Identifying Metabolic Dysfunction-Associated Steatotic Liver Disease Using Natural Language Processing in a US National Cohort.

Abstract

Although metabolic dysfunction-associated steatotic liver disease (MASLD) and MASLD with increased alcohol intake (MetALD) are identified as clinical entities; tools to identify patients from electronic health records (EHRs) to perform large outcome studies are lacking. In this retrospective study of participants from the Veterans Analysis of Liver Disease cohort assembled from 1/1/2013 to 12/31/2022, a rule-based natural language processing (NLP) algorithm searched EHRs for imaging evidence of hepatic steatosis. This was combined with identification of cardiometabolic risk factors and harmful alcohol use. Algorithm-derived diagnoses of MASLD, MetALD, alcohol-associated steatotic liver disease (ALD), and no steatotic liver disease (SLD) were validated using a blinded review of randomly selected charts. Among 817,657 eligible veterans, SLD was present in over half (n = 438,209, 53.5%), including MASLD in 299,259 (36.5%), 99,163 with MetALD (12.1%), and 38,552 (4.7%) with ALD. The NLP algorithm had a high correlation with steatosis on chart review, with a κ of 0.86 (95% CI 0.82-0.90), sensitivity of 0.96, and specificity of 0.90. Classification of MASLD, MetALD, ALD, and no SLD by the algorithm also showed high correlation with chart review, with a κ of 0.87 (95% CI 0.82-0.91). This algorithm identified 299,259 (36.5%) of the study cohort with MASLD, compared with 23,218 patients (2.8%) identified using International Classification of Diseases-9/10 codes. An algorithm combining rule-based NLP with cardiometabolic risk factors and alcohol use from EHRs accurately identifies and classifies SLD and can be applied in large epidemiologic studies of SLD in the Veterans Health Administration.