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: Performance of prognostic models in critically ill cancer patients – a review. | Available online http content 9 4 R458 Research Performance of prognostic models in critically ill cancer patients -a review Sylvia den Boer1 Nicolette F de Keizer2 and Evert de Jonge1 Intensivist Department of Intensive Care Academic Medical Center Universiteit van Amsterdam Amsterdam Netherlands 2Informatician Department of Medical Informatics Academic Medical Center Universiteit van Amsterdam Amsterdam Netherlands Corresponding author Evert de Jonge Received 27 Apr 2005 Revisions requested 26 May 2005 Revisions received 2 Jun 2005 Accepted 16 Jun 2005 Published 8 Jul 2005 Critical Care 2005 9 R458-R463 DOI 86 cc3765 This article is online at http content 9 4 R458 2005 den Boer et al. licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Open Access Abstract Introduction Prognostic models such as the Acute Physiology and Chronic Health Evaluation APACHE II or III the Simplified Acute Physiology Score SAPS II and the Mortality Probability Models MPM II were developed to quantify the severity of illness and the likelihood of hospital survival for a general intensive care unit ICU population. Little is known about the performance of these models in specific populations such as patients with cancer. Recently specific prognostic models have been developed to predict mortality for cancer patients who are admitted to the ICU. The present analysis reviews the performance of general prognostic models and specific models for cancer patients to predict in-hospital mortality after ICU admission. Methods Studies were identified by searching the Medline databases from 1994 to 2004. We included studies evaluating the performance of mortality prediction models in critically ill cancer .
