Artificial intelligence to identify harmful alcohol use after early liver transplant for alcohol-associated hepatitis

Abstract

Early liver transplantation (LT) for alcohol‐associated hepatitis (AH) is the fastest growing indication for LT, but prediction of harmful alcohol use post‐LT remains limited. Among 10 ACCELERATE‐AH centers, we examined psychosocial evaluations from consecutive LT recipients for AH from 2006 to 2017. A multidisciplinary panel used content analysis to develop a maximal list of psychosocial variables. We developed an artificial intelligence model to predict post‐LT harmful alcohol use. The cohort included training (N = 91 among 8 centers) and external validation (N = 25 among 2 centers) sets, with median follow‐up of 4.4 (IQR 3.0–6.0) years post‐LT. In the training set, AUC was 0.930 (95%CI 0.862–0.998) with positive predictive value of 0.891 (95%CI 0.620–1.000), internally validated through fivefold cross‐validation. In the external validation set, AUC was 0.692 (95%CI 0.666–0.718) with positive predictive value of 0.82 (95%CI 0.625–1.000). The model identified specific variables related to social support and substance use as highly important to predict post‐LT harmful alcohol use. We retrospectively developed and validated a model that identified psychosocial profiles at LT predicting harmful alcohol use post‐LT for AH. This preliminary model may inform selection and post‐LT management for AH and warrants prospective evaluation in larger studies among all alcohol‐associated liver disease being considered for early LT.