Elsevier

Critical Care Clinics

Volume 20, Issue 3, July 2004, Pages 345-362
Critical Care Clinics

Value and role of intensive care unit outcome prediction models in end-of-life decision making

https://doi.org/10.1016/j.ccc.2004.03.002Get rights and content

Section snippets

History and context

Evaluation of hospital treatment outcomes is said to have begun in the late nineteenth century with Florence Nightingale's 1863 publication of Notes on Hospitals, in which she identified broad variation in British hospital mortality rates. In the early twentieth century, Ernest Codman challenged his surgical colleagues at the Massachusetts General Hospital to evaluate the effects of specific interventions on patients' outcomes, which he labeled the “end results idea.” Both pioneers acknowledged

Current role

The most common explicit use for mortality prediction models in the United States health care system is for the calculation of “risk-adjusted” mortality rates for groups of patients treated in hospitals (eg, patients admitted after an acute myocardial infarction), organizational units (eg, patients admitted to an ICU), or by individual providers (eg, patients undergoing bypass surgery by Dr. Smith). Ostensibly, this allows for the identification of quality outliers whose observed mortality rate

Quality improvement

Efforts to improve upon the statistical performance of mortality prediction models — as distinct from risk-adjustment models for hospital and ICU quality profiling—would be welcome and cannot go unmentioned. Furthermore, efforts to develop models that incorporate data on long-term outcomes, such as functional independence and cognition, would provide patients and their proxies with the information that may most significantly affect their preferences for intensive medical care. However, the most

Acknowledgements

We would like to thank Mark S. Roberts, MD, MPP, for help with the figures used in this chapter.

First page preview

First page preview
Click to open first page preview

References (53)

  • M.M Pollack et al.

    Pediatric risk of mortality (PRISM) score

    Crit Care Med

    (1988)
  • S Lemeshow et al.

    A method for predicting survival and mortality of ICU patients using objectively derived weights

    Crit Care Med

    (1985)
  • S Lemeshow et al.

    Refining intensive care unit outcome prediction by using changing probabilities of mortality

    Crit Care Med

    (1988)
  • S Lemeshow et al.

    Predicting the outcome of intensive care unit patients

    J Am Stat Assoc

    (1988)
  • S Lemeshow et al.

    Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients

    JAMA

    (1993)
  • W.A Knaus et al.

    APACHE II: a severity of disease classification system

    Crit Care Med

    (1985)
  • P.M Steen et al.

    Predicted probabilities of hospital death as a measure of admission severity of illnes

    Inquiry

    (1993)
  • R.W Chang et al.

    Predicting deaths among intensive care unit patients

    Crit Care Med

    (1988)
  • R.W Chang et al.

    Predicting outcome among intensive care unit patients using computerised trend analysis of daily Apache II scores corrected for organ system failure

    Intensive Care Med

    (1988)
  • D.P Wagner et al.

    Daily prognostic estimates for critically ill adults in intensive care units: results from a prospective, multicenter, inception cohort analysis

    Crit Care Med

    (1994)
  • S Jacobs et al.

    The Riyadh Intensive Care Program applied to a mortality analysis of a teaching hospital intensive care unit

    Anaesthesia

    (1992)
  • A.T Hope et al.

    The Riyadh Intensive Care Program mortality prediction algorithm assessed in 617 intensive care patients in Glasgow

    Anaesthesia

    (1995)
  • D.K McClish et al.

    How well can physicians estimate mortality in a medical intensive care unit

    Med Decis Making

    (1989)
  • S.M Shortell et al.

    The performance of intensive care units: does good management make a difference

    Med Care

    (1994)
  • J.M Teno et al.

    Prognosis-based futility guidelines: does anyone win? SUPPORT Investigators. Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment

    J Am Geriatr Soc

    (1994)
  • W.A Knaus et al.

    Do objective estimates of chances for survival influence decisions to withhold or withdraw treatment? The French Multicentric Group of ICU Research

    Med Decis Making

    (1990)
  • Cited by (0)

    Funding by the Robert Wood Johnson Foundation; National Institute on Aging, Grant # 1 K08 AG021921-01.

    View full text