Fuzzy classification of sepsis subtypes and implications for trajectory and treatment.

Journal: EBioMedicine
Published Date: Jan 19, 2026

Abstract

BACKGROUND: Sepsis is common and deadly, and subtypes are proposed to guide precision treatment. However, little is known about the uncertainty in subtype classification, and its implications for trajectory and treatment response. METHODS: In multiple electronic health record and trial data of adults with sepsis, we assigned patients clinical sepsis subtypes (α, β, γ, or δ-type), and measured uncertainty by defining core (≥90%) and margin (<90%) strata for each subtype according to model-derived membership probabilities. In multivariable logistic regression models, we determined the association between subtype, core/margin strata, and two outcomes, i.) change in subtype over 48 h and ii.) 365-day mortality in the ProCESS randomised trial. FINDINGS: We included 35,691 adult patients (mean age 68 [SD 16] years; 51% male, 85% White, 5.7% in-hospital mortality) with community-acquired sepsis according to Sepsis-3. Most patients changed clinical sepsis subtype during the 48 h after presentation (82%) regardless of initial subtype. The majority of patients were in the margin stratum of the subtype (α-type: 70%, β-type: 66%, γ-type: 64%), except for those in δ-type (18% margin strata). The odds of subtype change over 48 h was increased in the margin strata (interaction p = 0.023), where, for example, patients with the margin delta subtype had significantly higher odds than patients with alpha core (ref) subtype (odds ratio, 7.13; 95% confidence interval [CI], 5.16-9.85). For risk-adjusted 365-day mortality in the ProCESS trial, the effect of randomised treatment was modified by the subtype margin strata (interaction p = 0.026). INTERPRETATION: In patients with community sepsis, clinical subtypes are dynamic. Patients on the subtype margin are more likely to change groups, and uncertainty of subtype classification modified treatment effects. FUNDING: National Institutes of Health, National Institute of General Medical Sciences (R35GM119519).

Authors

  • Jason N Kennedy
    Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Stuthi Iyer
    University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Peter C Nauka
    Department of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Katherine M Reitz
    Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Joyce Chang
    Department of Statistics, University of Pittsburgh, Graduate School of Public Health, Pittsburgh, PA, USA.
  • Lu Tang
    Department of Communication and Journalism, Texas A&M University.
  • Donald Yealy
    Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Derek C Angus
    Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Rombout B E Amstel
    Department of Intensive Care Medicine, Amsterdam UMC, Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands.
  • Lonneke A van Vught
    Department of Intensive Care Medicine, Amsterdam UMC, Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands.
  • Christopher W Seymour
    Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.

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