1932

Abstract

Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly being recognized as one of the principal barriers to finding efficacious targeted therapies. The advent of multiple high-throughput biological data (“omics”), coupled with the widespread access to increased computational power, has led to the emergence of phenotyping in critical care. Phenotyping aims to use a multitude of data to identify homogenous subgroups within an otherwise heterogenous population. Increasingly, phenotyping schemas are being applied to sepsis and ARDS to increase understanding of these clinical conditions and identify potential therapies. Here we present a selective review of the biological phenotyping schemas applied to sepsis and ARDS. Further, we outline some of the challenges involved in translating these conceptual findings to bedside clinical decision-making tools.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-med-043021-014005
2023-01-27
2024-04-30
Loading full text...

Full text loading...

/deliver/fulltext/med/74/1/annurev-med-043021-014005.html?itemId=/content/journals/10.1146/annurev-med-043021-014005&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Bernard GR, Artigas A, Brigham KL et al. 1994. The American-European Consensus Conference on ARDS. Definitions, mechanisms, relevant outcomes, and clinical trial coordination. Am. J. Respir. Crit. Care Med. 149:818–24
    [Google Scholar]
  2. 2.
    Bone RC, Balk RA, Cerra FB et al. 1992. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest 101:1644–55
    [Google Scholar]
  3. 3.
    Marshall JC. 2014. Why have clinical trials in sepsis failed?. Trends Mol. Med. 20:195–203
    [Google Scholar]
  4. 4.
    Bone RC. 1996. Why sepsis trials fail. JAMA 276:565–66
    [Google Scholar]
  5. 5.
    Matthay MA, McAuley DF, Ware LB. 2017. Clinical trials in acute respiratory distress syndrome: challenges and opportunities. Lancet Respir. Med. 5:524–34
    [Google Scholar]
  6. 6.
    Laffey JG, Kavanagh BP. 2018. Negative trials in critical care: why most research is probably wrong. Lancet Respir. Med. 6:659–60
    [Google Scholar]
  7. 7.
    Harhay MO, Wagner J, Ratcliffe SJ et al. 2014. Outcomes and statistical power in adult critical care randomized trials. Am. J. Respir. Crit. Care Med. 189:1469–78
    [Google Scholar]
  8. 8.
    Drawnel FM, Zhang JD, Kung E et al. 2017. Molecular phenotyping combines molecular information, biological relevance, and patient data to improve productivity of early drug discovery. Cell Chem. Biol. 24:624–34.e3
    [Google Scholar]
  9. 9.
    Shivade C, Raghavan P, Fosler-Lussier E et al. 2014. A review of approaches to identifying patient phenotype cohorts using electronic health records. J. Am. Med. Inform. Assoc. 21:221–30
    [Google Scholar]
  10. 10.
    Corren J, Lemanske RF, Hanania NA et al. 2011. Lebrikizumab treatment in adults with asthma. N. Engl. J. Med. 365:1088–98
    [Google Scholar]
  11. 11.
    Romond EH, Perez EA, Bryant J et al. 2005. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N. Engl. J. Med. 353:1673–84
    [Google Scholar]
  12. 12.
    Guerin C, Reignier J, Richard JC et al. 2013. Prone positioning in severe acute respiratory distress syndrome. N. Engl. J. Med. 368:2159–68
    [Google Scholar]
  13. 13.
    Papazian L, Forel JM, Gacouin A et al. 2010. Neuromuscular blockers in early acute respiratory distress syndrome. N. Engl. J. Med. 363:1107–16
    [Google Scholar]
  14. 14.
    Sinha P, Spicer A, Delucchi KL et al. 2021. Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: a secondary analysis of three randomised controlled trials. EBioMedicine 74:103697
    [Google Scholar]
  15. 15.
    Sinha P, Calfee CS. 2019. Phenotypes in acute respiratory distress syndrome: moving towards precision medicine. Curr. Opin. Crit. Care 25:12–20
    [Google Scholar]
  16. 16.
    Reddy K, Sinha P, O'Kane CM et al. 2020. Subphenotypes in critical care: translation into clinical practice. Lancet Respir. Med. 8:631–43
    [Google Scholar]
  17. 17.
    Sinha P, Bos LD. 2021. Pathophysiology of the acute respiratory distress syndrome: insights from clinical studies. Crit. Care Clin. 37:795–815
    [Google Scholar]
  18. 18.
    Wong HR, Cvijanovich N, Lin R et al. 2009. Identification of pediatric septic shock subclasses based on genome-wide expression profiling. BMC Med. 7:34
    [Google Scholar]
  19. 19.
    Wong HR, Cvijanovich NZ, Allen GL et al. 2011. Validation of a gene expression-based subclassification strategy for pediatric septic shock. Crit. Care Med. 39:2511–17
    [Google Scholar]
  20. 20.
    Wong HR, Wheeler DS, Tegtmeyer K et al. 2010. Toward a clinically feasible gene expression-based subclassification strategy for septic shock: proof of concept. Crit. Care Med. 38:1955–61
    [Google Scholar]
  21. 21.
    Wong HR, Cvijanovich NZ, Anas N et al. 2015. Developing a clinically feasible personalized medicine approach to pediatric septic shock. Am. J. Respir. Crit. Care Med. 191:309–15
    [Google Scholar]
  22. 22.
    Davenport EE, Burnham KL, Radhakrishnan J et al. 2016. Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study. Lancet Respir. Med. 4:259–71
    [Google Scholar]
  23. 23.
    Burnham KL, Davenport EE, Radhakrishnan J et al. 2017. Shared and distinct aspects of the sepsis transcriptomic response to fecal peritonitis and pneumonia. Am. J. Respir. Crit. Care Med. 196:328–39
    [Google Scholar]
  24. 24.
    Antcliffe DB, Burnham KL, Al-Beidh F et al. 2019. Transcriptomic signatures in sepsis and a differential response to steroids. From the VANISH Randomized Trial. Am. J. Respir. Crit. Care Med. 199:980–86
    [Google Scholar]
  25. 25.
    Scicluna BP, van Vught LA, Zwinderman AH et al. 2017. Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study. Lancet Respir. Med. 5:816–26
    [Google Scholar]
  26. 26.
    Sweeney TE, Azad TD, Donato M et al. 2018. Unsupervised analysis of transcriptomics in bacterial sepsis across multiple datasets reveals three robust clusters. Crit. Care Med. 46:915–25
    [Google Scholar]
  27. 27.
    Sweeney TE, Liesenfeld O, Wacker J et al. 2021. Validation of inflammopathic, adaptive, and coagulopathic sepsis endotypes in coronavirus disease 2019. Crit. Care Med. 49:e170–78
    [Google Scholar]
  28. 28.
    Yao L, Rey DA, Bulgarelli L et al. 2022. Gene expression scoring of immune activity levels for precision use of hydrocortisone in vasodilatory shock. Shock 57:384–91
    [Google Scholar]
  29. 29.
    Cohen J, Blumenthal A, Cuellar-Partida G et al. 2021. The relationship between adrenocortical candidate gene expression and clinical response to hydrocortisone in patients with septic shock. Intens. Care Med. 47:974–83
    [Google Scholar]
  30. 30.
    Reyes M, Filbin MR, Bhattacharyya RP et al. 2020. An immune-cell signature of bacterial sepsis. Nat. Med. 26:333–40
    [Google Scholar]
  31. 31.
    Monneret G, Lepape A, Voirin N et al. 2006. Persisting low monocyte human leukocyte antigen-DR expression predicts mortality in septic shock. Intens. Care Med. 32:1175–83
    [Google Scholar]
  32. 32.
    Heftrig D, Sturm R, Oppermann E et al. 2017. Impaired surface expression of HLA-DR, TLR2, TLR4, and TLR9 in ex vivo–in vitro stimulated monocytes from severely injured trauma patients. Mediators Inflamm. 2017:2608349
    [Google Scholar]
  33. 33.
    Bodinier M, Peronnet E, Brengel-Pesce K et al. 2021. Monocyte trajectories endotypes are associated with worsening in septic patients. Front. Immunol. 12:795052
    [Google Scholar]
  34. 34.
    Leijte GP, Rimmele T, Kox M et al. 2020. Monocytic HLA-DR expression kinetics in septic shock patients with different pathogens, sites of infection and adverse outcomes. Crit. Care 24:110
    [Google Scholar]
  35. 35.
    Mathew D, Giles JR, Baxter AE et al. 2020. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science 369:1122–27
    [Google Scholar]
  36. 36.
    Laing AG, Lorenc A, Del Molino Del Barrio I et al. 2020. A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat. Med. 26:1623–35
    [Google Scholar]
  37. 37.
    Dunning J, Blankley S, Hoang LT et al. 2018. Progression of whole-blood transcriptional signatures from interferon-induced to neutrophil-associated patterns in severe influenza. Nat. Immunol. 19:625–35
    [Google Scholar]
  38. 38.
    Mazer MB, Caldwell CC, Hanson J et al. 2021. A whole blood enzyme-linked immunospot assay for functional immune endotyping of septic patients. J. Immunol. 206:23–36
    [Google Scholar]
  39. 39.
    Wang Y, Gloss B, Tang B et al. 2021. Immunophenotyping of Peripheral blood mononuclear cells in septic shock patients with high-dimensional flow cytometry analysis reveals two subgroups with differential responses to immunostimulant drugs. Front. Immunol. 12:634127
    [Google Scholar]
  40. 40.
    Panacek EA, Marshall JC, Albertson TE et al. 2004. Efficacy and safety of the monoclonal anti-tumor necrosis factor antibody F(ab′)2 fragment afelimomab in patients with severe sepsis and elevated interleukin-6 levels. Crit. Care Med. 32:2173–82
    [Google Scholar]
  41. 41.
    Meyer NJ, Reilly JP, Anderson BJ et al. 2018. Mortality benefit of recombinant human interleukin-1 receptor antagonist for sepsis varies by initial interleukin-1 receptor antagonist plasma concentration. Crit. Care Med. 46:21–28
    [Google Scholar]
  42. 42.
    Janz DR, Bastarache JA, Rice TW et al. 2015. Randomized, placebo-controlled trial of acetaminophen for the reduction of oxidative injury in severe sepsis: the Acetaminophen for the Reduction of Oxidative Injury in Severe Sepsis trial. Crit. Care Med. 43:534–41
    [Google Scholar]
  43. 43.
    Kudo D, Goto T, Uchimido R et al. 2021. Coagulation phenotypes in sepsis and effects of recombinant human thrombomodulin: an analysis of three multicentre observational studies. Crit. Care 25:114
    [Google Scholar]
  44. 44.
    Shankar-Hari M, Santhakumaran S, Prevost AT et al. 2021. Defining phenotypes and treatment effect heterogeneity to inform acute respiratory distress syndrome and sepsis trials: secondary analyses of three RCTs. Effic. Mech. Eval. 8:10 https://doi.org/10.3310/eme08100
    [Crossref] [Google Scholar]
  45. 45.
    Wiersema R, Jukarainen S, Vaara ST et al. 2020. Two subphenotypes of septic acute kidney injury are associated with different 90-day mortality and renal recovery. Crit. Care 24:150
    [Google Scholar]
  46. 46.
    Madushani R, Patel V, Loftus T et al. 2022. Early biomarker signatures in surgical sepsis. J. Surg. Res. 277:372–83
    [Google Scholar]
  47. 47.
    Yehya N, Varisco BM, Thomas NJ et al. 2020. Peripheral blood transcriptomic sub-phenotypes of pediatric acute respiratory distress syndrome. Crit. Care 24:681
    [Google Scholar]
  48. 48.
    Kangelaris KN, Prakash A, Liu KD et al. 2015. Increased expression of neutrophil-related genes in patients with early sepsis-induced ARDS. Am. J. Physiol. Lung Cell. Mol. Physiol. 308:L1102–13
    [Google Scholar]
  49. 49.
    Kangelaris KN, Clemens R, Fang X et al. 2021. A neutrophil subset defined by intracellular olfactomedin 4 is associated with mortality in sepsis. Am. J. Physiol. Lung Cell. Mol. Physiol. 320:L892–902
    [Google Scholar]
  50. 50.
    Juss JK, House D, Amour A et al. 2016. Acute respiratory distress syndrome neutrophils have a distinct phenotype and are resistant to phosphoinositide 3-kinase inhibition. Am. J. Respir. Crit. Care Med. 194:961–73
    [Google Scholar]
  51. 51.
    Morrell ED, Radella F 2nd, Manicone AM et al. 2018. Peripheral and alveolar cell transcriptional programs are distinct in acute respiratory distress syndrome. Am. J. Respir. Crit. Care Med. 197:528–32
    [Google Scholar]
  52. 52.
    Morrell ED, Bhatraju PK, Mikacenic CR et al. 2019. Alveolar macrophage transcriptional programs are associated with outcomes in acute respiratory distress syndrome. Am. J. Respir. Crit. Care Med. 200:732–41
    [Google Scholar]
  53. 53.
    Rubin DB, Wiener-Kronish JP, Murray JF et al. 1990. Elevated von Willebrand factor antigen is an early plasma predictor of acute lung injury in nonpulmonary sepsis syndrome. J. Clin. Investig. 86:474–80
    [Google Scholar]
  54. 54.
    Sinha P, Delucchi KL, Chen Y et al. 2022. Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study. Thorax 77:13–21
    [Google Scholar]
  55. 55.
    Sinha P, Delucchi KL, Thompson BT et al. 2018. Latent class analysis of ARDS subphenotypes: a secondary analysis of the statins for acutely injured lungs from sepsis (SAILS) study. Intens. . Care Med. 44:1859–69
    [Google Scholar]
  56. 56.
    Famous KR, Delucchi K, Ware LB et al. 2017. Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy. Am. J. Respir. Crit. Care Med. 195:331–38
    [Google Scholar]
  57. 57.
    Calfee CS, Delucchi KL, Sinha P et al. 2018. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir. Med. 6:691–98
    [Google Scholar]
  58. 58.
    Calfee CS, Delucchi K, Parsons PE et al. 2014. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir. Med. 2:611–20
    [Google Scholar]
  59. 59.
    Sinha P, Furfaro D, Cummings MJ et al. 2021. Latent class analysis reveals COVID-19-related acute respiratory distress syndrome subgroups with differential responses to corticosteroids. Am. J. Respir. Crit. Care Med. 204:1274–85
    [Google Scholar]
  60. 60.
    Dahmer MK, Yang G, Zhang M et al. 2022. Identification of phenotypes in paediatric patients with acute respiratory distress syndrome: a latent class analysis. Lancet Respir. Med. 10:289–97
    [Google Scholar]
  61. 61.
    Maddali MV, Churpek M, Pham T et al. 2022. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis. Lancet Respir. Med. 10:367–77
    [Google Scholar]
  62. 62.
    Sinha P, Calfee CS, Cherian S et al. 2020. Prevalence of phenotypes of acute respiratory distress syndrome in critically ill patients with COVID-19: a prospective observational study. Lancet Respir. Med. 8:1209–18
    [Google Scholar]
  63. 63.
    Sinha P, Delucchi KL, McAuley DF et al. 2020. Development and validation of parsimonious algorithms to classify acute respiratory distress syndrome phenotypes: a secondary analysis of randomised controlled trials. Lancet Respir. Med. 8:247–57
    [Google Scholar]
  64. 64.
    Heijnen NFL, Hagens LA, Smit MR et al. 2021. Biological subphenotypes of acute respiratory distress syndrome show prognostic enrichment in mechanically ventilated patients without acute respiratory distress syndrome. Am. J. Respir. Crit. Care Med. 203:1503–11
    [Google Scholar]
  65. 65.
    Bos LD, Schouten LR, van Vught LA et al. 2017. Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis. Thorax 72:876–83
    [Google Scholar]
  66. 66.
    Sinha P, Churpek MM, Calfee CS. 2020. Machine learning classifier models can identify acute respiratory distress syndrome phenotypes using readily available clinical data. Am. J. Respir. Crit. Care Med. 202:996–1004
    [Google Scholar]
  67. 67.
    Delucchi K, Famous KR, Ware LB et al. 2018. Stability of ARDS subphenotypes over time in two randomised controlled trials. Thorax 73:439–45
    [Google Scholar]
  68. 68.
    Mayr VD, Dunser MW, Greil V et al. 2006. Causes of death and determinants of outcome in critically ill patients. Crit. Care 10:R154
    [Google Scholar]
  69. 69.
    Talwar S, Munson PJ, Barb J et al. 2006. Gene expression profiles of peripheral blood leukocytes after endotoxin challenge in humans. Physiol. Genom. 25:203–15
    [Google Scholar]
  70. 70.
    Cazalis MA, Lepape A, Venet F et al. 2014. Early and dynamic changes in gene expression in septic shock patients: a genome-wide approach. Intens. Care Med. Exp. 2:20
    [Google Scholar]
/content/journals/10.1146/annurev-med-043021-014005
Loading
/content/journals/10.1146/annurev-med-043021-014005
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error