Identification of Fast Progressors Among Patients With Nonalcoholic Steatohepatitis Using Machine Learning.

ABSTRACT:

Background and aims

There is a high unmet need to develop noninvasive tools to identify nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (NAFLD/NASH) patients at risk of fast progression to end-stage liver disease (ESLD). This study describes the development of a machine learning (ML) model using data around the first clinical evidence of NAFLD/NASH to identify patients at risk of future fast progression.

Methods

Adult patients with ESLD (cirrhosis or hepatocellular carcinoma) due to NAFLD/NASH were identified in Optum electronic health records (2007-2018 period). Patients were stratified into fast (0.5 and 3 years) and standard progressor (6-10 years) cohorts based on retrospectively established progression time between ESLD and the earliest observable disease,

SUBMITTER: Schattenberg JM 

PROVIDER: S-EPMC11307632 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature