Artificial intelligence to identify harmful alcohol use after early liver transplant for alcohol‐associated hepatitis. Issue 7 (25th April 2022)

• Record Type: Journal Article
• Authors: Lee, Brian P.; Roth, Nitzan; Rao, Prathik; Im, Gene Y.; Vogel, Alexander S.; Hasbun, Johann; Roth, Yoel; Shenoy, Akhil; Arvelakis, Antonios; Ford, Laura; Dawe, Inga; Schiano, Thomas D.; Davis, Jordan P.; Rice, John P.; Eswaran, Sheila; Weinberg, Ethan; Han, Hyosun; Hsu, Christine; Fix, Oren K.; Maddur, Haripriya; Ghobrial, R. Mark; Therapondos, George; Dilkina, Bistra; Terrault, Norah A.
• Is Part Of: American journal of transplantation. Volume 22:Issue 7(2022)
• Page Start: 1834
• Page End: 1841
• Publication Date: 2022-04-25
• Subjects: alcoholism and substance abuse -- clinical research/practice -- liver transplantation/hepatology -- risk assessment/risk stratification; Transplantation of organs, tissues, etc -- Periodicals; 617.95
• DOI: 10.1111/ajt.17059
• Languages: English
• ISSNs: 1600-6135

Abstract

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. Abstract : This study from the American Consortium of Early Liver Transplantation for Alcohol‐Associated Hepatitis (ACCELERATE‐AH) combines content analysis and artificial intelligence to develop a foundational model from pre‐transplant psychosocial variables to predict post‐transplant alcohol relapse in patients with severe alcohol‐associated hepatitis.