Rui Fajuan, Yeo Yee Hui, Xu Liang, Zheng Qi, Xu Xiaoming, Ni Wenjing, Tan Youwen, Zeng Qing-Lei, He Zebao, Tian Xiaorong, Xue Qi, Qiu Yuanwang, Zhu Chuanwu, Ding Weimao, Wang Jian, Huang Rui, Xu Yayun, Chen Yunliang, Fan Junqing, Fan Zhiwen, Qi Xiaolong, Huang Daniel Q, Xie Qing, Shi Junping, Wu Chao, Li Jie
Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Department of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China.
EClinicalMedicine. 2024 Jan 16;68:102419. doi: 10.1016/j.eclinm.2023.102419. eCollection 2024 Feb.
With increasingly prevalent coexistence of chronic hepatitis B (CHB) and hepatic steatosis (HS), simple, non-invasive diagnostic methods to accurately assess the severity of hepatic inflammation are needed. We aimed to build a machine learning (ML) based model to detect hepatic inflammation in patients with CHB and concurrent HS.
We conducted a multicenter, retrospective cohort study in China. Treatment-naive CHB patients with biopsy-proven HS between April 2004 and September 2022 were included. The optimal features for model development were selected by SHapley Additive explanations, and an ML algorithm with the best accuracy to diagnose moderate to severe hepatic inflammation (Scheuer's system ≥ G3) was determined and assessed by decision curve analysis (DCA) and calibration curve. This study is registered with ClinicalTrials.gov (NCT05766449).
From a pool of 1,787 treatment-naive patients with CHB and HS across eleven hospitals, 689 patients from nine of these hospitals were chosen for the development of the diagnostic model. The remaining two hospitals contributed to two independent external validation cohorts, comprising 509 patients in validation cohort 1 and 589 in validation cohort 2. Eleven features regarding inflammation, hepatic and metabolic functions were identified. The gradient boosting classifier (GBC) model showed the best performance in predicting moderate to severe hepatic inflammation, with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.83-0.88) in the training cohort, and 0.89 (95% CI 0.86-0.92), 0.76 (95% CI 0.73-0.80) in the first and second external validation cohorts, respectively. A publicly accessible web tool was generated for the model.
Using simple parameters, the GBC model predicted hepatic inflammation in CHB patients with concurrent HS. It holds promise for guiding clinical management and improving patient outcomes.
This research was supported by the National Natural Science Foundation of China (No. 82170609, 81970545), Natural Science Foundation of Shandong Province (Major Project) (No. ZR2020KH006), Natural Science Foundation of Jiangsu Province (No.BK20231118), Tianjin Key Medical Discipline (Specialty), Construction Project, TJYXZDXK-059B, Tianjin Health Science and Technology Project key discipline special, TJWJ2022XK034, and Research project of Chinese traditional medicine and Chinese traditional medicine combined with Western medicine of Tianjin municipal health and Family Planning Commission (2021022).
随着慢性乙型肝炎（CHB）与肝脂肪变性（HS）并存的情况日益普遍，需要简单、无创的诊断方法来准确评估肝脏炎症的严重程度。我们旨在建立一个基于机器学习（ML）的模型，以检测CHB合并HS患者的肝脏炎症。
我们在中国进行了一项多中心回顾性队列研究。纳入2004年4月至2022年9月间未经治疗且经活检证实患有HS的CHB患者。通过Shapley值法选择模型开发的最佳特征，并确定诊断中度至重度肝脏炎症（Scheuer系统≥G3）准确率最高的ML算法，并通过决策曲线分析（DCA）和校准曲线进行评估。本研究已在ClinicalTrials.gov注册（NCT05766449）。
在11家医院的1787例未经治疗的CHB合并HS患者中，选择了其中9家医院的689例患者来开发诊断模型。其余两家医院提供了两个独立的外部验证队列，验证队列1中有509例患者，验证队列2中有589例患者。确定了11个与炎症、肝脏和代谢功能相关的特征。梯度提升分类器（GBC）模型在预测中度至重度肝脏炎症方面表现最佳，训练队列中受试者操作特征曲线下面积（AUROC）为0.86（95%CI 0.83-0.88），在第一个和第二个外部验证队列中分别为0.89（95%CI 0.86-0.92）、0.76（95%CI 0.73-0.80）。为该模型生成了一个可公开访问的网络工具。
使用简单参数，GBC模型可预测CHB合并HS患者的肝脏炎症。它有望指导临床管理并改善患者预后。
本研究得到中国国家自然科学基金（No. 82170609、81970545）、山东省自然科学基金（重大项目）（No. ZR2020KH006）、江苏省自然科学基金（No.BK20231118）、天津市重点医学学科（专科）建设项目TJYXZDXK-059B、天津市卫生科技项目重点学科专项TJWJ2022XK034以及天津市卫生健康委员会中医药和中西医结合研究项目（2021022）的支持。
