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In 1967, Ashbaugh et al first described acute respiratory distress syndrome (ARDS)—an acute illness, characterised by tachypnoea, hypoxaemia and loss of lung compliance occurring after a variety of pulmonary and non-pulmonary insults (including trauma, acute pancreatitis, viral pneumonitis).1 This concept is retained as the ARDS illness model within the current consensus definitions, with acute defined as within 7 days of insult, and hypoxaemia categorised using partial pressure of oxygen/fraction of inspired oxygen concentration (PaO2/FiO2 ratio) into mild ( 5 cm water.2 Fifty years on, ARDS remains a clinical challenge. Globally, ARDS remains clinically underrecognised, with an acute hospital mortality of 46% in patients with severe ARDS.3 Further, after more than 150 randomised controlled trials (RCTs),4 we do not have a single drug proven to benefit patients with ARDS. Notably, the histopathological hallmark of ARDS, diffuse alveolar damage (DAD),1 is only found in half of the patients, and is difficult to ascertain during acute illness.5 This clinical challenge led to the hypothesis that the heterogeneity of ARDS will manifest as subpopulations with similar clinical, biological, outcome and/or treatment response characteristics. Further, these subpopulations may be unique to ARDS or shared with other critical illness syndromes. If we could identify ARDS subpopulations based on clinical and/or biological characteristics, this may highlight molecular mechanisms to target in RCTs, subpopulations with a higher risk of adverse outcomes or greater treatment responses.6 Calfee et al have led the field of determining such ARDS subpopulations, primarily with data from patients enrolled into RCTs, using latent class analyses (LCA) of clinical and biomarker data. They consistently report a two class model (two ARDS subpopulations or subphenotypes) as the best fit for the clinical and biomarker data …