Department of Ultrasound, The First Hospital of Lanzhou University, Lanzhou, 730000, China.
Department of Ultrasound, Donggang Branch, The First Hospital of Lanzhou University, Lanzhou, 730000, China.
Sci Rep. 2023 Aug 8;13(1):12834. doi: 10.1038/s41598-023-39977-8.
Patients with chronic liver disease progressed to compensated advanced chronic liver disease (cACLD), the risk of liver-related decompensation increased significantly. This study aimed to develop prediction model based on individual bile acid (BA) profiles to identify cACLD. This study prospectively recruited 159 patients with hepatitis B virus (HBV) infection and 60 healthy volunteers undergoing liver stiffness measurement (LSM). With the value of LSM, patients were categorized as three groups: F1 [LSM ≤ 7.0 kilopascals (kPa)], F2 (7.1 < LSM ≤ 8.0 kPa), and cACLD group (LSM ≥ 8.1 kPa). Random forest (RF) and support vector machine (SVM) were applied to develop two classification models to distinguish patients with different degrees of fibrosis. The content of individual BA in the serum increased significantly with the degree of fibrosis, especially glycine-conjugated BA and taurine-conjugated BA. The Marco-Precise, Marco-Recall, and Marco-F1 score of the optimized RF model were all 0.82. For the optimized SVM model, corresponding score were 0.86, 0.84, and 0.85, respectively. RF and SVM models were applied to identify individual BA features that successfully distinguish patients with cACLD caused by HBV. This study provides a new tool for identifying cACLD that can enable clinicians to better manage patients with chronic liver disease.
患有慢性肝病的患者进展为代偿性晚期慢性肝病（cACLD），肝相关失代偿的风险显著增加。本研究旨在开发一种基于个体胆汁酸（BA）谱的预测模型，以识别 cACLD。本研究前瞻性招募了 159 名乙型肝炎病毒（HBV）感染患者和 60 名健康志愿者进行肝硬度测量（LSM）。根据 LSM 值，患者分为三组：F1 [LSM≤7.0 千帕斯卡（kPa）]、F2（7.1<LSM≤8.0 kPa）和 cACLD 组（LSM≥8.1 kPa）。随机森林（RF）和支持向量机（SVM）用于开发两种分类模型，以区分不同纤维化程度的患者。血清中个体 BA 的含量随纤维化程度显著增加，尤其是甘氨酸结合 BA 和牛磺酸结合 BA。优化 RF 模型的 Marco-Precise、Marco-Recall 和 Marco-F1 评分均为 0.82。对于优化的 SVM 模型，相应的评分分别为 0.86、0.84 和 0.85。RF 和 SVM 模型被用于识别能够成功区分 HBV 引起的 cACLD 患者的个体 BA 特征。本研究为识别 cACLD 提供了一种新工具，使临床医生能够更好地管理慢性肝病患者。
