Article Text

Download PDFPDF
ARDS subphenotypes: searching for Rorschach among the roentgenograms?
  1. Andrew Conway Morris1,2,
  2. Katharina Kohler2,
  3. Manu Shankar-Hari3,4
  1. 1John V Farman Intensive Care Unnit, Addenbrooke's Hospital, Cambridge, UK
  2. 2University Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
  3. 3Intensive Care Medicine, Guy's and St Thomas' Hospitals NHS Trust, London, UK
  4. 4School of Immunobiology and Microbial Sciences, King's College London, London, UK
  1. Correspondence to Professor Manu Shankar-Hari, Intensive Care Medicine, Guy's and St Thomas' Hospitals NHS Trust, London SE1 7EH, UK; manu.shankar-hari{at}kcl.ac.uk

Statistics from Altmetric.com

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 (<40 Kpa), moderate (13.3–26.6 Kpa) and severe (≤13.3 Kpa) ARDS on a positive end expiratory pressure of >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 …

View Full Text

Footnotes

  • Twitter @msh_manu

  • Contributors All authors developed the outline. ACM and KK wrote the first draft and generated the figures. All authors critically revised the manuscript for important intellectual content and agreed the final submitted version of the manuscript.

  • Funding MS-H is supported by the National Institute for Health Research Clinician Scientist Award (NIHR-CS-2016-16-011). ACM is supported by an MRC Clinician Scientist Fellowship (MR/V006118/1).

  • Disclaimer The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the UK National Institute for Health Research or the Department of Health.

  • Competing interests None declared.

  • Provenance and peer review Commissioned; externally peer reviewed.

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Linked Articles