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Semin Respir Crit Care Med
DOI: 10.1055/s-0044-1787992
Review Article

Informative Subtyping of Patients with Sepsis

John Cafferkey 
1   Department of Anaesthesia, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
,
Manu Shankar-Hari 
2   Centre for Inflammation Research, Institute For Regeneration and Repair, University of Edinburgh, Edinburgh, Scotland, United Kingdom
› Author Affiliations Funding M.S.H. was supported by the National Institute of Health Research Clinician Scientist Award (award no.: CS-2016-16-011; 2017–2023). M.S.H. acknowledges a program grant named Time critical precision medicine for acute critical illness using treatable trait principles: TRAITS Program (PMAS/21/08) from Chief Scientist's Office, Scotland (see link https://traits-trial.ed.ac.uk ).
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Abstract

Sepsis pathobiology is complex. Heterogeneity refers to the clinical and biological variation within sepsis cohorts. Sepsis subtypes refer to subpopulations within sepsis cohorts derived based on these observable variations and latent features. The overarching goal of such endeavors is to enable precision immunomodulation. However, we are yet to identify immune endotypes of sepsis to achieve this goal. The sepsis subtyping field is just starting to take shape. The current subtypes in the literature do not have a core set of shared features between studies. Thus, in this narrative review, we reason that there is a need to a priori state the purpose of sepsis subtyping and minimum set of features that would be required to achieve the goal of precision immunomodulation for future sepsis.

Keywords

sepsis - phenotypes - endotypes - infectious disease - critical care - subtypes - precision medicine

Authors' Contributions

Structure of this invited narrative review was conceived by J.C. and M.S.H. Contributor J.C. had access to all the published cohort-level data summarized in this narrative review and take responsibility for the integrity of the data tabulation. The first draft of manuscript was by J.C. and underwent critical revision by J.C. and M.S.H. Administrative, technical, and material support was provided by M.S.H. Authors J.C. and M.S.H. have read the final draft of the manuscript and confirm the integrity of the work.


Note

The views expressed in this article are those of the authors and not necessarily those of the National Health Service, NIHR or Department of Health and Social Care. The funders of the study had no role in review or approval of the manuscript, or the decision to submit for publication.




Publication History

Article published online:
08 July 2024

© 2024. Thieme. All rights reserved.

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  • References

  • 1 Singer M, Deutschman CS, Seymour CW. et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016; 315 (08) 801-810
    CrossrefPubMedGoogle Scholar
  • 2 Shankar-Hari M, Phillips GS, Levy ML. et al; Sepsis Definitions Task Force. Developing a new definition and assessing new clinical criteria for septic shock: for the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016; 315 (08) 775-787
    CrossrefPubMedGoogle Scholar
  • 3 Rudd KE, Johnson SC, Agesa KM. et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet 2020; 395 (10219): 200-211
    CrossrefPubMedGoogle Scholar
  • 4 van der Poll T, Shankar-Hari M, Wiersinga WJ. The immunology of sepsis. Immunity 2021; 54 (11) 2450-2464
    CrossrefPubMedGoogle Scholar
  • 5 Marshall JC, Leligdowicz A. Gaps and opportunities in sepsis translational research. EBioMedicine 2022; 86: 104387
    CrossrefPubMedGoogle Scholar
  • 6 van Amstel RBE, Kennedy JN, Scicluna BP. et al; MARS Consortium. Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes. Intensive Care Med 2023; 49 (11) 1360-1369
    CrossrefPubMedGoogle Scholar
  • 7 DeMerle KM, Angus DC, Baillie JK. et al. Sepsis subclasses: a framework for development and interpretation. Crit Care Med 2021; 49 (05) 748-759
    CrossrefPubMedGoogle Scholar
  • 8 Shankar-Hari M, Calandra T, Soares MP. et al. Reframing sepsis immunobiology for translation: towards informative subtyping and targeted immunomodulatory therapies. Lancet Respir Med 2024; 12 (04) 323-336
    CrossrefPubMedGoogle Scholar
  • 9 McDonald VM, Osadnik CR, Gibson PG. Treatable traits in acute exacerbations of chronic airway diseases. Chron Respir Dis 2019; 16: 1479973119867954
    CrossrefPubMedGoogle Scholar
  • 10 Geri G, Vignon P, Aubry A. et al. Cardiovascular clusters in septic shock combining clinical and echocardiographic parameters: a post hoc analysis. Intensive Care Med 2019; 45 (05) 657-667
    CrossrefPubMedGoogle Scholar
  • 11 Bhavani SV, Wolfe KS, Hrusch CL. et al. Temperature trajectory subphenotypes correlate with immune responses in patients with sepsis. Crit Care Med 2020; 48 (11) 1645-1653
    CrossrefPubMedGoogle Scholar
  • 12 Ding M, Luo Y. Unsupervised phenotyping of sepsis using nonnegative matrix factorization of temporal trends from a multivariate panel of physiological measurements. BMC Med Inform Decis Mak 2021; 21 (5, Suppl 5): 95
    CrossrefPubMedGoogle Scholar
  • 13 Gårdlund B, Dmitrieva NO, Pieper CF, Finfer S, Marshall JC, Taylor Thompson B. Six subphenotypes in septic shock: latent class analysis of the PROWESS Shock study. J Crit Care 2018; 47: 70-79
    CrossrefPubMedGoogle Scholar
  • 14 Kudo D, Goto T, Uchimido R. et al. Coagulation phenotypes in sepsis and effects of recombinant human thrombomodulin: an analysis of three multicentre observational studies. Crit Care 2021; 25 (01) 114
    CrossrefPubMedGoogle Scholar
  • 15 Seymour CW, Kennedy JN, Wang S. et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA 2019; 321 (20) 2003-2017
    CrossrefPubMedGoogle Scholar
  • 16 Knox DB, Lanspa MJ, Kuttler KG, Brewer SC, Brown SM. Phenotypic clusters within sepsis-associated multiple organ dysfunction syndrome. Intensive Care Med 2015; 41 (05) 814-822
    CrossrefPubMedGoogle Scholar
  • 17 Bhavani SV, Semler M, Qian ET. et al. Development and validation of novel sepsis subphenotypes using trajectories of vital signs. Intensive Care Med 2022; 48: 1582-1592
    CrossrefPubMedGoogle Scholar
  • 18 Scicluna BP, van Vught LA, Zwinderman AH. et al; MARS consortium. Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study. Lancet Respir Med 2017; 5 (10) 816-826
    CrossrefPubMedGoogle Scholar
  • 19 Davenport EE, Burnham KL, Radhakrishnan J. et al. Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study. Lancet Respir Med 2016; 4 (04) 259-271
    CrossrefPubMedGoogle Scholar
  • 20 Sweeney TE, Azad TD, Donato M. et al. Unsupervised analysis of transcriptomics in bacterial sepsis across multiple datasets reveals three robust clusters. Crit Care Med 2018; 46 (06) 915-925
    CrossrefPubMedGoogle Scholar
  • 21 Zhang Z, Pan Q, Ge H, Xing L, Hong Y, Chen P. Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values. EBioMedicine 2020; 62: 103081
    CrossrefPubMedGoogle Scholar
  • 22 Baghela A, Pena OM, Lee AH. et al. Predicting sepsis severity at first clinical presentation: the role of endotypes and mechanistic signatures. EBioMedicine 2022; 75: 103776
    CrossrefPubMedGoogle Scholar
  • 23 Antcliffe DB, Burnham KL, Al-Beidh F. et al. Transcriptomic signatures in sepsis and a differential response to steroids. From the VANISH randomized trial. Am J Respir Crit Care Med 2019; 199 (08) 980-986
    CrossrefPubMedGoogle Scholar
  • 24 Datta D, Conway Morris A, Antonelli J. et al; ExPRES Sepsis Investigators. Early PREdiction of Severe Sepsis (ExPRES-Sepsis) study: protocol for an observational derivation study to discover potential leucocyte cell surface biomarkers. BMJ Open 2016; 6 (08) e011335
    CrossrefPubMedGoogle Scholar
  • 25 Bonaguro L, Schulte-Schrepping J, Ulas T, Aschenbrenner AC, Beyer M, Schultze JL. A guide to systems-level immunomics. Nat Immunol 2022; 23 (10) 1412-1423
    CrossrefPubMedGoogle Scholar
  • 26 Bhavani SV, Carey KA, Gilbert ER, Afshar M, Verhoef PA, Churpek MM. Identifying novel sepsis subphenotypes using temperature trajectories. Am J Respir Crit Care Med 2019; 200 (03) 327-335
    CrossrefPubMedGoogle Scholar
  • 27 Abe T, Ogura H, Shiraishi A. et al; JAAM FORECAST group. Characteristics, management, and in-hospital mortality among patients with severe sepsis in intensive care units in Japan: the FORECAST study. Crit Care 2018; 22 (01) 322
    CrossrefPubMedGoogle Scholar
  • 28 Cano-Gamez E, Burnham KL, Goh C. et al; GAinS Investigators. An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression. Sci Transl Med 2022; 14 (669) eabq4433
    CrossrefPubMedGoogle Scholar
  • 29 Sweeney TE, Thomas NJ, Howrylak JA, Wong HR, Rogers AJ, Khatri P. Multicohort analysis of whole-blood gene expression data does not form a robust diagnostic for acute respiratory distress syndrome. Crit Care Med 2018; 46 (02) 244-251
    CrossrefPubMedGoogle Scholar
  • 30 Sinha P, Kerchberger VE, Willmore A. et al. Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials. Lancet Respir Med 2023; 11 (11) 965-974
    CrossrefPubMedGoogle Scholar
  • 31 Bellani G, Laffey JG, Pham T. et al; LUNG SAFE Investigators, ESICM Trials Group. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA 2016; 315 (08) 788-800
    CrossrefPubMedGoogle Scholar
  • 32 Cohn O, Yankovitz G, Peshes-Yaloz N. et al. Distinct gene programs underpinning disease tolerance and resistance in influenza virus infection. Cell Syst 2022; 13 (12) 1002-1015.e9
    CrossrefPubMedGoogle Scholar
  • 33 Weis S, Carlos AR, Moita MR. et al. Metabolic adaptation establishes disease tolerance to sepsis. Cell 2017; 169 (07) 1263-1275.e14
    CrossrefPubMedGoogle Scholar
  • 34 Colaço HG, Barros A, Neves-Costa A. et al. Tetracycline antibiotics induce host-dependent disease tolerance to infection. Immunity 2021; 54 (01) 53-67.e7
    CrossrefPubMedGoogle Scholar
  • 35 Leventogiannis K, Kyriazopoulou E, Antonakos N. et al. Toward personalized immunotherapy in sepsis: the PROVIDE randomized clinical trial. Cell Rep Med 2022; 3 (11) 100817
    CrossrefPubMedGoogle Scholar
  • 36 Wolf DM, Yau C, Wulfkuhle J. et al; I-SPY2 Investigators. Redefining breast cancer subtypes to guide treatment prioritization and maximize response: predictive biomarkers across 10 cancer therapies. Cancer Cell 2022; 40 (06) 609-623.e6
    CrossrefPubMedGoogle Scholar