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: Evaluation of SOFA-based models for predicting mortality in the ICU: A systematic review. | Available online http content 1 2 6 R161 Open Access Research Evaluation of SOFA-based models for predicting mortality in the ICU A systematic review Lilian Minne1 Ameen Abu-Hanna1 and Evert de Jonge2 Department of Medical Informatics Academic Medical Center Meibergdreef 9 1105 AZ Amsterdam The Netherlands intensive Care Department Academic Medical Center Meibergdreef 9 1105 AZ Amsterdam The Netherlands Corresponding author Ameen Abu-Hanna Received 29 Oct 2008 Revisions requested 27 Nov 2008 Revisions received 12 Dec 2008 Accepted 17 Dec 2008 Published 17 Dec 2008 Critical Care 2008 12 R161 doi cc7160 This article is online at http content 12 6 R1 61 2008 Minne 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. Abstract Introduction To systematically review studies evaluating the performance of Sequential Organ Failure Assessment SOFA -based models for predicting mortality in patients in the intensive care unit ICU . Methods Medline EMBASE and other databases were searched for English-language articles with the major objective of evaluating the prognostic performance of SOFA-based models in predicting mortality in surgical and or medical ICU admissions. The quality of each study was assessed based on a quality framework for prognostic models. Results Eighteen articles met all inclusion criteria. The studies differed widely in the SOFA derivatives used and in their methods of evaluation. Ten studies reported about developing a probabilistic prognostic model only five of which used an independent validation data set. The other studies used the SOFA-based score directly to discriminate between survivors and non-survivors without fitting a probabilistic model. In five .
