Comparison of Risks Factors for Unplanned ICU Transfer after ED Admission in Patients with Infections and Those without Infections. (2nd January 2014)

• Record Type: Journal Article
• Authors: Tsai, Jeffrey Che-Hung; Cheng, Ching-Wan; Weng, Shao-Jen; Huang, Chin-Yin; Yen, David Hung-Tsang; Chen, Hsiu-Ling
• Other Names: Baydin A. Academic Editor.; Catena F. Academic Editor.
• Is Part Of: TheScientificWorldjournal. Volume 2014(2014)
• Publication Date: 2014-01-02
• Subjects: Science -- Periodicals; Technology -- Periodicals; Medicine -- Periodicals; 505
• DOI: 10.1155/2014/102929
• Languages: English
• ISSNs: 2356-6140

Abstract

Abstract : Background . The objectives of this study were to compare the risk factors for unplanned intensive care unit (ICU) transfer after emergency department (ED) admission in patients with infections and those without infections and to explore the feasibility of using risk stratification tools for sepsis to derive a prediction system for such unplanned transfer. Methods . The ICU transfer group included 313 patients, while the control group included 736 patients randomly selected from those who were not transferred to the ICU. Candidate variables were analyzed for association with unplanned ICU transfer in the 1049 study patients. Results . Twenty-four variables were associated with unplanned ICU transfer. Sixteen (66.7%) of these variables displayed association in patients with infections and those without infections. These common risk factors included specific comorbidities, physiological responses, organ dysfunctions, and other serious symptoms and signs. Several common risk factors were statistically independent. Conclusions . The risk factors for unplanned ICU transfer in patients with infections were comparable to those in patients without infections. The risk factors for unplanned ICU transfer included variables from multiple dimensions that could be organized according to the PIRO (predisposition, insult/infection, physiological response, and organ dysfunction) model, providing the basis for the development of a predictive system.