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: Use of dynamic microsimulation to predict disease progression in patients with pneumonia-related sepsis. | Available online http content 11 3 R65 Research Use of dynamic microsimulation to predict disease progression in patients with pneumonia-related sepsis Gorkem Saka1 Jennifer E Kreke1 Andrew J Schaefer1 2 Chung-Chou H Chang2 Mark S Roberts1 2 Derek C Angus3 for the GenIMS Investigators Department of Industrial Engineering University of Pittsburgh 3700 OHara St. 3700 Benedum Hall Pittsburgh PA 15261 USA 2Section of Decision Sciences and Clinical Systems Modeling Department of Medicine Division of General Internal Medicine University of Pittsburgh 200 Meyran Ave. Suite 200 Pittsburgh PA 15213 USA 3The Clinical Research Investigation and Systems Modeling of Acute Illness CRISMA Laboratory Department of Critical Care Medicine University of Pittsburgh 3550 Terrace St. 600 Scaife Hall Pittsburgh PA 15261 USA Corresponding author Mark S Roberts robertsm@ Received 18 Dec 2006 Revisions requested 29 Jan 2007 Revisions received 20 Apr 2007 Accepted 14 Jun 2007 Published 14 Jun 2007 Critical Care 2007 11 R65 doi cc5942 This article is online at http content 11 3 R65 2007 Saka 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. For a list of the GenIMS Investigators see Additional file 1 Open Access Abstract Introduction Sepsis is the leading cause of death in critically ill patients and often affects individuals with community-acquired pneumonia. To overcome the limitations of earlier mathematical models used to describe sepsis and predict outcomes we designed an empirically based Monte Carlo model that simulates the progression of sepsis in hospitalized patients over a 30-day period. Methods The model simulates changing health over time as represented by the Sepsis-related Organ Failure .
