Severe forms of alcoholic hepatitis in patients with alcoholic liver disease are associated with high mortality; it is therefore vital to identify those patients at greatest risk of mortality in 28 days as they may benefit from aggressive intervention. The aim of this study is to propose a new predictive model that can be used in clinical practice to identify such patients and to monitor their progress while in hospital. A cohort of 82 patients was selected and for each of them, a number of clinical findings and standard laboratory tests at the time of admission to hospital were recorded. Also, some variables were collected up to 7 days after admission. The proposed logistic regression model selected four statistically significant predictors (namely, the level of creatinine on and after admission, the presence of encephalopathy and prothrombin time evaluated after admission). A comparison with the available mortality predictive scores showed an increase by 25% in predictive power, demonstrating increased accuracy in identifying these sick patients with alcoholic hepatitis in clinical practice.
Keywords: Alcoholic hepatitis; Alcoholic liver disease; Clinical decision support system; Logistic regression; Mortality model.
© 2013 Published by Elsevier Ltd.