Determining Prognosis of ALD and Alcohol-associated Hepatitis

Alcohol-associated hepatitis has a poor prognosis in terms of short-term mortality and often presents with symptoms, such as jaundice, acute renal failure, and ascites. There are many prognostic models that have been developed to predict short-term and long-term mortality in these patients. Current prognostic models can be divided into static scores, which are measured at admission, and dynamic models, which measure baseline and after a certain amount of time. The efficacy of these models in predicting short-term mortality is disputed. Numerous studies across the world have compared prognostic models, such as the Maddrey's discriminant function, the model for end-stage liver disease score, model for end-stage liver disease score-Na, Glasgow alcohol-associated hepatitis score, and the age-bilirubin-international normalized ratio-creatinine (ABIC) score, to each other to determine which score is more useful for a particular context. There are also prognostic markers such as liver biopsy, breath biomarkers, and acute kidney injury that are able to predict mortality. The accuracy of these scores is a key to determining when treatment with corticosteroids is futile since there is an increased risk of infection in those treated with it. Furthermore, although these scores are helpful in predicting short-term mortality, the only factor that is able to predict long-term mortality in patients with alcohol-related liver disease is abstinence. Numerous studies have proven that even though corticosteroids provide a treatment for alcohol-associated hepatitis, it is a temporary one, at best. The purpose of this paper is to compare the historical models to current ones in their ability to predict mortality in patients with alcohol-related liver disease by analyzing multiple studies that have examined these prognostic markers. This paper also isolates the knowledge gaps in the ability to delineate which patients would benefit from corticosteroids and patients who would not and provides potential models for the future that could narrow this gap.