Research questions:
Combining demographic datas like BMI and age, metabolic indicators, including LDL-C, HDL, total cholesterol and triacylglycerols, liver function indicators, including AST, ALT, ALP, GGT, TBil, PT, PLT, ALB, AFP and LSM, can we prognosis the outcomes of patients with fatty liver and chronic liver disease?
Objectives:
Adult patients (age > 18 years) with fatty liver disease and chronic liver disease.
Scientific rationale for the research:
Patients with fatty liver and chronic liver diseases is likely to get advanced liver fibrosis and subsequent severe complications, which significantly reduces their quality of life and imposes a heavy economic burden. With the increasing number of cirrhosis and severe complications cases each year, early detection, effective treatment, and prevention of disease progression have become major challenges in modern medicine. Therefore, research on prognosis assessment in cirrhosis holds substantial theoretical and practical importance. Nowadays, there are some studies focusing on the association of serum biomarkers, imaging indicators and dietary indicators with diseases. Therefore, combining metabolic indicators, such as LDL-C, traditional and novel liver function indicators, we would like to construct a new machine-learning based model to predict the outcome of patients with fatty liver and chronic liver diseases.
