Assessment of compensated advanced chronic liver disease based on serum bile acids in chronic hepatitis B patients
Abstract 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 pros...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-39977-8 |
| _version_ | 1797576831403032576 |
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| author | Fei Chen Yaning Yao Zhen Li Long Deng Ruiling He |
| author_facet | Fei Chen Yaning Yao Zhen Li Long Deng Ruiling He |
| author_sort | Fei Chen |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-10T21:59:16Z |
| format | Article |
| id | doaj.art-50c62618934943afa366755ff0ef306d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T21:59:16Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-50c62618934943afa366755ff0ef306d2023-11-19T13:00:40ZengNature PortfolioScientific Reports2045-23222023-08-011311810.1038/s41598-023-39977-8Assessment of compensated advanced chronic liver disease based on serum bile acids in chronic hepatitis B patientsFei Chen0Yaning Yao1Zhen Li2Long Deng3Ruiling He4Department of Ultrasound, The First Hospital of Lanzhou UniversityDepartment of Ultrasound, The First Hospital of Lanzhou UniversityDepartment of Ultrasound, Donggang Branch, The First Hospital of Lanzhou UniversityDepartment of Ultrasound, The First Hospital of Lanzhou UniversityDepartment of Ultrasound, Donggang Branch, The First Hospital of Lanzhou UniversityAbstract 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.https://doi.org/10.1038/s41598-023-39977-8 |
| spellingShingle | Fei Chen Yaning Yao Zhen Li Long Deng Ruiling He Assessment of compensated advanced chronic liver disease based on serum bile acids in chronic hepatitis B patients Scientific Reports |
| title | Assessment of compensated advanced chronic liver disease based on serum bile acids in chronic hepatitis B patients |
| title_full | Assessment of compensated advanced chronic liver disease based on serum bile acids in chronic hepatitis B patients |
| title_fullStr | Assessment of compensated advanced chronic liver disease based on serum bile acids in chronic hepatitis B patients |
| title_full_unstemmed | Assessment of compensated advanced chronic liver disease based on serum bile acids in chronic hepatitis B patients |
| title_short | Assessment of compensated advanced chronic liver disease based on serum bile acids in chronic hepatitis B patients |
| title_sort | assessment of compensated advanced chronic liver disease based on serum bile acids in chronic hepatitis b patients |
| url | https://doi.org/10.1038/s41598-023-39977-8 |
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