Predicting Sepsis: A Comparison of Analytical Approaches

  • Femida Gwadry-Sridhar
  • Ali Hamou
  • Benoit Lewden
  • Claudio Martin
  • Michael Bauer
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 69)

Abstract

Sepsis is a significant cause of mortality and morbidity and is often associated with increased hospital resource utilization, prolonged intensive care unit and hospital stay. With advances in medicine, there is now aggressive goal oriented treatments that can be used to help patients that may be at risk for sepsis. To predict this risk, we hypothesized that commonly used univariate and multivariate models could be enhanced by using multiple analytic methods to providing greater precision. As a first step, we analyze data about patients with and without sepsis using multiple regression, decision trees and cluster analysis. We compare the predictive accuracy of the three different approaches in predicting which patients are likely (or not likely) to develop sepsis. The precision analysis suggests that decision trees may provide a better predictive model than either regression methods or cluster analysis.

Keywords

Intensive Care Unit Decision Tree Septic Shock Severe Sepsis Intensive Care Unit Patient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Dellinger, R.P., Levy, M.M., Carlet, J.M., et al.: Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit. Care Med. 36(1), 296–327 (2008)CrossRefGoogle Scholar
  2. 2.
    Angus, D.C., Linde-Zwirble, W.T., Lidicker, J., Clermont, G., Carcillo, J., Pinsky, M.R.: Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit. Care Med. 29(7), 1303–1310 (2001)CrossRefGoogle Scholar
  3. 3.
    Brun-Buisson, C., Doyon, F., Carlet, J., et al.: Incidence, risk factors, and outcome of severe sepsis and septic shock in adults. A multicenter prospective study in intensive care units. French ICU Group for Severe Sepsis. JAMA 274(12), 968–974 (1995)CrossRefGoogle Scholar
  4. 4.
    Letarte, J., Longo, C.J., Pelletier, J., Nabonne, B., Fisher, H.: Patient characteristics and costs of severe sepsis and septic shock in Quebec. J. Crit. Care 17(1), 39–49 (2002)CrossRefGoogle Scholar
  5. 5.
    Alberti, C., Brun-Buisson, C., Burchardi, H., et al.: Epidemiology of sepsis and infection in ICU patients from an international multicentre cohort study. Intensive Care Med. 28(2), 108–121 (2002)CrossRefGoogle Scholar
  6. 6.
    American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Crit. Care Med. 20(6), 864–74 (1992)Google Scholar
  7. 7.
    Levy, M.M., Fink, M.P., Marshall, J.C., et al.: SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Crit. Care Med. 31(4), 1250–1256 (2001)CrossRefGoogle Scholar
  8. 8.
    Martin, C., Priestap, F., Fisher, H., et al.: A prospective, observational registry of patients with severe sepsis: The Canadian Sepsis Treatment And Response (STAR) Registry. Crit. Care Med. (2009) (in press)Google Scholar
  9. 9.
    Rivers, E., Nguyen, B., Havstad, S., et al.: Early goal-directed therapy in the treatment of severe sepsis and septic shock. N. Engl. J. Med. 345(19), 1368–1377 (2001)CrossRefGoogle Scholar
  10. 10.
    Minneci, P.C., Deans, K.J., Banks, S.M., Eichacker, P.Q., Natanson, C.: Meta-analysis: the effect of steroids on survival and shock during sepsis depends on the dose. Ann. Intern. Med. 141(1), 47–56 (2004)CrossRefGoogle Scholar
  11. 11.
    Gwadry-Sridhar, F., Lewden, B., Mequanint, S., Bauer, M.: Multi-Analytical Approaches Informing the Risk of Sepsis. In: Biomedical Engineering Systems and Technologies. CCIS, pp. 394–406. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Critical Care Research Network: About CCR-Net (2005), http://www.criticalcareresearch.net/, http://www.criticalcareresearch.net/
  13. 13.
    Keenan, S.P., Martin, C.M., Kossuth, J.D., Eberhard, J., Sibbald, W.J.: The Critical Care Research Network: a partnership in community-based research and research transfer. J. Eval. Clin. Pract. 6(1), 15–22 (2000)CrossRefGoogle Scholar
  14. 14.
    Knaus, W.A., Draper, E.A., Wagner, D.P., Zimmerman, J.E.: APACHE II: a severity of disease classification system. Crit. Care Med. 13(10), 818–829 (1985)CrossRefGoogle Scholar
  15. 15.
    Knaus, W.A., Wagner, D.P., Draper, E.A., et al.: The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100(6), 1619–1636 (1991)CrossRefGoogle Scholar
  16. 16.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar
  17. 17.
    Hartigan, J.A., Wong, M.A.: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics) 28(1), 100–108 (1979)MATHGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2011

Authors and Affiliations

  • Femida Gwadry-Sridhar
    • 1
  • Ali Hamou
    • 1
  • Benoit Lewden
    • 1
  • Claudio Martin
    • 2
  • Michael Bauer
    • 3
  1. 1.I-THINK Research LabLawson Health Research InstituteLondonCanada
  2. 2.Dept of Medicine and PhysiologyUniversity of Western OntarioLondonCanada
  3. 3.Dept of Computer ScienceUniversity of Western OntarioLondonCanada

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