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Approaching Death Improving Care at the End of Life (1997) / Chapter Skim
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Appendix D
Pages 358-362

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From page 358...
... directors, and quality assurance managers. Early classification systems, such as the Killip Class for acute myocardial infarction, the Glasgow Coma Score, and other disease-specific scoring systems, were clinically based, simple, and widely embraced.
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From page 359...
... The models invariably perform best in the midrange of probabilities and are most useful in that range when used on aggregated patients. Models lack statistical power among very high-risk patients because of the low number of cases in the very high-risk strata.
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From page 360...
... Models lack generalizability if they are "overfitted" to the particular population and hence disproportionately reflect outliers or idiosyncratic values in that data set. Models may also fail to distinguish between data elements that represent process of care versus those that more truly represent the patient's condition.
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From page 361...
... I believe, however, that we are far from achieving the goal of using individual patient level predictors to make difficult and painful decisions regarding which critically ill patients may no longer benefit from intensive care. These clinical predictors are one more piece of information, like any other diagnoetic test, to be used in the context of the full clinical picture informed by patient and family preferences.
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From page 362...
... A clinical assessment of Medisgroups. Journal of tI7e American Medical Association 260(:31)
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Key Terms

  • care units
  • individual patient
  • predictive models
  • critically ill
  • icu patients

  • pediatric intensive
  • apache iii
  • patient decisions
  • prediction model
  • percent accurate

  • models lack
  • hospital mortality
  • ill patients
  • care medicine
  • mortality prediction

  • care unit
  • critical care
  • disease process
  • eight pediatric
  • individual decision

  • appropriate cutoff
  • chronic disease
  • paramount importance
  • accurate prediction
  • care patients

  • veterans affairs
  • level predictors
  • prediction models
  • clinical decisionmaking
  • prognostic risk


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