Skip to content
1887

Journal of Medical Microbiology logo Journal of Medical Microbiology

Volume 74, Issue 3

Sepsis : definition, development and application of an electronic phenotype for sepsis Open Access

Abstract

Repurposing electronic health record (EHR) or electronic medical record (EMR) data holds significant promise for evidence-based epidemic intelligence and research. Key challenges include sepsis recognition by physicians and issues with EHR and EMR data. Recent advances in data-driven techniques, alongside initiatives like the Surviving Sepsis Campaign and the Severe Sepsis and Septic Shock Management Bundle (SEP-1), have improved sepsis definition, early detection, subtype characterization, prognostication and personalized treatment. This includes identifying potential biomarkers or digital signatures to enhance diagnosis, guide therapy and optimize clinical management. Machine learning applications play a crucial role in identifying biomarkers and digital signatures associated with sepsis and its sub-phenotypes. Additionally, electronic phenotyping, leveraging EHR and EMR data, has emerged as a valuable tool for evidence-based sepsis identification and management. This review examines methods for identifying sepsis cohorts, focusing on two main approaches: utilizing health administrative data with standardized diagnostic coding via the International Classification of Diseases and integrating clinical data. This overview provides a comprehensive analysis of current cohort identification and electronic phenotyping strategies for sepsis, highlighting their potential applications and challenges. The accuracy of an electronic phenotype or signature is pivotal for precision medicine, enabling a shift from subjective clinical descriptions to data-driven insights.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): bloodstream infection , digital phenotype , machine learning , precision medicine and sepsis
Funding
This study was supported by the:
  • Western Australian Future Health and Innovation Fund, Government of Western Australia
    • Principal Award Recipient: TimothyJ. J. Inglis
  • National Health and Medical Research Council (Award 2021/GNT2012074;)
    • Principal Award Recipient: J. J. InglisTimothy
© 2025 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
Loading

Article metrics loading...

/content/journal/jmm/10.1099/jmm.0.001986
2025-03-28
2025-12-13

Metrics

Loading full text...

Full text loading...

/deliver/fulltext/jmm/74/3/jmm001986.html?itemId=/content/journal/jmm/10.1099/jmm.0.001986&mimeType=html&fmt=ahah

References

  1. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016; 315:801–810 [View Article] [PubMed]
     [Google Scholar]
  2. Neviere R, Parsons PE, Finlay G. Sepsis syndromes in adults: epidemiology, definitions, clinical presentation, diagnosis, and prognosis. Monograf´ıa en [Internet]. Wolters Kluwer; 2017
  3. Seymour CW, Liu VX, Iwashyna TJ, Brunkhorst FM, Rea TD et al. Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016; 315:762–774 [View Article] [PubMed]
     [Google Scholar]
  4. Shankar-Hari M, Phillips GS, Levy ML, Seymour CW, Liu VX et al. 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; 315:775–787 [View Article] [PubMed]
     [Google Scholar]
  5. Hotchkiss RS, Moldawer LL, Opal SM, Reinhart K, Turnbull IR et al. Sepsis and septic shock. Nat Rev Dis Primers 2016; 2:1–21 [View Article]
     [Google Scholar]
  6. Levy MM, Fink MP, Marshall JC, Abraham E, Angus D et al. 2001 sccm/esicm/accp/ats/sis international sepsis definitions conference. Crit Care Med 2003; 31:1250–1256 [View Article] [PubMed]
     [Google Scholar]
  7. Bone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Chest 1992; 101:1644–1655 [View Article]
     [Google Scholar]
  8. International Classification of Diseases (ICD) World Health Organization. https://www.who.int/standards/classifications/classification-of-diseases accessed 3 November 2023
  9. Independent Health and Aged Care Pricing Authority (IHACPA) ICD-10-AM/ACHI/ACS; 2024 https://www.ihacpa.gov.au/health-care/classification/icd-10-amachiacs#:˜:text=The%20ICD%2D10%2DAM%2F,injuries%20and%20related%20health%20problems
  10. Centers for Medicare and Medicaid Services FY 2022 ICD-10-CM Coding Guidelines; 2022 https://www.cms.gov/files/document/fy-2022-icd-10-cm-coding-guidelines-updated-02012022.pdf
  11. Rhee C, Klompas M. Sepsis trends: increasing incidence and decreasing mortality, or changing denominator?. J Thorac Dis 2020; 12:S89–S100 [View Article] [PubMed]
     [Google Scholar]
  12. Cadarette SM, Wong L. An introduction to health care administrative data. Can J Hosp Pharm 2015; 68:232–237 [View Article] [PubMed]
     [Google Scholar]
  13. Medicine I of What You Need to Know About Infectious Disease. Washington, DC: The National Academies Press; 2011 https://nap.nationalacademies.org/catalog/13006/what-you-need-to-know-about-infectious-disease
  14. Centers for Disease Control and Prevention About sepsis; 2023 https://www.cdc.gov/sepsis/about/index.html accessed 13 October 2023
  15. Australian Sepsis Network Australian Sepsis Network - Causes and Prevention; 2018 https://www.australiansepsisnetwork.net.au/community-%20awareness/causes-prevention accessed 13 October 2023
  16. Inglis TJJ. Speaking of sepsis: semantics, syntax, and slang. Front Med 2023; 10:1250499 [View Article] [PubMed]
     [Google Scholar]
  17. Reinhart K. How antimicrobial resistance amplifies the burden of sepsis; 2023 https://www.news-medical.net/health/How-Antimicrobial-Resistance-Amplifies-the-Burden-of-Sepsis.aspx accessed 13 October 2023
  18. Sheikh F, Douglas W, Catenacci V, Machon C, Fox-Robichaud AE. Social determinants of health associated with the development of sepsis in adults: A Scoping Review. Crit Care Explor 2022; 4:e0731 [View Article] [PubMed]
     [Google Scholar]
  19. Machon C, Sheikh F, Fox-Robichaud A. Social determinants of health associated with the development of sepsis in adults: a scoping review protocol. BMJ Open 2020; 10:e039146 [View Article] [PubMed]
     [Google Scholar]
  20. Kempker JA, Wang HE, Martin GS. Sepsis is a preventable public health problem. Crit Care 2018; 22:116 [View Article] [PubMed]
     [Google Scholar]
  21. Reinhart K, Daniels R, Kissoon N, Machado FR, Schachter RD et al. Recognizing sepsis as a global health priority - a WHO resolution. N Engl J Med 2017; 377:414–417 [View Article] [PubMed]
     [Google Scholar]
  22. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the global burden of disease study. The Lancet 2020; 395:200–211 [View Article]
     [Google Scholar]
  23. Liu V, Escobar GJ, Greene JD, Soule J, Whippy A et al. Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA 2014; 312:90–92 [View Article] [PubMed]
     [Google Scholar]
  24. Rhee C, Dantes R, Epstein L, Murphy DJ, Seymour CW et al. Incidence and trends of sepsis in us hospitals using clinical vs claims data, 2009-2014. JAMA 2017; 318:1241–1249 [View Article] [PubMed]
     [Google Scholar]
  25. Australia, Healthdirect Australian Sepsis Network. en-AU; 2021 https://www.healthdirect.gov.au/partners/sepsis-australia accessed 7 February 2023
  26. Australian Commission on Safety and Quality in Health Care Sepsis in Australia: Infographic; 2022 https://www.safetyandquality.gov.au/publications-and-resources/resource-library/infographic-sepsis-australia accessed 24 October 2023
  27. Skei NV, Moe K, Nilsen TIL, Aasdahl L, Prescott HC et al. Return to work after hospitalization for sepsis: a nationwide, registry-based cohort study. Crit Care 2023; 27:443 [View Article]
     [Google Scholar]
  28. Joynes E. More challenges around sepsis: definitions and diagnosis. J Thorac Dis 2016; 8:E1467–E1469 [View Article] [PubMed]
     [Google Scholar]
  29. Dellinger RP, Levy MM, Rhodes A, Annane D, Gerlach H et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med 2013; 39:165–228 [View Article] [PubMed]
     [Google Scholar]
  30. Bedoya AD, Futoma J, Clement ME, Corey K, Brajer N et al. Machine learning for early detection of sepsis: an internal and temporal validation study. JAMIA Open 2020; 3:252–260 [View Article] [PubMed]
     [Google Scholar]
  31. Society of Critical Care Medicine (SCCM) Surviving sepsis campaign - about SSC; 2023 https://www.sccm.org/survivingsepsiscampaign accessed 2 November 2023
  32. Wang J, Strich JR, Applefeld WN, Sun J, Cui X et al. Driving blind: instituting SEP-1 without high quality outcomes data. J Thorac Dis 2020; 12:S22–S36 [View Article]
     [Google Scholar]
  33. Sloan SNB, Rodriguez N, Seward T, Sare L, Moore L et al. Compliance with SEP-1 guidelines is associated with improved outcomes for septic shock but not for severe sepsis. J Intensive Med 2022; 2:167–172 [View Article] [PubMed]
     [Google Scholar]
  34. Pendergrass SA, Crawford DC. Using electronic health records to generate phenotypes for research. Curr Protoc Hum Genet 2019; 100:e80 [View Article] [PubMed]
     [Google Scholar]
  35. Casey JA, Schwartz BS, Stewart WF, Adler NE. Using electronic health records for population health research: a review of methods and applications. Annu Rev Public Health 2016; 37:61–81 [View Article] [PubMed]
     [Google Scholar]
  36. Bastarache L. Using phecodes for research with the electronic health record: from PheWAS to PheRS. Annu Rev Biomed Data Sci 2021; 4:1–19 [View Article] [PubMed]
     [Google Scholar]
  37. Leader JB, Pendergrass SA, Verma A, Carey DJ, Hartzel DN et al. Contrasting association results between existing PheWAS phenotype definition methods and five validated electronic phenotypes. AMIA Annu Symp Proc 2015; 2015:824–832 [PubMed]
     [Google Scholar]
  38. Wei WQ, Denny JC. Extracting research-quality phenotypes from electronic health records to support precision medicine. Genome Med 2015; 7:41 [View Article] [PubMed]
     [Google Scholar]
  39. Chiang HY, Liang LY, Lin CC, Chen YJ, Wu MY et al. Electronic medical record-based deep data cleaning and phenotyping improve the diagnostic validity and mortality assessment of infective endocarditis: medical big data initiative of CMUH. Biomed 2021; 11:59–67 [View Article] [PubMed]
     [Google Scholar]
  40. Banda JM, Seneviratne M, Hernandez-Boussard T, Shah NH. Advances in electronic phenotyping: from rule-based definitions to machine learning models. Annu Rev Biomed Data Sci 2018; 1:53–68 [View Article] [PubMed]
     [Google Scholar]
  41. Why your EHR is not enough to improve sepsis outcomes. en. n.d https://www.wolterskluwer.com/en/expert-insights/why-your-ehr-is-not-enough-to-improve-sepsis-outcomes accessed 1 December 2023
  42. Joshi M, Mecklai K, Rozenblum R, Samal L. Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: a qualitative study. JAMIA Open 2022; 5:ooac022 [View Article] [PubMed]
     [Google Scholar]
  43. Fleuren LM, Klausch TLT, Zwager CL, Schoonmade LJ, Guo T et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med 2020; 46:383–400 [View Article] [PubMed]
     [Google Scholar]
  44. Donnelly JP, Dai Y, Colantonio LD, Zhao H, Safford MM et al. Agreement of claims-based methods for identifying sepsis with clinical criteria in the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. BMC Med Res Methodol 2020; 20:54 [View Article] [PubMed]
     [Google Scholar]
  45. Rhee C, Jentzsch MS, Kadri SS, Seymour CW, Angus DC et al. Variation in identifying sepsis and organ dysfunction using administrative versus electronic clinical data and impact on hospital outcome comparisons. Crit Care Med 2019; 47:493–500 [View Article] [PubMed]
     [Google Scholar]
  46. Niemantsverdriet MSA, de Hond TAP, Hoefer IE, van Solinge WW, Bellomo D et al. A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department. BMC Emerg Med 2022; 22:208 [View Article] [PubMed]
     [Google Scholar]
  47. de Hond TAP, Niemantsverdriet MSA, van Solinge WW, Oosterheert JJ, Haitjema S et al. Sepsis labels defined by claims-based methods are ill-suited for training machine learning algorithms. Clinic Microb Infect 2022; 28:1170–1171 [View Article]
     [Google Scholar]
  48. Insco AM. Sepsis: do the clinical criteria support the medical coding? PhD thesis. The University of Tennessee Health Science Center 2022
     [Google Scholar]
  49. Sand J, Kuqi A. Current challenges in sepsis documentation and coding: a review of the literature. Perspect Health Inf Manag 202320
     [Google Scholar]
  50. Jolley RJ, Quan H, Jetté N, Sawka KJ, Diep L et al. Validation and optimisation of an ICD-10-coded case definition for sepsis using administrative health data. BMJ Open 2015; 5:e009487 [View Article] [PubMed]
     [Google Scholar]
  51. Romano PS, Mark DH. Bias in the coding of hospital discharge data and its implications for quality assessment. Med Care 1994; 32:81–90 [View Article] [PubMed]
     [Google Scholar]
  52. Kumar A, Hammond N, Delaney A, Thompson K, Finfer S. Validity of ICD coding methodologies in estimating sepsis epidemiology: a scoping review. Aust Crit Care 2022; 35:S10 [View Article]
     [Google Scholar]
  53. Centers for Medicare & Medicaid Services (CMS) ICD-10; 2024 https://www.cms.gov/medicare/coding-billing/icd-10-codes?redirec t=/ICD10 accessed 17 January 2024
  54. Chan HK, Khose S, Chavez S, Patel B, Wang HE. Updated estimates of sepsis hospitalizations at United States academic medical centers. J Am Coll Emerg Physicians Open 2022; 3:e12782 [View Article] [PubMed]
     [Google Scholar]
  55. Liu B, Hadzi-Tosev M, Liu Y, Lucier KJ, Garg A et al. Accuracy of International classification of diseases, 10th revision codes for identifying sepsis: a systematic review and meta-analysis. Crit Care Explor 2022; 4:e0788 [View Article] [PubMed]
     [Google Scholar]
  56. Duke GJ, Bishara M, Hirth S, Lim LL, Worth LJ. Performance of hospital administrative data for detection of sepsis in Australia: the sepsis coding and documentation (SECOND) study. Health Inf Manag 2024; 53:61–67 [View Article] [PubMed]
     [Google Scholar]
  57. Classification Development AC For Australian Coding Standards; 2015
  58. Mellhammar L, Wollter E, Dahlberg J, Donovan B, Olséen CJ et al. Estimating sepsis incidence using administrative data and clinical medical record review. JAMA Netw Open 2023; 6:e2331168 [View Article]
     [Google Scholar]
  59. Centers for Disease Control and Prevention (CDC) Hospital toolkit for adult sepsis surveillance; 2018 https://stacks.cdc.gov/view/cdc/59358 accessed 3 November 2023
  60. Gohil SK, Cao C, Phelan M, Tjoa T, Rhee C et al. Impact of policies on the rise in sepsis incidence, 2000-2010. Clin Infect Dis 2016; 62:695–703 [View Article] [PubMed]
     [Google Scholar]
  61. Rhee C, Gohil S, Klompas M. Regulatory mandates for sepsis care--reasons for caution. N Engl J Med 2014; 370:1673–1676 [View Article] [PubMed]
     [Google Scholar]
  62. Academy of Medical Royal Colleges Statement on the initial antimicrobial treatment of sepsis; 2022 https://www.aomrc.org.uk/wp-content/uploads/2022/10/Statement_on_the_initial_antimicrobial_treatment_of_sepsis_V2_1022.pdf accessed 2 November 2023
  63. Rhee C, Klompas M. Should hospital-onset adult sepsis event surveillance be routine… or even mandatory?. ASHE 2022; 2:e32 [View Article]
     [Google Scholar]
  64. Rhee C, Zhang Z, Kadri SS, Murphy DJ, Martin GS et al. Sepsis surveillance using adult sepsis events simplified eSOFA criteria versus sepsis-3 sequential organ failure assessment criteria. Crit Care Med 2019; 47:307–314 [View Article] [PubMed]
     [Google Scholar]
  65. Robinson JR, Wei W-Q, Roden DM, Denny JC. Defining phenotypes from clinical data to drive genomic research. Annu Rev Biomed Data Sci 2018; 1:69–92 [View Article]
     [Google Scholar]
  66. Velasco E, Agheneza T, Denecke K, Kirchner G, Eckmanns T. Social media and internet-based data in global systems for public health surveillance: a systematic review. Milbank Q 2014; 92:7–33 [View Article] [PubMed]
     [Google Scholar]
  67. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res 2009; 11:e11 [View Article] [PubMed]
     [Google Scholar]
  68. Yang S, Varghese P, Stephenson E, Tu K, Gronsbell J. Machine learning approaches for electronic health records phenotyping: a methodical review. J Am Med Inform Assoc 2023; 30:367–381 [View Article] [PubMed]
     [Google Scholar]
  69. Komorowski M, Green A, Tatham KC, Seymour C, Antcliffe D. Sepsis biomarkers and diagnostic tools with a focus on machine learning. EBioMedicine 2022; 86:104394 [View Article] [PubMed]
     [Google Scholar]
  70. Goh KH, Wang L, Yeow AYK, Poh H, Li K et al. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun 2021; 12:711 [View Article] [PubMed]
     [Google Scholar]
  71. Zhu XJ. Semi-supervised learning literature survey; 2005
  72. Bashiri FS, Caskey JR, Mayampurath A, Dussault N, Dumanian J et al. Identifying infected patients using semi-supervised and transfer learning. J Am Med Inform Assoc 2022; 29:1696–1704 [View Article] [PubMed]
     [Google Scholar]
  73. Halpern Y, Horng S, Choi Y, Sontag D. Electronic medical record phenotyping using the anchor and learn framework. J Am Med Inform Assoc 2016; 23:731–740 [View Article] [PubMed]
     [Google Scholar]
  74. Yu S, Chakrabortty A, Liao KP, Cai T, Ananthakrishnan AN et al. Surrogate-assisted feature extraction for high-throughput phenotyping. J Am Med Inform Assoc 2017; 24:e143–e149 [View Article] [PubMed]
     [Google Scholar]
  75. Nogues IE, Wen J, Lin Y, Liu M, Tedeschi SK et al. Weakly semi-supervised phenotyping using electronic health records. J Biomed Inform 2022; 134:104175 [View Article] [PubMed]
     [Google Scholar]
  76. Li H, Markal A, Balch JA, Loftus TJ, Efron PA et al. Methods for phenotyping adult patients in sepsis and septic shock: a Scoping Review. Crit Care Explor 2022; 4:e0672 [View Article] [PubMed]
     [Google Scholar]
  77. Seymour CW, Kennedy JN, Wang S, Chang C-CH, Elliott CF et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA 2019; 321:2003–2017 [View Article] [PubMed]
     [Google Scholar]
  78. Hu C, Li Y, Wang F, Peng Z. Application of machine learning for clinical subphenotype identification in sepsis. Infect Dis Ther 2022; 11:1949–1964 [View Article] [PubMed]
     [Google Scholar]
  79. Shappell CN, Klompas M, Rhee C. Surveillance strategies for tracking sepsis incidence and outcomes. J Infect Dis 2020; 222:S74–S83 [View Article] [PubMed]
     [Google Scholar]
  80. Fleischmann-Struzek C, Thomas-Rüddel DO, Schettler A, Schwarzkopf D, Stacke A et al. Comparing the validity of different ICD coding abstraction strategies for sepsis case identification in German claims data. PLoS One 2018; 13:e0198847 [View Article]
     [Google Scholar]
  81. Jordan Kempker A, Rudd KE, Wang HE, Martin GS. Sepsis epidemiology across the international classification of diseases, 9th edition, to international classification of diseases, 10th edition, chasm—a direct application of the institute for health metrics and evaluation case definition to hospital discharge data*. Crit Care Med 2020; 48:1881–1884 [View Article]
     [Google Scholar]
  82. Rhee C, Murphy MV, Li L, Platt R, Klompas M et al. Comparison of trends in sepsis incidence and coding using administrative claims versus objective clinical data. Clin Infect Dis 2015; 60:88–95 [View Article] [PubMed]
     [Google Scholar]
  83. Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J et al. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001; 29:1303–1310 [View Article] [PubMed]
     [Google Scholar]
  84. Dombrovskiy VY, Martin AA, Sunderram J, Paz HL. Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: a trend analysis from 1993 to 2003. Critical Care Medicine 2007; 35:1244–1250 [View Article]
     [Google Scholar]
  85. Martin GS, Mannino DM, Eaton S, Moss M. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med 2003; 348:1546–1554 [View Article] [PubMed]
     [Google Scholar]
  86. Centers for Medicare and Medicaid Services Implementation of severe sepsis and septic shock: management bundle measure (NQF# 0500). Tech Rep NQF 2012 [View Article]
     [Google Scholar]
  87. Buchman TG, Simpson SQ, Sciarretta KL, Finne KP, Sowers N et al. Sepsis among medicare beneficiaries: 1. the burdens of sepsis, 2012-2018. Crit Care Med 2020; 48:276–288 [View Article] [PubMed]
     [Google Scholar]
  88. Baghdadi JD, Wong MD, Uslan DZ, Bell D, Cunningham WE et al. Adherence to the SEP-1 sepsis bundle in hospital-onset v. community-onset sepsis: a multicenter retrospective cohort study. J Gen Intern Med 2020; 35:1153–1160 [View Article] [PubMed]
     [Google Scholar]
/content/journal/jmm/10.1099/jmm.0.001986
Loading
Sepsis in silico: definition, development and application of an electronic phenotype for sepsis
J. Med. Microbiol. 74, 001986 (2025); https://doi.org/10.1099/jmm.0.001986
/content/journal/jmm/10.1099/jmm.0.001986
/content/journal/jmm/10.1099/jmm.0.001986
Loading

Data & Media loading...

Most cited Most Cited RSS feed

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