• R B Becker and J E Zimmerman.
• Department of Anesthesiology, George Washington University Medical Center, Washington, DC, USA.
• Crit Care Clin. 1996 Jul 1; 12 (3): 503-14.

AbstractToo much time and effort are wasted in attempts to pass final judgment on whether systems for ICU prognostication are "good or bad" and whether they "do or do not" provide a simple answer to the complex and often unpredictable question of individual mortality in the ICU. A substantial amount of data supports the usefulness of general ICU prognostic systems in comparing ICU performance with respect to a wide variety of endpoints, including ICU and hospital mortality, duration of stay, and efficiency of resource use. Work in progress is analyzing both general resource use and specific therapeutic interventions. It also is time to fully acknowledge that statistics never can predict whether a patient will die with 100% accuracy. There always will be exceptions to the rule, and physicians frequently will have information that is not included in prognostic models. In addition, the values of both physicians and patients frequently lead to differences in how a probability in interpreted; for some, a 95% probability estimate means that death is near and, for others, this estimate represents a tangible 5% chance for survival. This means that physicians must learn how to integrate such estimates into their medical decisions. In doing so, it is our hope that prognostic systems are not viewed as oversimplifying or automating clinical decisions. Rather, such systems provide objective data on which physicians may ground a spectrum of decisions regarding either escalation or withdrawal of therapy in critically ill patients. These systems do not dehumanize our decision-making process but, rather, help eliminate physician reliance on emotional, heuristic, poorly calibrated, or overly pessimistic subjective estimates. No decision regarding patient care can be considered best if the facts upon which it is based on imprecise or biased. Future research will improve the accuracy of individual patient predictions but, even with the highest degree of precision, such predictions are useful only in support of, and not as a substitute for, good clinical judgment.

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