagement may be a risk factor for aspiration pneumonia in susceptible patients. KEY POINTS: Aspiration of gastric contents can induce an acute lung injury that may progress to life-threatening aspiration pneumonia or acute respiratory distress syndrome. Cough is an essential defensive reflex that protects the airways from aspiration. Patients with an absent or ineffective cough reflex are at significantly greater risk for developing aspiration pneumonia. Using an animal model, we have determined the potential mechanisms driving cough in response to gastric fluid aspiration. We found that gastric fluid acidity is essential to the initiation of cough and we identified acid-sensing ion channels expressed by vagal sensory nerves terminating in the airway mucosa as key effectors of this reflex. We speculate that patients with dysfunctional acid-sensing mechanisms in their bronchopulmonary vagal afferent nerves may be at increased risk of aspiration pneumonia. We also summarize the evidence suggesting that this signalling pathway could explain the emergence of cough in patients with gastroesophageal reflux disease.

OBJECTIVE: Traditional deep learning models for multivariate time-series data often fall short in capturing long-range temporal dependencies critical for early prediction of the onset of acute respiratory distress syndrome (ARDS). To address this gap, we introduce Graph-spa, a dynamic Spatiotemporal Graph Neural Network (STGNN) based framework that not only improves ARDS prediction by modeling evolving interactions among clinical variables but also enhances interpretability through model-agnostic feature attribution. METHODS: Graph-spa at its core integrates temporal convolution layers with an STGNN model that dynamically updates the adjacency structure, capturing both local and non-local temporal dependencies across three datasets (HiRID, MIMIC-IV, and eICU). We benchmarked our model against four traditional deep learning models (GRU, LSTM, TCN, Transformer) and an STGNN baseline. To complement the prediction framework, we applied mask-based interpretability approaches to generate feature-time attribution scores. These scores guide a subsequent co-occurrence analysis that identifies clusters of sustained feature activations in the 12-hour window preceding ARDS onset. RESULTS: Our experiments demonstrate that Graph-spa consistently outperforms the baseline models in both internal and external validations. On the AUC F1-MCC metric, chosen for this imbalanced classification task, Graph-spa achieves 50.02% vs 45.61% on HiRID, 48.52% vs 46.88% on MIMIC-IV, and 46.64% vs 45.41% on eICU-CRD compared with the STGNN baseline. Graph-spa also outperforms recurrent, convolutional, and attention-based models evaluated under identical settings (Wilcoxon signed-rank; Holm-adjusted p-values ¡ 0.05). The dynamic adjacency enhancement allows the model to capture complex, evolving feature interactions, as evidenced by more diversified connectivity patterns compared to the baseline. In addition, interpretability analysis reveals that sustained abnormalities in potassium levels, along with declining Glasgow Coma Scale scores, form a critical composite risk profile that may serve as an early indicator of ARDS. CONCLUSION: Graph-spa advances dynamic clinical event prediction and also offers significant promise for early detection of organ failure in acute care settings by illustrating an end-to-end approach covering spatiotemporal modeling, interpretability, and discovery of sub-clinical signatures. Because its core modules, dynamic spatiotemporal graph construction, mask-based attribution, and co-occurrence mining, are model-agnostic, the framework can easily be extrapolated to any dynamic classification or regression task in the ICU. The code is available at https://github.com/vsubbian/Graph-spa.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common complication after type A aortic dissection surgery and often leads to worsened clinical outcomes for patients. The early prediction of postoperative ARDS is a crucial challenge in clinical practice; however, there have been few reports on related studies based on the 2023 global new definition. METHODS: A retrospective analysis was conducted on the clinical data of 423 patients who were diagnosed with type A aortic dissection and who underwent surgery at Northern Jiangsu People's Hospital in Jiangsu Province from November 2019 to April 2025. A 7:3 random division was applied to the patients, resulting in a training set n = 296 and a validation set n = 127. Risk factors were identified via LASSO analysis, and a comprehensive risk prediction model was subsequently constructed by integrating five machine learning algorithms. The receiver operating characteristic (ROC) curve was utilised, and the model with the best predictive performance was selected based on the area under the curve (AUC). RESULTS: Among the 423 included patients, 192 developed ARDS, with an incidence rate of 45.39%. LASSO analysis revealed 13 risk factors. Among the five machine learning models constructed based on these factors, the random forest model demonstrated the highest prediction efficiency for ARDS (AUC = 0.978), followed by the logistic regression (AUC = 0.965), decision tree (AUC = 0.881), support vector machine (AUC = 0.835), and K-nearest neighbour (AUC = 0.807) models. CONCLUSION: The development of a nomogram model using machine learning algorithms for predicting ARDS risk in patients with type A aortic dissection after surgery could identifying high-risk patients at an early stage and enable timely implementations of preventive strategies. TRIAL REGISTRATION: The medical research ethics committee of the Northern Jiangsu People's Hospital provided approval for this study (ethics number: 2024ky314). This study is registered in the Chinese Clinical Trial Registry under registration number ChiCTR2500099730.The registration date was March 27,2025.