A simplified prognostic model to predict mortality in patients with acute variceal bleeding. Issue 3 (March 2018)

• Record Type: Journal Article
• Authors: Lee, Han Hee; Park, Jae Myung; Han, Seunghoon; Park, Sung Min; Kim, Hee Yeon; Oh, Jung Hwan; Kim, Chang Wook; Yoon, Seung Kew; Choi, Myung-Gyu
• Is Part Of: Digestive and liver disease. Volume 50:Issue 3(2018)
• Page Start: 247
• Page End: 253
• Publication Date: 2018-03
• Subjects: Cirrhosis -- Logistic regression -- Prognostic model -- Survival -- Variceal bleeding; Digestive organs -- Diseases -- Periodicals; Liver -- Diseases -- Periodicals; 616.33005
• DOI: 10.1016/j.dld.2017.11.006
• Languages: English
• ISSNs: 1590-8658

Abstract

Abstract: Background: Acute variceal bleeding (AVB) is a major cause of death in patients with liver cirrhosis. The aim of this study was to investigate mortality predictors and develop a new simple prognostic model using easily verified factors at admission in AVB patients. Methods: Between January 2009 and May 2015, 333 consecutive patients with AVB were included. A simplified prognostic model was developed using multiple logistic regression after identifying significant predictors of 6-week mortality. Mortality prediction accuracy was assessed with area under the receiver operating characteristic (AUROC) curve. We compared the new model to existing models of model for end-stage liver disease (MELD) and Child–Pugh scores. Results: The 6-week overall mortality rate was 12.9%. Multivariate analysis showed that C-reactive protein (CRP), total bilirubin, and the international normalized ratio were independent predictors of mortality. A new logistic model using these variables was developed. This model's AUROC was 0.834, which was significantly higher than that of MELD (0.764) or Child–Pugh scores (0.699). Two external validation studies showed that the AUROC of our model was consistently higher than 0.8. Conclusions: Our new simplified model accurately and consistently predicted 6-week mortality in patients with AVB using objective variables measured at admission. Our system can be used to identify high risk AVB patients.