The Mayo Clinic study is important for patients who have a high likelihood of death in a hospital. Early, accurate identification of these patients when they arrive at the hospital might limit potentially futile, aggressive ICU care to such patients. However, limiting such aggressive care based on inaccurate tools might shorten potentially productive lives, says Keith Berge, M.D., a Mayo Clinic anaesthesiologist who was the lead researcher.
In this study, researchers used the Acute Physiology and Chronic Health Evaluation III (APACHE III), a computerised system that is designed to prospectively predict mortality rates in "real time” for ICU patients, to identify a group of patients with a very low predicted likelihood of survival.
They found that patients whose families had unrealistic expectations used substantially more ICU resources. While these patients survived to hospital discharge at a higher rate than others, their survival one year later did not differ much from those without documentation of unrealistic expectations.
Apart from that, the researchers noted that APACHE III, a highly sophisticated and widely used computer tool for predicting outcomes, was more than five times more pessimistic than what they observed in their extremely ill subset of patients. The researchers also noted that physician insights of the likelihood of survival of some groups of ICU patients were considerably more accurate than the outcomes predicted using the computer tool.
In the study, the researchers noted the value of physician insights and interpretations in making decisions on care, saying "This finding also sounds a cautionary note to those who would use prognostication tools to limit resource allocation to extremely ill patients based solely on a low predicted likelihood of survival.”
MEDICA.de; Source: Mayo Clinic