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Original article
Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis
  1. L D Bos1,2,3,
  2. L R Schouten1,3,
  3. L A van Vught4,
  4. M A Wiewel4,
  5. D S Y Ong5,6,
  6. O Cremer6,
  7. A Artigas7,
  8. I Martin-Loeches8,
  9. A J Hoogendijk4,
  10. T van der Poll4,
  11. J Horn1,3,
  12. N Juffermans1,3,
  13. C S Calfee9,
  14. M J Schultz1,3
  15. On behalf of the MARS consortium
    1. 1Department of Intensive Care, Academic Medical Center, Amsterdam, The Netherlands
    2. 2Department of Respiratory Medicine, Academic Medical Center, Amsterdam, The Netherlands
    3. 3Laboratory of Experimental Intensive Care and Anesthesiology (L.E.I.C.A), Academic Medical Center, Amsterdam, The Netherlands
    4. 4Center for Experimental and Molecular Medicine (CEMM), Academic Medical Center, Amsterdam, The Netherlands
    5. 5Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, The Netherlands
    6. 6Department of Intensive Care Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
    7. 7CIBER enfermedades respiratorias (CIBERES), Critical Care Center, Sabadell Hospital, Corporación Sanitaria Universitaria Parc Taulí, Universitat Autonoma de Barcelona, Sabadell, Spain
    8. 8Multidisciplinary Intensive Care Research Organization (MICRO), Department of Clinical Medicine, Trinity Centre for Health Sciences, Dublin, Ireland
    9. 9Departments of Medicine and Anesthesia, Cardiovascular Research Institute, University of California San Francisco, San Francisco, California, USA
    1. Correspondence to Dr Lieuwe Bos, Department of Intensive Care, Academic Medical Center, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands; l.d.bos{at}amc.nl

    Abstract

    Rationale We hypothesised that patients with acute respiratory distress syndrome (ARDS) can be clustered based on concentrations of plasma biomarkers and that the thereby identified biological phenotypes are associated with mortality.

    Methods Consecutive patients with ARDS were included in this prospective observational cohort study. Cluster analysis of 20 biomarkers of inflammation, coagulation and endothelial activation provided the phenotypes in a training cohort, not taking any outcome data into account. Logistic regression with backward selection was used to select the most predictive biomarkers, and these predicted phenotypes were validated in a separate cohort. Multivariable logistic regression was used to quantify the independent association with mortality.

    Results Two phenotypes were identified in 454 patients, which we named ‘uninflamed’ (N=218) and ‘reactive’ (N=236). A selection of four biomarkers (interleukin-6, interferon gamma, angiopoietin 1/2 and plasminogen activator inhibitor-1) could be used to accurately predict the phenotype in the training cohort (area under the receiver operating characteristics curve: 0.98, 95% CI 0.97 to 0.99). Mortality rates were 15.6% and 36.4% (p<0.001) in the training cohort and 13.6% and 37.5% (p<0.001) in the validation cohort (N=207). The ‘reactive phenotype’ was independent from confounders associated with intensive care unit mortality (training cohort: OR 1.13, 95% CI 1.04 to 1.23; validation cohort: OR 1.18, 95% CI 1.06 to 1.31).

    Conclusions Patients with ARDS can be clustered into two biological phenotypes, with different mortality rates. Four biomarkers can be used to predict the phenotype with high accuracy. The phenotypes were very similar to those found in cohorts derived from randomised controlled trials, and these results may improve patient selection for future clinical trials targeting host response in patients with ARDS.

    • ARDS
    • Cytokine Biology

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    Footnotes

    • Collaborators MARS Consortium are Jos F Frencken, (Department of Intensive Care Medicine and Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands); Marc Bonten, Peter M C Klein Klouwenberg, David Ong (Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, the Netherlands) and Roosmarijn T M van Hooijdonk, Mischa A Huson, Laura R A Schouten, Marleen Straat, Lonneke A van Vught, Maryse A Wiewel, Esther Witteveen, Gerie J Glas, and Luuk Wieske, (Department of Intensive Care Medicine, Academic Medical Center, University of Amsterdam); Brendon P Scicluna, Arjan J Hoogendijk, H Belkasim-Bohoudi, Tom van der Poll (Center of Experimental Molecular Medicine; CEMM, Academic Medical Center, University of Amsterdam). The list of MARS Consortium members is also available as an online supplementary appendix.

    • Contributors LDB, LRS and MJS: study design, data collection, data analysis, data interpretation and writing. LAV, MAW, DO OC and AJH: data collection, data analysis, data interpretation and writing. AA, IML, TvdP, JH and NF: study design, data analysis, data interpretation and writing. CSC: data analysis, data interpretation and writing.

    • Funding Center for Translational Molecular Medicine, 10.13039/501100006020, MARS project.

    • Competing interests CC was supported by the NIH (HL133390 and HL131621).

    • Ethics approval UMCU and AMC (IRBNo.10-056C).

    • Provenance and peer review Not commissioned; externally peer reviewed.

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