Defining Phenotypes of Alcohol-Associated Liver Disease with Acute Hepatic Decompensation

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Project Summary Alcohol consumption and mortality due to alcohol-associated liver disease (ALD) are increasing in the United States, and ALD is now the leading cause of liver transplantation. The natural history of ALD is distinct from other etiologies of liver disease, with more advanced disease at the time of presentation but also opportunity for hepatic recovery. Accurate prediction of outcome remains challenging. The proposed research will establish a prospective registry and biorepository that will evaluate the outcomes of patients with ALD and alcohol use disorder who are hospitalized with acute hepatic decompensation. This study will take a multifaceted approach, considering the biologic and psychosocial influences on clinical outcomes. Patients will be recruited during hospitalization and followed for 2 years in the outpatient clinics, with longitudinal measures of alcohol use patterns, serum biomarkers, radiographic features, nutrition, frailty/sarcopenia, and non-invasive assessments of fibrosis, inflammation, and steatosis. Specific Aims: (1) Characterize the cohort and optimize retention procedures using frequent communication, collateral contacts, and technology-based resources, (2) Identify distinct trajectories of ALD using latent class trajectory analysis and the predictors of trajectory classification, and (3) Develop a patient-specific risk prediction model to predict hepatic recovery after an acute hepatic decompensation. These project aims will identify distinct phenotypes and predictors of outcome in ALD and improve prognostication to better target interventions and allocate resources. The principal investigator (PI) is a hepatologist and clinical researcher at Stanford University, with a long-term vision of improving care for patients with ALD. Her experience with outcomes research and her Master’s in Clinical Research and Epidemiology have prepared her well to execute the project aims. The proposed research and career development plan are well-supported by a multi-disciplinary mentorship team and the institution. The PI will acquire advanced skills in longitudinal data analysis and machine learning, as well as content expertise in alcohol research, which will allow her to apply advanced statistical techniques to optimize prediction of outcome in ALD in a clinically relevant context. In addition, she will have a well-characterized ALD registry and biorepository to serve as a platform for future translational and multicenter studies. This award will provide the PI with the protected time, mentorship, training, and research experience to develop an independent research career in ALD and improve our understanding of this serious and prevalent condition.