title: Predicting mortality in intensive care unit survivors using a subjective scoring system.
abstract: Most prognostic models rely on variables recorded within 24 hours of admission to predict the mortality rate of patients in the intensive care unit (ICU). Although a significant number of patients die after discharge from the ICU, there is a paucity of data related to predicting hospital mortality based on information obtained at ICU discharge. It is likely that experienced intensivists may be able to predict the likelihood of hospital death at ICU discharge accurately if they incorporate patients' age, preferences regarding life support, comorbidities, prehospital quality of life, and clinical course in the ICU into their prediction. However, if it is to be generalizable and reproducible and to perform well without bias, then a good prediction model should be based on objectively defined variables.
authors.affiliation: Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA. afessa.bekele@mayo.edu
authors.forename: Bekele
authors.initials: B
authors.lastname: Afessa
authors.forename: Mark T
authors.initials: MT
authors.lastname: Keegan
mesh_terms: D018806:APACHE; D016638:Critical Illness / Q000401:mortality; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D010351:Patient Discharge; D011379:Prognosis; D017741:Survivors