Spann, Ashley

Racial, ethnic, and socioeconomic disparities exist in the prevalence and natural history of chronic liver disease, access to care, and clinical outcomes. Solutions to improve health equity range widely, from digital health tools to policy changes. The current review outlines the disparities along the chronic liver disease health care continuum from screening and diagnosis to the management of cirrhosis and considerations of pre-liver and post-liver transplantation. Using a health equity research and implementation science framework, we offer pragmatic strategies to address barriers to implementing high-quality equitable care for patients with chronic liver disease.

Cardiovascular disease (CVD) is a leading complication after liver transplantation and has a significant impact on patients’ outcomes posttransplant. The major risk factors for post–liver transplant CVD are age, preexisting CVD, nonalcoholic fatty liver disease, chronic kidney disease, and metabolic syndrome. This review explores the contemporary strategies and approaches to minimizing cardiometabolic disease burden in liver transplant recipients. We highlight areas for potential intervention to reduce the mortality of patients with metabolic syndrome and CVD after liver transplantation.

Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.