In intensive care units, a predictive model that identified patients who are certain to die would spare suffering and free resources for more productive work. In a prospective study to determine factors which might predict the outcome of a protracted stay in intensive care units, information was collected for 162 patients who remained in intensive care longer than 48 hours after cardiac surgery. Of these patients, 21% presented as emergencies, 35% as urgent cases, and 44% as elective cases. They were drawn from 2256 adult patients operated upon during a 12-month period in three UK centres. 115 patients (71%) who were in intensive care for more than 48 hours survived to be discharged. The median duration of stay was 6 days (range 3-90 days) and the median duration of hospital stay was 21 days (7-111 days). An existing algorithm developed and calibrated to predict outcome for general patients in intensive care was applied to forecast outcomes. Contrary to expectations, the algorithm performed well for patients after cardiac surgery. In identifying deaths in intensive care and before hospital discharge, the specificities for death at various intervals after admission were all 97% or more. There is little scope for improving the algorithm's ability to forecast longer term outcome. Furthermore, if it were to be introduced to aid decisions about withdrawal of treatment, the potential saving in intensive care bed-days would be small--less than 3% overall.