In 1999, one out of every five Americans died in the hospital using intensive care,
and terminal admissions associated with intensive care unit (ICU) admission consumed
80% of all terminal hospitalization costs [
[1]
]. Most of these ICU decedents are over age 65. Indeed, the proportion of fee-for-service
Medicare beneficiaries with one or more ICU admissions in the last year of life increased
from 30.5% in 1985 to 35% in 1999 [
[2]
]. High levels of ICU use at the end of life attract policy attention because of the
concern that this care is futile and, hence, wasteful. Yet, these high levels of use
are identified retrospectively. The challenge is to predict prospectively which patients will not survive despite ICU care, and for whom averting or interrupting
an ICU stay is an acceptable goal. Many mortality prediction models currently exist;
how are we to measure the value of these models for informing the utility of ICU care? First, we would expect the prediction tool to be highly reliable. Second,
it would have to be available in a timely fashion to inform the decision both to initiate
and to continue intensive care. Third, it would have to offer predictions about the
outcomes in which patients are most interested. Fourth, it would have to influence
clinician behavior. Finally, it would have to help patients and families receive care
that is consistent with their preferences.To read this article in full you will need to make a payment
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References
- The use of intensive care at the end of life in the United States: an epidemiologic study.Crit Care Med. 2004; 32: 638-643
- Trends in inpatient treatment intensity among Medicare beneficiaries at the end of life.Health Serv Res. 2004; 39: 359-372
- APACHE-acute physiology and chronic health evaluation: a physiologically based classification system.Crit Care Med. 1981; 9: 591-597
- Accurate prediction of the outcome of pediatric intensive care. A new quantitative method.N Engl J Med. 1987; 316: 134-139
- Pediatric risk of mortality (PRISM) score.Crit Care Med. 1988; 16: 1110-1116
- A method for predicting survival and mortality of ICU patients using objectively derived weights.Crit Care Med. 1985; 13: 519-525
- Refining intensive care unit outcome prediction by using changing probabilities of mortality.Crit Care Med. 1988; 16: 470-477
- Predicting the outcome of intensive care unit patients.J Am Stat Assoc. 1988; 83: 348-356
- Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients.JAMA. 1993; 270: 2478-2486
- APACHE II: a severity of disease classification system.Crit Care Med. 1985; 13: 818-829
- The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults.Chest. 1991; 100: 1619-1636
- Statistical methods for quantifying the severity of clinical acute pancreatitis.J Surg Res. 1977; 22: 79-91
- Predicted probabilities of hospital death as a measure of admission severity of illnes.Inquiry. 1993; 30: 128-141
- Predicting deaths among intensive care unit patients.Crit Care Med. 1988; 16: 34-42
- 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; 14: 558-566
- Individual outcome prediction models for intensive care units.Lancet. 1989; 2: 143-146
- Daily prognostic estimates for critically ill adults in intensive care units: results from a prospective, multicenter, inception cohort analysis.Crit Care Med. 1994; 22: 1359-1372
- Identification of futility in intensive care.Lancet. 1994; 344: 1203-1206
- The Riyadh Intensive Care Program applied to a mortality analysis of a teaching hospital intensive care unit.Anaesthesia. 1992; 47: 775-780
- The Riyadh Intensive Care Program mortality prediction algorithm assessed in 617 intensive care patients in Glasgow.Anaesthesia. 1995; 50: 103-107
- How well can physicians estimate mortality in a medical intensive care unit.Med Decis Making. 1989; 9: 125-132
- The performance of intensive care units: does good management make a difference.Med Care. 1994; 32: 508-525
- Use of APACHE II severity of disease classification to identify intensive-care-unit patients who would not benefit from total parenteral nutrition.Lancet. 1986; 1: 1483-1487
- 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; 42: 1202-1207
- 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; 10: 163-171
- Does prediction of outcome alter patient management?.Lancet. 1993; 341: 1487-1491
- A controlled trial to improve care for seriously ill hospitalized patients: the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT) [erratum appears in JAMA 1996 Apr 24;275(16):1232].JAMA. 1995; 274: 1591-1598
- Increasing incidence of withholding and withdrawal of life support from the critically ill.Am J Respir Crit Care Med. 1997; 155: 15-20
- A national survey of end-of-life care for critically ill patients.Am J Respir Crit Care Med. 1998; 158: 1163-1167
- Determinants in Canadian health care workers of the decision to withdraw life support from the critically ill.JAMA. 1995; 273: 703-708
- The big secret in health care: rationing is here.Wall Street Journal. 2003; Sep 12: A1-A6
- Factors associated with do-not-resuscitate orders: patients' preferences, prognoses, and physicians' judgments. SUPPORT Investigators. Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment.Ann Intern Med. 1996; 125: 284-293
- Rationing intensive care–physician responses to a resource shortage.N Engl J Med. 1983; 309: 1155-1160
- Accuracy of decisions to withdraw therapy in critically ill patients: clinical judgment versus a computer model.Crit Care Med. 1989; 17: 1091-1097
- The SUPPORT prognostic model. Objective estimates of survival for seriously ill hospitalized adults. Study to understand prognoses and preferences for outcomes and risks of treatments.Ann Intern Med. 1995; 122: 191-203
- Comparison of clinical assessment with APACHE II for predicting mortality risk in patients admitted to a medical intensive care unit.JAMA. 1988; 260: 1739-1742
- The answer to “What are my chances, doctor?” depends on whom is asked: prognostic disagreement and inaccuracy for critically ill patients.Crit Care Med. 1989; 17: 827-833
- Are two (inexperienced) heads better than one (experienced) head? Averaging house officers' prognostic judgments for critically ill patients.Arch Intern Med. 1990; 150: 1874-1878
- What difference do two days make? The inertia of physicians' sequential prognostic judgments for critically ill patients.Med Decis Making. 1990; 10: 6-14
- Ego bias, reverse ego bias, and physicians' prognostic.Crit Care Med. 1991; 19: 1533-1539
- Physicians' confidence in their estimates of the probability of survival: relationship to accuracy.Med Decis Making. 1992; 12: 336
- Doing all they can: physicians who deny medical futility.J Law Med Ethics. 1994; 22: 318-326
- Six-month neuropsychological outcome of medical intensive care unit patients.Crit Care Med. 2003; 31: 1226-1234
- Surviving intensive care: a report from the 2002 Brussels Roundtable.Intensive Care Med. 2003; 29: 368-377
- Surviving intensive care.Crit Care Med. 2002; 30: 703-705
- Understanding the treatment preferences of seriously ill patients.N Engl J Med. 2002; 346: 1061-1066
- Judgment under uncertainty: heuristics and biases. Cambridge University Press, Cambridge (UK)1982: 555
- Kahneman D Tversky A Choices, values, and frames. Cambridge University Press, Russell Sage Foundation, New York2000: 840
- Obstacles to acceptance of clinical decision analysis.BMJ. 1989; 298: 579-582
- Withdrawal of mechanical ventilation in anticipation of death in the intensive care unit.N Engl J Med. 2003; 349: 1123-1132
- Appropriateness of care. A comparison of global and outcome methods to set standards.Med Care. 1992; 30: 565-586
- Death foretold: prophecy and prognosis in medical care. The University of Chicago Press, Chicago (IL)1999: 328
- Treatment decisions for terminally ill patients: physicians' legal defensiveness and knowledge of medical law.Law Med Health Care. 1992; 20: 364-376
Article info
Footnotes
☆Funding by the Robert Wood Johnson Foundation; National Institute on Aging, Grant # 1 K08 AG021921-01.
Identification
Copyright
© 2004 Elsevier Inc. Published by Elsevier Inc. All rights reserved.
