Department of Gastroenterology, University of Kansas Medical Center, Kansas, USA.
Department of Gastroenterology and Hepatology, University of Louisville, Louisville, Kentucky, USA.
Hepatology. 2024 Nov 1;80(5):1196-1211. doi: 10.1097/HEP.0000000000000883. Epub 2024 Apr 12.
Alcohol-associated hepatitis (AH) poses significant short-term mortality. Existing prognostic models lack precision for 90-day mortality. Utilizing artificial intelligence in a global cohort, we sought to derive and validate an enhanced prognostic model.
The Global AlcHep initiative, a retrospective study across 23 centers in 12 countries, enrolled patients with AH per National Institute for Alcohol Abuse and Alcoholism criteria. Centers were partitioned into derivation (11 centers, 860 patients) and validation cohorts (12 centers, 859 patients). Focusing on 30 and 90-day postadmission mortality, 3 artificial intelligence algorithms (Random Forest, Gradient Boosting Machines, and eXtreme Gradient Boosting) informed an ensemble model, subsequently refined through Bayesian updating, integrating the derivation cohort's average 90-day mortality with each center's approximate mortality rate to produce posttest probabilities. The ALCoholic Hepatitis Artificial INtelligence Ensemble score integrated age, gender, cirrhosis, and 9 laboratory values, with center-specific mortality rates. Mortality was 18.7% (30 d) and 27.9% (90 d) in the derivation cohort versus 21.7% and 32.5% in the validation cohort. Validation cohort 30 and 90-day AUCs were 0.811 (0.779-0.844) and 0.799 (0.769-0.830), significantly surpassing legacy models like Maddrey's Discriminant Function, Model for End-Stage Liver Disease variations, age-serum bilirubin-international normalized ratio-serum Creatinine score, Glasgow, and modified Glasgow Scores ( p < 0.001). ALCoholic Hepatitis Artificial INtelligence Ensemble score also showcased superior calibration against MELD and its variants. Steroid use improved 30-day survival for those with an ALCoholic Hepatitis Artificial INtelligence Ensemble score > 0.20 in both derivation and validation cohorts.
Harnessing artificial intelligence within a global consortium, we pioneered a scoring system excelling over traditional models for 30 and 90-day AH mortality predictions. Beneficial for clinical trials, steroid therapy, and transplant indications, it's accessible at: https://aihepatology.shinyapps.io/ALCHAIN/ .
酒精相关性肝炎（AH）具有显著的短期死亡率。现有的预后模型缺乏对 90 天死亡率的精确预测。本研究利用人工智能在一个全球队列中，旨在开发和验证一种增强的预后模型。
全球 AlcHep 计划是一项在 12 个国家的 23 个中心进行的回顾性研究，纳入了符合国家酒精滥用和酒精中毒研究所标准的 AH 患者。中心分为推导（11 个中心，860 例患者）和验证队列（12 个中心，859 例患者）。本研究主要关注 30 天和 90 天的住院死亡率，3 种人工智能算法（随机森林、梯度提升机和极端梯度提升机）提供了一个集成模型，然后通过贝叶斯更新进行了优化，该模型将推导队列的平均 90 天死亡率与每个中心的近似死亡率相结合，以产生后测概率。ALCoholic 肝炎人工智能集成评分整合了年龄、性别、肝硬化和 9 项实验室值，以及中心特异性死亡率。推导队列的死亡率为 18.7%（30 天）和 27.9%（90 天），验证队列的死亡率为 21.7%和 32.5%。验证队列的 30 天和 90 天 AUC 分别为 0.811（0.779-0.844）和 0.799（0.769-0.830），显著优于传统模型，如 Maddrey 判别函数、终末期肝病模型变化、年龄-血清胆红素-国际标准化比值-血清肌酐评分、格拉斯哥和改良格拉斯哥评分（p < 0.001）。ALCoholic 肝炎人工智能集成评分在预测 MELD 及其变体方面也表现出更好的校准性能。对于 ALCoholic 肝炎人工智能集成评分 > 0.20 的患者，在推导和验证队列中使用类固醇治疗均能提高 30 天生存率。该评分系统具有良好的临床试验、类固醇治疗和移植适应证的预测价值，可在以下网址获得：https://aihepatology.shinyapps.io/ALCHAIN/ 。
