World Journal of Surgery

, Volume 20, Issue 4, pp 401–405 | Cite as

Severity Stratification and Outcome Prediction for Multisystem Organ Failure and Dysfunction

  • Jack E. Zimmerman
  • William A. Knaus
  • Xiaolu Sun
  • Douglas P. Wagner

Abstract. Multiple organ system failure or dysfunction (MOSF/MODS) remains a major cause of morbidity and mortality in hospitalized adults. Among intensive care unit (ICU) patients the extent of physiologic derangement, the type of associated disease or injury, increasing age, and life-threatening comorbid conditions are the major determinants of risk for developing MOSF and for survival during the 1980s. Hospital mortality for patients with a single organ system failure (OSF) lasting more than 1 day approached 40%; and for those with two OSFs hospital mortality increased to 60%. These outcomes did not change over the decade. For patients with three or more OSFs persisting after 3 days of OSF, however, data suggest that between 1982 and 1990 the mortality has been reduced from 98% to 84% (

p = 0.0003). Because of variations in the types and combinations of OSFs, associated disease, and extent of physiologic derangement, it is difficult to interpret variations in mortality among patients with one or more OSFs defined using categorical criteria. For this and other reasons, outcome prediction based on a comprehensive assessment of patient risk factors is a more sensitive, specific, useful approach to quantifying MODS than a simple count of the number and duration of OSFs. Because repeated assessment of risk factors during subsequent ICU days reflects complications and response to therapy, daily outcome predictions are even more precise than estimates at ICU admission. The ability to more accurately predict survival from MODS/MOSF can improve our ability to test new therapies, evaluate how outcome has changed over time, and assess the efficacy of supportive therapy for individuals.

Keywords

Intensive Care Unit Organ Failure Hospital Mortality Intensive Care Unit Admission Outcome Prediction 
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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Copyright information

© Société Internationale de Chirugie 1996

Authors and Affiliations

  • Jack E. Zimmerman
    • 1
  • William A. Knaus
    • 1
  • Xiaolu Sun
    • 1
  • Douglas P. Wagner
    • 1
  1. 1.ICU Research Unit, Department of Anesthesiology, The George Washington University Medical Center, 2300 K Street N.W., Washington, D.C. 20037, U.S.A.US

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