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Intensive Care Medicine

, Volume 41, Issue 5, pp 814–822 | Cite as

Phenotypic clusters within sepsis-associated multiple organ dysfunction syndrome

  • Daniel B. Knox
  • Michael J. Lanspa
  • Kathryn G. Kuttler
  • Simon C. Brewer
  • Samuel M. Brown
Original

Abstract

Introduction

Sepsis is a devastating condition that is generally treated as a single disease. Identification of meaningfully distinct clusters may improve research, treatment and prognostication among septic patients. We therefore sought to identify clusters among patients with severe sepsis or septic shock.

Methods

We retrospectively studied all patients with severe sepsis or septic shock admitted directly from the emergency department to the intensive care units (ICUs) of three hospitals, 2006–2013. Using age and Sequential Organ Failure Assessment (SOFA) subscores, we defined clusters utilizing self-organizing maps, a method for representing multidimensional data in intuitive two-dimensional grids to facilitate cluster identification.

Results

We identified 2533 patients with severe sepsis or septic shock. Overall mortality was 17 %, with a mean APACHE II score of 24, mean SOFA score of 8 and a mean ICU stay of 5.4 days. Four distinct clusters were identified; (1) shock with elevated creatinine, (2) minimal multi-organ dysfunction syndrome (MODS), (3) shock with hypoxemia and altered mental status, and (4) hepatic disease. Mortality (95 % confidence intervals) for these clusters was 11 (8–14), 12 (11–14), 28 (25-32), and 21 (16–26) %, respectively (p < 0.0001). Regression modeling demonstrated that the clusters differed in the association between clinical outcomes and predictors, including APACHE II score.

Conclusions

We identified four distinct clusters of MODS among patients with severe sepsis or septic shock. These clusters may reflect underlying pathophysiological differences and could potentially facilitate tailored treatments or directed research.

Keywords

Sequential organ failure assessment (SOFA) Sepsis Multiple organ dysfunction syndrome (MODS) Cluster analysis Phenotyping Intensive Care Unit outcomes research 

Notes

Acknowledgments

The authors thank Allan J. Walkey, MD MSc and Allison Turnbull, DVM MPH PhD for close readings of the manuscript. This study was funded by National Institute of General Medical Sciences (1K23GM094465 to S.M.B.) and the Intermountain Research and Medical Foundation.

Conflicts of interest

The author(s) declare that they have no competing interest.

Supplementary material

134_2015_3764_MOESM1_ESM.pdf (173 kb)
Supplementary material 1 (PDF 173 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg and ESICM 2015

Authors and Affiliations

  • Daniel B. Knox
    • 1
    • 2
  • Michael J. Lanspa
    • 1
    • 2
  • Kathryn G. Kuttler
    • 1
    • 3
  • Simon C. Brewer
    • 4
  • Samuel M. Brown
    • 1
    • 2
    • 5
  1. 1.Pulmonary and Critical CareIntermountain Medical CenterSalt Lake CityUSA
  2. 2.Pulmonary and Critical CareUniversity of Utah School of MedicineSalt Lake CityUSA
  3. 3.Homer Warner Center for Informatics ResearchIntermountain HealthcareSalt Lake CityUSA
  4. 4.Geography DepartmentUniversity of UtahSalt Lake CityUSA
  5. 5.Shock Trauma Intensive Care UnitMurrayUSA

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