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Oxygen supplementation triggers inflammation and disrupts alveolar and microvascular growth in preterm infants, often leading to bronchopulmonary dysplasia (BPD). The autonomic nervous system (ANS) is critical in lung tissue homeostasis and repair. The sympathetic co-neurotransmitter neuropeptide Y (NPY) emerges as a central regulator of the ANS-organ interface with immune-modulatory function. Here, we studied sympathetic nervous system (SNS) signaling and the contribution of NPY to a hyperoxia-based model of BPD. To this end, neonatal wild-type (WT) and NPY knockout mice (NPY −/− ) were exposed to 85% O 2 (HYX) or 21% O 2 (NOX) from birth to postnatal day 14. Prolonged hyperoxia caused a 7-fold increase of tyrosine hydroxylase (TH) protein, an enzyme characteristic for the SNS, and NPY mRNA (> 40-fold) in neonatal WT lungs. The analysis of lung scRNA-seq revealed an upregulation of NPY in alveolar macrophages of WT HYX when compared to WT NOX . In contrast, NPY −/− HYX showed lower amount of TH than WT HYX , indicating reduced sympathetic-associated signaling. Quantitative histomorphometry demonstrated an aggravated hyperoxia-induced alveolar growth arrest and septal thickening in NPY −/− HYX , whereas vascular muscularization and proliferation of vascular smooth muscle cells (SMC) were attenuated compared to WT HYX . Additionally, NPY −/− HYX were protected from hyperoxia-induced CD68 + macrophage recruitment, despite exhibiting higher lung expression of Il6 (20-fold) and Il1b (4-fold) than WT HYX . In summary, our data demonstrate a dual role of NPY in neonatal lungs in response to hyperoxia, preserving alveolarization while promoting immune cell recruitment and vascular muscularization, highlighting the importance of ANS-lung interface in lung maturation and injury.

Background Chronic obstructive pulmonary disease (COPD) is a common reason for admission to the intensive care unit (ICU), where accurate risk stratification is crucial for clinical decision-making. This study aimed to develop and validate machine learning models for predicting in-hospital mortality risk in ICU patients with COPD using multicenter critical care databases, and to evaluate their incremental value and clinical utility. Methods This was a multicenter retrospective study utilizing data from three public databases: MIMIC-IV (for model development and internal validation), MIMIC-III (for internal temporal validation), and eICU (for external validation). Patients with a first ICU admission, aged ≥ 18 years, and meeting ICD diagnosis codes for COPD were included; those with an ICU length of stay < 24 h were excluded. The primary outcome was in-hospital mortality. Core predictors were selected through collinearity analysis, the Boruta algorithm, and recursive feature elimination with tenfold nested cross-validation. Twelve algorithms were employed for model development, and cost-sensitive learning was applied to address class imbalance in the training set (MIMIC-IV). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves (calibration intercept and slope), Brier score, and decision curve analysis (DCA). The DeLong test was used to compare AUC between models, and the Integrated Discrimination Improvement (IDI) quantified the incremental value of the model relative to the SAPS II score. The SHAP method was used for model interpretation. Results A total of 7,900 patients from the MIMIC-IV cohort, 1,979 from MIMIC-III, and 8,491 from the eICU cohort were included. Thirteen core predictors were ultimately selected (e.g., SAPS II, respiratory rate, heart rate, blood urea nitrogen, lactate). The CatBoost model demonstrated the best robustness across the three independent validation sets, achieving an AUC of 0.753 (95% CI: 0.722–0.784) in internal validation, 0.731 (95% CI: 0.701–0.760) in temporal validation, and 0.735 (95% CI: 0.718–0.751) in external validation. After calibration, the model’s predictive accuracy improved significantly in both MIMIC-III (Brier score decreased from 0.16 to 0.14) and eICU (Brier score decreased from 0.11 to 0.09). DCA indicated a clinical net benefit for the model within the 0.10–0.60 risk threshold range. Compared to the SAPS II score, CatBoost significantly improved discrimination and reclassification in the MIMIC-IV (ΔAUC = + 0.063, P  < 0.001; IDI = 0.066, P  < 0.001) and MIMIC-III (ΔAUC = + 0.044, P  < 0.001; IDI = 0.058, P  < 0.001) cohorts. SHAP analysis identified SAPS II, respiratory rate, and blood urea nitrogen as key drivers of risk prediction. An online risk calculator based on this model has been publicly deployed. Conclusion This study successfully developed a CatBoost model for predicting in-hospital mortality in ICU patients with COPD using multicenter data. The model demonstrated good discrimination, calibration, and clinical utility across cross-institutional and cross-temporal validation, with performance superior to the traditional SAPS II score. The online tool, integrated with SHAP explanations, can provide clinicians with individualized risk prediction support.

The aim of these guidelines is to update the 2017 clinical practice guideline (CPG) of the European Society of Intensive Care Medicine (ESICM). The scope of this CPG is limited to adult patients and to non-pharmacological respiratory support strategies across different aspects of acute respiratory distress syndrome (ARDS), including ARDS due to coronavirus disease 2019 (COVID-19). These guidelines were formulated by an international panel of clinical experts, one methodologist and patients’ representatives on behalf of the ESICM. The review was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement recommendations. We followed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence and grade recommendations and the quality of reporting of each study based on the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) network guidelines. The CPG addressed 21 questions and formulates 21 recommendations on the following domains: (1) definition; (2) phenotyping, and respiratory support strategies including (3) high-flow nasal cannula oxygen (HFNO); (4) non-invasive ventilation (NIV); (5) tidal volume setting; (6) positive end-expiratory pressure (PEEP) and recruitment maneuvers (RM); (7) prone positioning; (8) neuromuscular blockade, and (9) extracorporeal life support (ECLS). In addition, the CPG includes expert opinion on clinical practice and identifies the areas of future research.

PurposeTo investigate the global burden of sepsis in hospitalized adults by updating and expanding a systematic review and meta-analysis and to compare findings with recent Institute for Health Metrics and Evaluation (IHME) sepsis estimates.MethodsThirteen electronic databases were searched for studies on population-level sepsis incidence defined according to clinical criteria (Sepsis-1, -2: severe sepsis criteria, or sepsis-3: sepsis criteria) or relevant ICD-codes. The search of the original systematic review was updated for studies published 05/2015–02/2019 and complemented by a search targeting low- or middle-income-country (LMIC) studies published 01/1979–02/2019. We performed a random-effects meta-analysis with incidence of hospital- and ICU-treated sepsis and proportion of deaths among these sepsis cases as outcomes.ResultsOf 4746 results, 28 met the inclusion criteria. 21 studies contributed data for the meta-analysis and were pooled with 30 studies from the original meta-analysis. Pooled incidence was 189 [95% CI 133, 267] hospital-treated sepsis cases per 100,000 person-years. An estimated 26.7% [22.9, 30.7] of sepsis patients died. Estimated incidence of ICU-treated sepsis was 58 [42, 81] per 100,000 person-years, of which 41.9% [95% CI 36.2, 47.7] died prior to hospital discharge. There was a considerably higher incidence of hospital-treated sepsis observed after 2008 (+ 46% compared to the overall time frame).ConclusionsCompared to results from the IHME study, we found an approximately 50% lower incidence of hospital-treated sepsis. The majority of studies included were based on administrative data, thus limiting our ability to assess temporal trends and regional differences. The incidence of sepsis remains unknown for the vast majority of LMICs, highlighting the urgent need for improved epidemiological sepsis surveillance.