• Camille A Kezer, Seth M Buryska, Joseph C Ahn, William S Harmsen, Winston Dunn, Ashwani K Singal, Juan P Arab, Luis A Díaz, Jorge Arnold, Patrick S Kamath, Vijay H Shah, and Douglas A Simonetto.
• Department of Internal Medicine, Mayo Clinic, Rochester, MN.
• Mayo Clin. Proc. 2022 Mar 1; 97 (3): 480-490.

ObjectiveTo develop a new scoring system that more accurately predicts 30-day mortality in patients with alcohol-associated hepatitis (AH).MethodsA cohort of consecutive adults diagnosed with AH at a single academic center from January 1, 1998, to December 31, 2018, was identified for model derivation. Multivariate logistic regression was used to create a new scoring system to predict 30-day mortality. External validation of this score was performed on a multicenter retrospective cohort.ResultsIn the derivation cohort of 266 patients, the 30-day mortality rate was 19.2%. The following variables were found to be significantly associated with mortality on multivariate analysis: age (P=.002), blood urea nitrogen (P=.003), albumin (P=.01), bilirubin (P=.02), and international normalized ratio (P=.001). A model incorporating these variables, entitled the Mortality Index for Alcohol-Associated Hepatitis (MIAAH), achieved a C statistic of 0.86. Comparison of the accuracy of the MIAAH to existing prognostic models, including the Model for End-Stage Liver Disease and Maddrey Discriminant Function, showed that the highest concordance was achieved by the MIAAH and that this difference was significant. In the validation cohort of 249 patients, the MIAAH C statistic decreased to 0.73 and was found to be significantly superior to the Maddrey Discriminant Function but not to the Model for End-Stage Liver Disease.ConclusionThe MIAAH competes with the current prognostication models and is at a minimum as accurate as these existing scores in identifying patients with AH at high risk of short-term mortality. Furthermore, the MIAAH demonstrates advantageous performance characteristics in its ability to increasingly accurately dichotomize patients into those at highest risk of death and those likely to survive.Copyright © 2021 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

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