| Literature DB >> 27583863 |
Jin Young Lee1, Seung Hwan Lee, Myung Jae Jung, Jae Gil Lee.
Abstract
Few studies have evaluated the risk factors for in-hospital mortality in critically ill surgical patients who have undergone emergency gastrointestinal (GI) surgery. The aim of this study was to identify the risk factors associated with in-hospital mortality in critically ill surgical patients after emergency GI surgery.The medical records of 362 critically ill surgical patients who underwent emergency GI surgery, admitted to intensive care unit between January 2007 and December 2011, were reviewed retrospectively. Perioperative biochemical and clinical parameters of survivors and nonsurvivors were compared. Logistic regression multivariate analysis was performed to identify the independent risk factors of mortality.The in-hospital mortality rate was 15.2% (55 patients). Multivariate analyses revealed cancer-related perforation (odds ratio [OR] 16.671, 95% confidence interval [CI] 2.629-105.721, P = 0.003), preoperative anemia (hemoglobin <10 g/dL; OR 6.976, 95% CI 1.376-35.360, P = 0.019), and preoperative hypoalbuminemia (albumin <2.7 g/dL; OR 9.954, 95% CI 1.603-61.811, P = 0.014) were independent risk factors of in-hospital mortality after emergency GI surgery.The findings of this study suggest that in critically ill patients undergoing emergency GI surgery, cancer-related peritonitis, preoperative anemia, and preoperative hypoalbuminemia are associated with in-hospital mortality. Recognizing risk factors at an early stage could aid risk stratification and the provision of optimal perioperative care.Entities:
Mesh:
Year: 2016 PMID: 27583863 PMCID: PMC5008547 DOI: 10.1097/MD.0000000000004530
Source DB: PubMed Journal: Medicine (Baltimore) ISSN: 0025-7974 Impact factor: 1.889
Baseline characteristics of patients.
Baseline characteristics of patients.
Preoperative parameters by univariate analysis.
Intraoperative parameters by univariate analysis.
Postoperative parameters by univariate analysis.
Univariate and multivariate logistic regression model for in-hospital mortality.