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Báo cáo khoa học: " Predicting mortality in intensive care unit survivors using a subjective scoring system"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Predicting mortality in intensive care unit survivors using a subjective scoring system. | Available online http ccforum.eom content 11 1 109 Commentary Predicting mortality in intensive care unit survivors using a subjective scoring system Bekele Afessa1 and Mark T Keegan2 1 Division of Pulmonary and Critical Care Medicine Department of Internal Medicine Mayo Clinic College of Medicine 200 First St. SW Rochester Minnesota 55905 USA 2Critical Care Department of Anesthesia Mayo Clinic College of Medicine 200 First St. SW Rochester Minnesota USA Corresponding author Bekele Afessa afessa.bekele@mayo.edu Published 15 February 2007 This article is online at http ccforum.com content 11 1 109 2007 BioMed Central Ltd Critical Care 2007 11 109 doi 10.1186 cc5683 See related research by Fernandez et al. http ccforum.com content 10 6 R179 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. Prognostic models are used to predict the outcome of patients admitted to the intensive care unit ICU . Age comorbidities physiologic abnormalities acute diagnoses and lead-time bias are among the predictor variables entered into these models. These variables are usually selected and scored subjectively by expert consensus or objectively using statistical methods. Some of the ICU prognostic models require cumbersome data collection and