Background: High-flow nasal cannula oxygen therapy (HFNC) has been shown to be a useful therapy in the treatment of patients with Acute Respiratory Distress Syndrome (ARDS), but its efficacy is still unknown in patients with COVID-19. Our objective is to describe its utility as therapy for the treatment of ARDS caused by SARS-CoV-2. Methods: A retrospective, observational study was performed at a single centre, evaluating patients with ARDS secondary to COVID-19 treated with HFNC. The main outcome was the intubation rate at day 30, which defined failure of therapy. We also analysed the role of the ROX index to predict the need for intubation.Results: In the study period, 196 patients with bilateral pneumonia were admitted to our pulmonology unit, 40 of whom were treated with HFNC due to the presence of ARDS. The intubation rate at day 30 was 52.5%, and overall mortality was 22.5%. After initiating HFNC, the SpO2/FiO2 ratio was significantly better in the group that did not require intubation (113.4±6.6 vs 93.7±6.7, p=0.020), as was the ROX index (5.0±1.6 vs 4.0±1.0, p=0.018). A ROX index less than 4.94 measured 2 to 6 h after the start of therapy was associated with increased risk of intubation (HR 4.03 [95% CI 1.18 – 13.7]; p=0.026).Conclusion: High-flow therapy is a useful treatment in ARDS in order to avoid intubation or as a bridge therapy, and no increased mortality was observed secondary to the delay in intubation. After initiating HFNC, a ROX index below 4.94 predicts the need for intubation.

Introduction: The clinical heterogeneity of patients with chronic bronchial infection (CBI) under nebulized antibiotics (NA) suggests the existence of different phenotypes with prognostic implications. Our objective was to analyse sociodemographic and clinical characteristics prior to the start of NA in these patients and evaluate their influence on prognosis and treatment response. Material and Methods: Multicentre, retrospective, observational study of patients with CBI under NA were included between January 2018 and December 2022 in 8 centres in Spain. Patients were classified by Partitioning Around Medoids algorithm on clinical characteristics prior the start of treatment. The response outcomes were exacerbations and eradication. Specific predictors of response for each cluster were assessed using stepwise logistic regression. Results: A total of 244 patients were recruited, predominantly male (50.4%) with a median age of 73 [61.0;80.2] years. Two clusters were identified: (1) females with bronchiectasis and fewer comorbidities, and (2) older males with COPD and a higher burden of comorbidities. Cluster 1 demonstrated a superior response to NA, showing a greater reduction in exacerbations [100% vs 66.7%; p<0.001] along with clinical enhancements, including reduced sputum production [87.6% vs 74.2%; p=0.05],diminished purulence [89.9% vs 75.8%; p=0.03] and decreased dyspnoea [72.4% vs 54.8%; p=0.04]. Cluster 1 experienced fewer adverse events [12.7% vs 29.8%; p<0.001]. Conclusions: In this pilot study, specific phenotypes were identified and showed differences in responses to treatment with NA.

ChatGPT (Chat-Generative Pre-trained Transformer) is an accessible and innovative tool for obtaining healthcare information. This study evaluates the quality and reliability of information provided by ChatGPT 4.0^® regarding Obstructive Sleep Apnea (OSA), comparing it with responses from sleep medicine specialists. Thirty frequently asked questions about OSA were posed to ChatGPT 4.0^® and two expert physicians. Responses from both sources (V1: AI and V2: Medical Experts) were blindly evaluated by a panel of six specialists using a five-point Likert scale across precision, relevance, and depth dimensions. The AI-generated responses (V1) achieved a slightly higher overall score compared to those from medical experts (V2), although the difference was not statistically significant (p > 0.08). These results suggest that both sources offer comparable quality and content. Additionally, ChatGPT’s responses were clear and easily understandable, providing an accessible explanation of OSA pathology.