Development and Validation of a Novel Model to Discriminate Idiosyncratic Drug-Induced Liver Injury and Autoimmune Hepatitis

IF 6 2区医学 Q1 GASTROENTEROLOGY & HEPATOLOGY

Liver International Pub Date : 2025-01-16 DOI:10.1111/liv.16239

Yu Wang, Xuhui Lin, Ying Sun, Jimin Liu, Jia Li, Qiuju Tian, Feng Guo, Xiaoli Hu, Liang Wang, Pingying Li, Jingshou Chen, Yan Wang, Zikun Ma, Jidong Jia, Jing Zhang, Zhengsheng Zou, Xinyan Zhao

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引用次数: 0

Abstract

Background and Aim

Discriminating between idiosyncratic drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) is critical yet challenging. We aim to develop and validate a machine learning (ML)-based model to aid in this differentiation.

Methods

This multicenter cohort study utilised a development set from Beijing Friendship Hospital, with retrospective and prospective validation sets from 10 tertiary hospitals across various regions of China spanning January 2009 to May 2023. Different ML algorithms were tested using 24 routine laboratory parameters. The Shapley Additive exPlanations (SHAP) analysis was used to evaluate the contribution of each parameter in the ML model.

Results

A total of 2554 patients (1750 for DILI and 804 for AIH) were included. Using Gradient Boost Decision Tree algorithm, five key parameters—aspartate transaminase, globulin, prealbumin, creatinine and platelet count—were selected to construct the ML model. Consequently, a web-based tool named Beijing-AID (BJ-AID) was developed (http://43.143.153.225:5000/). The BJ-AID model demonstrated excellent discrimination performance, with an area under the receiver operating characteristic curve (AUROC) of 0.94 (95% CI, 0.902–0.975) in the development set, 0.91 (95% CI, 0.900–0.928) in all external validation sets and 0.93 (95% CI, 0.889–0.974) in a prospective validation set. Notably, the BJ-AID model also effectively discriminated atypical cases, including drug-induced autoimmune-like hepatitis and AIH with the history of drug consumption, achieving an AUROC = 0.85 (95% CI, 0.742–0.949).

Conclusions

We successfully developed and validated a machine learning-based model, BJ-AID, which exhibits a strong discrimination performance. BJ-AID can assist practitioners and hepatologists in diagnosing both typical and atypical cases of DILI and AIH.

Trial Registration

ClinicalTrials.gov identifier: NCT05532345

查看原文本刊更多论文

鉴别特异性药物性肝损伤和自身免疫性肝炎新模型的建立和验证。

背景和目的：区分特异性药物性肝损伤（DILI）和自身免疫性肝炎（AIH）是至关重要但具有挑战性的。我们的目标是开发和验证一个基于机器学习（ML）的模型，以帮助实现这种区分。方法：本多中心队列研究使用了北京友谊医院的开发集，以及2009年1月至2023年5月中国各地区10家三级医院的回顾性和前瞻性验证集。使用24个常规实验室参数测试不同的ML算法。使用Shapley加性解释（SHAP）分析来评估ML模型中每个参数的贡献。结果：共纳入2554例患者（DILI 1750例，AIH 804例）。采用梯度提升决策树算法，选取谷草转氨酶、球蛋白、前白蛋白、肌酐和血小板计数5个关键参数构建ML模型。因此，一个基于网络的工具被命名为北京-援助（BJ-AID）被开发（http://43.143.153.225:5000/）。BJ-AID模型表现出优异的鉴别性能，开发集的受试者工作特征曲线下面积（AUROC）为0.94 (95% CI, 0.902-0.975)，所有外部验证集的AUROC为0.91 (95% CI, 0.900-0.928)，前瞻性验证集的AUROC为0.93 （95% CI, 0.889-0.974）。值得注意的是，BJ-AID模型也有效地区分了非典型病例，包括药物性自身免疫样肝炎和有药物消费史的AIH， AUROC = 0.85 （95% CI, 0.742-0.949）。结论：我们成功开发并验证了基于机器学习的BJ-AID模型，该模型具有较强的识别性能。BJ-AID可以帮助医生和肝病学家诊断DILI和AIH的典型和非典型病例。试验注册：ClinicalTrials.gov标识符：NCT05532345。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Liver International 医学-胃肠肝病学

CiteScore

13.90

自引率

4.50%

发文量

348

审稿时长

2 months

期刊介绍： Liver International promotes all aspects of the science of hepatology from basic research to applied clinical studies. Providing an international forum for the publication of high-quality original research in hepatology, it is an essential resource for everyone working on normal and abnormal structure and function in the liver and its constituent cells, including clinicians and basic scientists involved in the multi-disciplinary field of hepatology. The journal welcomes articles from all fields of hepatology, which may be published as original articles, brief definitive reports, reviews, mini-reviews, images in hepatology and letters to the Editor.