Article Data

  • Views 3626
  • Dowloads 503

Systematic reviews

Open Access Special Issue

Precision medicine in Acute Respiratory Distress Syndrome

  • Nanon F.L. Heijnen1,*,
  • Dennis C.J.J. Bergmans1,2
  • Marcus J. Schultz3,4,5,6
  • Lieuwe D.J. Bos3,7

1Department of Intensive Care Medicine, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands

2NUTRIM school of Nutrition and Translational Research, Maastricht University, 6229 HX Maastricht, The Netherlands

3Department of Intensive Care Medicine, Amsterdam University Medical Centers, location Academic Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands

4Laboratory of Experimental Intensive Care and Anesthesiology (L·E·I·C·A), Academic Medical Centers, location Academic Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands

5Mahidol-Oxford Tropical Medicine Research Unit (MORU), Mahidol University, 10330 Bangkok, Thailand

6Nuffield Department of Medicine, University of Oxford, OX3 Oxford, UK

7Department of Respiratory Medicine, Amsterdam University Medical Centers, location Academic Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands

DOI: 10.22514/sv.2021.233 Vol.18,Issue 5,September 2022 pp.75-85

Submitted: 30 August 2021 Accepted: 14 October 2021

Published: 08 September 2022

(This article belongs to the Special Issue New Insights in Acute Respiratory Distress Syndrome)

*Corresponding Author(s): Nanon F.L. Heijnen E-mail: nanon.heijnen@mumc.nl

Abstract

Many patients with acute respiratory failure fulfill the diagnosis of Acute Respiratory Distress Syndrome (ARDS), forming a very heterogeneous population. Clinical trials have not yielded beneficial treatment effects in ARDS, possibly caused by this heterogeneity. Dividing patients with ARDS into subgroups, each with similar characteristics, may result in improved treatment strategies as part of a precision medicine approach. In this systematic review, we summarize the subphenotypes identified so far, the current state, and future directions for precision medicine in ARDS. Multiple data-driven subphenotypes have been identified based on a wide range of variables. These subphenotypes are associated with differences in clinical outcomes, which could be used for prognostic- and predictive enrichment of future interventional studies. True treatable traits have not been identified yet, deeper phenotyping will hopefully reveal these along with mechanistic differences.


Keywords

Precision medicine; Phenotypes; ARDS


Cite and Share

Nanon F.L. Heijnen,Dennis C.J.J. Bergmans,Marcus J. Schultz,Lieuwe D.J. Bos. Precision medicine in Acute Respiratory Distress Syndrome. Signa Vitae. 2022. 18(5);75-85.

References

[1] Thompson BT, Chambers RC, Liu KD. Acute Respiratory Distress Syndrome. New England Journal of Medicine. 2017; 377: 562–572.

[2] Confalonieri M, Salton F, Fabiano F. Acute respiratory distress syndrome. European Respiratory Review : an Official Journal of the European Respiratory Society. 2017; 26: 160116.

[3] Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, Fan E, et al. Acute respiratory distress syndrome: the Berlin Definition. Journal of the American Medical Association. 2012; 307: 2526–2533.

[4] Matthay MA, McAuley DF, Ware LB. Clinical trials in acute respiratory distress syndrome: challenges and opportunities. The Lancet Respiratory Medicine. 2017; 5: 524–534.

[5] Bos LDJ, Artigas A, Constantin J, Hagens LA, Heijnen N, Laffey JG, et al. Precision medicine in acute respiratory distress syndrome: workshop report and recommendations for future research. European Respiratory Review. 2021; 30: 200317.

[6] Jameson JL, Longo DL. Precision medicine–personalized, problematic, and promising. The New England Journal of Medicine. 2015; 372: 2229–2234.

[7] Loibl S, Gianni L. Her2-positive breast cancer. Lancet. 2017; 389: 2415–2429.

[8] Wenzel SE, Schwartz LB, Langmack EL, Halliday JL, Trudeau JB, Gibbs RL, et al. Evidence that severe asthma can be divided pathologically into two inflammatory subtypes with distinct physiologic and clinical characteristics. American Journal of Respiratory and Critical Care Medicine. 1999; 160: 1001–1008.

[9] Wenzel SE. Asthma phenotypes: the evolution from clinical to molecular approaches. Nature Medicine. 2012; 18: 716–725.

[10] Prescott HC, Calfee CS, Thompson BT, Angus DC, Liu VX. Toward Smarter Lumping and Smarter Splitting: Rethinking Strategies for Sepsis and Acute Respiratory Distress Syndrome Clinical Trial Design. American Journal of Respiratory and Critical Care Medicine. 2016; 194: 147–155.

[11] Reddy K, Sinha P, O’Kane CM, Gordon AC, Calfee CS, McAuley DF. Subphenotypes in critical care: translation into clinical practice. The Lancet Respiratory Medicine. 2020; 8: 631–643.

[12] FDA. Enrichment Strategies for Clinical Trials to Support Determination of Effectiveness of Human Drugs and Biological Products. Guidance for Industry. 2019. Available at: https://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guidances/default.htm (Accessed: 20 May 2021).

[13] Ranjeva S, Pinciroli R, Hodell E, Mueller A, Hardin CC, Thompson BT, et al. Identifying clinical and biochemical phenotypes in acute respiratory distress syndrome secondary to coronavirus disease-2019. EClinicalMedicine. 2021; 34: 100829.

[14] Wendel Garcia PD, Caccioppola A, Coppola S, Pozzi T, Ciabattoni A, Cenci S, et al. Latent class analysis to predict intensive care outcomes in Acute Respiratory Distress Syndrome: a proposal of two pulmonary phenotypes. Critical Care. 2021; 25: 154.

[15] Puybasset L, Cluzel P, Gusman P, Grenier P, Preteux F, Rouby J-. Regional distribution of gas and tissue in acute respiratory distress syndrome. i. Consequences for lung morphology. Intensive Care Medicine. 2000; 26: 857–869.

[16] Sinha P, Delucchi KL, Chen Y, Zhuo H, Abbott J, Wang C, et al. Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study. Thorax. 2021. (in press)

[17] Kitsios GD, Yang L, Manatakis DV, Nouraie M, Evankovich J, Bain W, et al. Host-Response Subphenotypes Offer Prognostic Enrichment in Patients with or at Risk for Acute Respiratory Distress Syndrome. Critical Care Medicine. 2019; 47: 1724–1734.

[18] Calfee CS, Delucchi KL, Sinha P, Matthay MA, Hackett J, Shankar-Hari M, et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. The Lancet Respiratory Medicine. 2018; 6: 691–698.

[19] Sinha P, Delucchi KL, Thompson BT, McAuley DF, Matthay MA, Calfee CS. Latent class analysis of ARDS subphenotypes: a secondary analysis of the statins for acutely injured lungs from sepsis (SAILS) study. Intensive Care Medicine. 2018; 44: 1859–1869.

[20] Famous KR, Delucchi K, Ware LB, Kangelaris KN, Liu KD, Thompson BT, et al. Acute Respiratory Distress Syndrome Subphenotypes Respond Differently to Randomized Fluid Management Strategy. American Journal of Respiratory and Critical Care Medicine. 2017; 195: 331–338.

[21] Calfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. The Lancet Respiratory Medicine. 2014; 2: 611–620.

[22] Bos LD, Schouten LR, van Vught LA, Wiewel MA, Ong DSY, Cremer O, et al. Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis. Thorax. 2017; 72: 876–883.

[23] Rouby J-, Puybasset L, Cluzel P, Richecoeur J, Lu Q, Grenier P. Regional distribution of gas and tissue in acute respiratory distress syndrome. II. Physiological correlations and definition of an ARDS Severity Score. Intensive Care Medicine. 2000; 26: 1046–1056.

[24] Senn S. Mastering variation: variance components and personalised medicine. Statistics in Medicine. 2016; 35: 966–977.

[25] Santhakumaran S, Gordon A, Prevost AT, O’Kane C, McAuley DF, Shankar-Hari M. Heterogeneity of treatment effect by baseline risk of mortality in critically ill patients: re-analysis of three recent sepsis and ARDS randomised controlled trials. Critical Care. 2019; 23: 156.

[26] Kent DM, Rothwell PM, Ioannidis JPA, Altman DG, Hayward RA. Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal. Trials. 2010; 11: 85.

[27] Brower RG, Lanken PN, MacIntyre N, Matthay MA, Morris A, Ancukiewicz M, et al. Higher versus lower positive end-expiratory pressures in patients with the acute respiratory distress syndrome. The New England Journal of Medicine. 2004; 351: 327–336.

[28] Wiedemann HP, Wheeler AP, Bernard GR, Thompson BT, Hayden D, deBoisblanc B, et al. Comparison of two fluid-management strategies in acute lung injury. The New England Journal of Medicine. 2006; 354: 2564–2575.

[29] McAuley DF, Laffey JG, O’Kane CM, Perkins GD, Mullan B, Trinder TJ, et al. Simvastatin in the acute respiratory distress syndrome. The New England Journal of Medicine. 2014; 371: 1695–1703.

[30] Constantin J, Jabaudon M, Lefrant J, Jaber S, Quenot J, Langeron O, et al. Personalised mechanical ventilation tailored to lung morphology versus low positive end-expiratory pressure for patients with acute respiratory distress syndrome in France (the LIVE study): a multicentre, single-blind, randomised controlled trial. The Lancet Respiratory Medicine. 2019; 7: 870–880.

[31] Sinha P, Churpek MM, Calfee CS. Machine Learning Classifier Models can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data. American Journal of Respiratory and Critical Care Medicine. 2020; 202: 996–1004.

[32] Delucchi K, Famous KR, Ware LB, Parsons PE, Thompson BT, Calfee CS. Stability of ARDS subphenotypes over time in two randomised controlled trials. Thorax. 2018; 73: 439–445.

[33] Hashem MD, Hopkins RO, Colantuoni E, Dinglas VD, Sinha P, Aronson Friedman L, et al. Six-month and 12-month patient outcomes based on inflammatory subphenotypes in sepsis-associated ARDS: secondary analysis of SAILS-ALTOS trial. Thorax. 2021. (in press)

[34] Bos LDJ, Scicluna BP, Ong DSY, Cremer O, van der Poll T, Schultz MJ. Understanding Heterogeneity in Biologic Phenotypes of Acute Respiratory Distress Syndrome by Leukocyte Expression Profiles. American Journal of Respiratory and Critical Care Medicine. 2019; 200: 42–50.

[35] Heijnen NFL, Hagens LA, Smit MR, Cremer OL, Ong DSY, van der Poll T, et al. Biological Subphenotypes of Acute Respiratory Distress Syndrome Show Prognostic Enrichment in Mechanically Ventilated Patients without Acute Respiratory Distress Syndrome. American Journal of Respiratory and Critical Care Medicine. 2021; 203: 1503–1511.

[36] Drohan CM, Nouraie SM, Bain W, Shah FA, Evankovich J, Zhang Y, et al. Biomarker-Based Classification of Patients with Acute Respiratory Failure into Inflammatory Subphenotypes: a Single-Center Exploratory Study. Critical Care Explorations. 2021; 3: e0518.

[37] Sathe NA, Zelnick LR, Mikacenic C, Morrell ED, Bhatraju PK, McNeil JB, et al. Identification of persistent and resolving subphenotypes of acute hypoxemic respiratory failure in two independent cohorts. Critical Care. 2021; 25: 336.

[38] Sinha P, Calfee CS, Cherian S, Brealey D, Cutler S, King C, et al. Prevalence of phenotypes of acute respiratory distress syndrome in critically ill patients with COVID-19: a prospective observational study. The Lancet Respiratory Medicine. 2020; 8: 1209–1218.

[39] Heijnen NFL, Hagens LA, Smit MR, Schultz MJ, Poll T, Schnabel RM, et al. Biological subphenotypes of acute respiratory distress syndrome may not reflect differences in alveolar inflammation. Physiological Reports. 2021; 9: e14693.

[40] Jabaudon M, Blondonnet R, Lutz J, Roszyk L, Bouvier D, Guérin R, et al. Net alveolar fluid clearance is associated with lung morphology phenotypes in acute respiratory distress syndrome. Anaesthesia, Critical Care & Pain Medicine. 2016; 35: 81–86.

[41] Jabaudon M, Blondonnet R, Roszyk L, Bouvier D, Audard J, Clairefond G, et al. Soluble Receptor for Advanced Glycation End-Products Predicts Impaired Alveolar Fluid Clearance in Acute Respiratory Distress Syndrome. American Journal of Respiratory and Critical Care Medicine. 2015; 192: 191–199.


Abstracted / indexed in

Science Citation Index Expanded (SciSearch) Created as SCI in 1964, Science Citation Index Expanded now indexes over 9,200 of the world’s most impactful journals across 178 scientific disciplines. More than 53 million records and 1.18 billion cited references date back from 1900 to present.

Journal Citation Reports/Science Edition Journal Citation Reports/Science Edition aims to evaluate a journal’s value from multiple perspectives including the journal impact factor, descriptive data about a journal’s open access content as well as contributing authors, and provide readers a transparent and publisher-neutral data & statistics information about the journal.

Chemical Abstracts Service Source Index The CAS Source Index (CASSI) Search Tool is an online resource that can quickly identify or confirm journal titles and abbreviations for publications indexed by CAS since 1907, including serial and non-serial scientific and technical publications.

Index Copernicus The Index Copernicus International (ICI) Journals database’s is an international indexation database of scientific journals. It covered international scientific journals which divided into general information, contents of individual issues, detailed bibliography (references) sections for every publication, as well as full texts of publications in the form of attached files (optional). For now, there are more than 58,000 scientific journals registered at ICI.

Geneva Foundation for Medical Education and Research The Geneva Foundation for Medical Education and Research (GFMER) is a non-profit organization established in 2002 and it works in close collaboration with the World Health Organization (WHO). The overall objectives of the Foundation are to promote and develop health education and research programs.

Scopus: CiteScore 1.0 (2022) Scopus is Elsevier's abstract and citation database launched in 2004. Scopus covers nearly 36,377 titles (22,794 active titles and 13,583 Inactive titles) from approximately 11,678 publishers, of which 34,346 are peer-reviewed journals in top-level subject fields: life sciences, social sciences, physical sciences and health sciences.

Embase Embase (often styled EMBASE for Excerpta Medica dataBASE), produced by Elsevier, is a biomedical and pharmacological database of published literature designed to support information managers and pharmacovigilance in complying with the regulatory requirements of a licensed drug.

Submission Turnaround Time

Conferences

Top