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.
Highlights
The intensive care unit (ICU) prediction models have the potential to help decision makers, physicians, and patients to select treatment options and allocate resources
Prognostic models are used to predict the outcome of patients admitted to the intensive care unit (ICU)
Most of the ICU prognostic models are based on variables recorded within 24 hours of ICU admission, and there is a paucity of data describing the role of these models in predicting the outcome of patients who survive their initial ICU stay
Summary
The ICU prediction models have the potential to help decision makers, physicians, and patients to select treatment options and allocate resources. Prognostic models are used to predict the outcome of patients admitted to the intensive care unit (ICU). Most of the ICU prognostic models are based on variables recorded within 24 hours of ICU admission, and there is a paucity of data describing the role of these models in predicting the outcome of patients who survive their initial ICU stay.
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