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.

Inclusion Criteria:

• male and female
• aged 18 years or over
• signed consent form
• depending on the study group: Confirmed ARDS according to the Berlin criteria (see below, Groups B and D) Confirmed severe CAP requiring mechanical ventilation and intensive care (see below, Groups B and C) Ventilated patients without signs of ARDS (see below, Group E)

Exclusion Criteria:

- under 18 years of age

All patients admitted to the University Hospitals of the Medical University of Vienna at the Vienna General Hospital (AKH Vienna) during the specified study period who meet the inclusion criteria will be enrolled in this study.

We plan to share de-identified individual participant data (IPD) underlying the results reported in this study, including demographic data, baseline characteristics, and outcome measures. Data will be available following publication of the primary results, upon reasonable request from qualified researchers.

4 years, starting from enrollment

Access will be granted after approval of a methodologically sound proposal and completion of a data sharing agreement, in compliance with institutional and data protection regulations.