Acute respiratory distress syndrome (ARDS) is a major contributor to ICU mortality and is characterised by hypoxaemia and pulmonary oedema. Pathomechanisms include barrier breakdown, immunopathology, haemostatic derailment and dysbiosis; however, the actual sequence of events and how they cumulatively lead to lung failure remains unclear. Although ARDS is frequently triggered by pneumonia, it can also occur as a result of trauma, aspiration or non-pulmonary causes. Importantly, ARDS is highly heterogeneous; growing evidence points to aetiology-specific pathomechanisms - a circumstance that explains why attempts to develop specific drugs or timely diagnostic markers have so far failed. A comprehensive analysis of key microenvironmental and haemostasis-related parameters of the lung, combined with multidimensional quantitative image features derived from chest CT scans (radiomics), will enable us to i) identify ARDS phenotypes with different biological characteristics and ii) generate new hypotheses regarding aetiology- or subgroup-specific mechanisms, molecular markers and therapeutic options. Our approach is based on ICU management of our patients guided by bronchoalveolar lavage fluid (BALF). Together with previously sampled cases and new samples collected as part of this study, our cohort will consist of patients with i) COVID-19-associated ARDS, ii) ARDS associated with other viral pneumonia, iii) ARDS associated with bacterial pneumonia, and iv) ARDS of non-pulmonary origin. Bacterial and fungal co-infections and superinfections are recorded in all patients and taken into account in the stratification. Patients with pneumonia without ARDS, as well as ventilated patients without underlying lung disease, serve as controls. To characterise the microbial lung microenvironment, the investigators combine data from routine microbiological diagnostics with microbiome sequencing and metabolomics. In addition, the investigators conduct comprehensive and longitudinal immune and haemostatic profiling by regularly analysing immune cells, cytokines and parameters of immune thrombosis in BALF and blood. Multi-omics integration then identifies phenotypic subgroups by merging all multimodal datasets - including radiomics. Selected samples from identified clusters are then further characterised using single-cell sequencing to uncover specific features/markers and pathomechanisms of the respective ARDS subtypes. Although it is clear that the pathogenesis of ARDS is multifactorial, comprehensive studies that integrate all relevant parameters are rare. Radiomics is increasingly recognised as a powerful tool for capturing the clinical status of ARDS in detail; however, to date, this imaging data has not been systematically linked to other omics readouts. The investigators aim to bridging this gap by conducting a thorough investigation across various ARDS aetiologies in the present study, incorporating all identifiable key factors. Our interdisciplinary team comprises basic immunologists, infectious disease and computational biologists, as well as clinicians with expertise in ARDS, infectious diseases, immunothrombosis and radiology.
Study Type
OBSERVATIONAL
Enrollment
130
blood will be drawn from patients on day 1 and once a week until discharge/withdrawal/death
mini-BALF using a physiologic saline soluation will be performed on day 1 and once a week until discharge/withdrawal/death
General Hospital of Vienna, Medical University of Vienna
Vienna, State of Vienna, Austria
RECRUITINGARDS phenotypes
The application of multi-omics cluster analysis to reveal any distinct ARDS phenotypes or subgroup-specific characteristics.
Time frame: 4 years
Lung Microbiome Composition Analysis via 16S rRNA Gene Sequencing of Bronchoalveolar Lavage (BAL) Fluid
Differences in the microbial community composition (alpha diversity, beta diversity, and taxonomic abundance at phylum/genus level) of the lower respiratory tract microbiome in intensive care patients with ARDS
Time frame: 4 years
Metabolomic Profile of Bronchoalveolar Lavage Fluid and Plasma
Differences in the metabolomic profile (relative concentrations of annotated metabolites and pathway-level scores) in bronchoalveolar lavage (BAL) fluid and plasma between ARDS subgroups (with vs. without bacterial/fungal co-/superinfection). Metabolomic measurements will be performed using the METAB02, AMINO01, and LIPID01 or other appropriate packages. Samples will be analyzed via mass spectrometry to quantify a wide range of analytes, including various amino acids, lipids and their subclasses, and metabolites such as pyruvate, lactate, citrate, and succinate.
Time frame: 4 years
Inflammatory Cytokine Profile in Bronchoalveolar Lavage Fluid and Plasma
Concentrations of predefined pro- and anti-inflammatory cytokines (e.g., IL-6, IL-8, TNF-α, others according to panel) in BAL fluid and plasma of ARDS patients, and detection of differences between ARDS subgroups (with vs. without bacterial/fungal co-/superinfection) by Multiplex bead-based immunoassay or ELISA panels (e.g., Luminex, electrochemiluminescence).
Time frame: 4 years
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