Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort studyResearch in context
Summary: Background: 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...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | EClinicalMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589537023005965 |
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| author | Fajuan Rui Yee Hui Yeo Liang Xu Qi Zheng Xiaoming Xu Wenjing Ni Youwen Tan Qing-Lei Zeng Zebao He Xiaorong Tian Qi Xue Yuanwang Qiu Chuanwu Zhu Weimao Ding Jian Wang Rui Huang Yayun Xu Yunliang Chen Junqing Fan Zhiwen Fan Xiaolong Qi Daniel Q. Huang Qing Xie Junping Shi Chao Wu Jie Li |
| author_facet | Fajuan Rui Yee Hui Yeo Liang Xu Qi Zheng Xiaoming Xu Wenjing Ni Youwen Tan Qing-Lei Zeng Zebao He Xiaorong Tian Qi Xue Yuanwang Qiu Chuanwu Zhu Weimao Ding Jian Wang Rui Huang Yayun Xu Yunliang Chen Junqing Fan Zhiwen Fan Xiaolong Qi Daniel Q. Huang Qing Xie Junping Shi Chao Wu Jie Li |
| author_sort | Fajuan Rui |
| collection | DOAJ |
| description | Summary: Background: 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. Methods: 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). Findings: 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. Interpretation: 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. Funding: 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). |
| first_indexed | 2024-03-08T12:09:07Z |
| format | Article |
| id | doaj.art-d4cf649ee1fb4d79a7373c42c3762560 |
| institution | Directory Open Access Journal |
| issn | 2589-5370 |
| language | English |
| last_indexed | 2024-03-08T12:09:07Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EClinicalMedicine |
| spelling | doaj.art-d4cf649ee1fb4d79a7373c42c37625602024-01-23T04:16:06ZengElsevierEClinicalMedicine2589-53702024-02-0168102419Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort studyResearch in contextFajuan Rui0Yee Hui Yeo1Liang Xu2Qi Zheng3Xiaoming Xu4Wenjing Ni5Youwen Tan6Qing-Lei Zeng7Zebao He8Xiaorong Tian9Qi Xue10Yuanwang Qiu11Chuanwu Zhu12Weimao Ding13Jian Wang14Rui Huang15Yayun Xu16Yunliang Chen17Junqing Fan18Zhiwen Fan19Xiaolong Qi20Daniel Q. Huang21Qing Xie22Junping Shi23Chao Wu24Jie Li25Department 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; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, ChinaKarsh Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USAClinical School of the Second People's Hospital, Tianjin Medical University, Tianjin, China; Department of Hepatology, Tianjin Second People's Hospital, Tianjin, China; Tianjin Research Institute of Liver Diseases, Tianjin, ChinaDepartment of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment 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; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, ChinaDepartment 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; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Hepatology, The Third Hospital of Zhenjiang Affiliated Jiangsu University, Zhenjiang, Jiangsu, ChinaDepartment of Infectious Diseases and Hepatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Infectious Diseases, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, ChinaSchool of Computer Science, China University of Geosciences, Wuhan, Hubei, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, Hubei, ChinaDepartment of Infectious Disease, Shandong Provincial Hospital Affiliated to Shandong Frist Medical University, Ji'nan, Shandong, ChinaDepartment of Infectious Diseases, The Fifth People's Hospital of Wuxi, Wuxi, Jiangsu, ChinaDepartment of Infectious Diseases, The Affiliated Infectious Diseases Hospital of Soochow University, Suzhou, Jiangsu, ChinaDepartment of Hepatology, Huai'an No.4 People's Hospital, Huai'an, Jiangsu, ChinaDepartment of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Infectious Disease, Shandong Provincial Hospital, Shandong University, Ji'nan, Shandong, ChinaSchool of Computer Science, China University of Geosciences, Wuhan, Hubei, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, Hubei, ChinaSchool of Computer Science, China University of Geosciences, Wuhan, Hubei, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, Hubei, ChinaDepartment of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaCenter of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical of School, Southeast University, Nanjing, Jiangsu, ChinaDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Gastroenterology and Hepatology, Department of Medicine, National University Health System, SingaporeDepartment of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China; Corresponding author. Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.Department of Infectious & Hepatology Diseases, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Corresponding author. Department of Infectious & Hepatology Diseases, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China.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; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China; Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China; Corresponding author. Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China.Summary: Background: 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. Methods: 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). Findings: 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. Interpretation: 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. Funding: 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).http://www.sciencedirect.com/science/article/pii/S2589537023005965Chronic hepatitis BHepatic steatosisInflammationMachine learningDiagnostic model |
| spellingShingle | Fajuan Rui Yee Hui Yeo Liang Xu Qi Zheng Xiaoming Xu Wenjing Ni Youwen Tan Qing-Lei Zeng Zebao He Xiaorong Tian Qi Xue Yuanwang Qiu Chuanwu Zhu Weimao Ding Jian Wang Rui Huang Yayun Xu Yunliang Chen Junqing Fan Zhiwen Fan Xiaolong Qi Daniel Q. Huang Qing Xie Junping Shi Chao Wu Jie Li Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort studyResearch in context EClinicalMedicine Chronic hepatitis B Hepatic steatosis Inflammation Machine learning Diagnostic model |
| title | Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort studyResearch in context |
| title_full | Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort studyResearch in context |
| title_fullStr | Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort studyResearch in context |
| title_full_unstemmed | Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort studyResearch in context |
| title_short | Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort studyResearch in context |
| title_sort | development of a machine learning based model to predict hepatic inflammation in chronic hepatitis b patients with concurrent hepatic steatosis a cohort studyresearch in context |
| topic | Chronic hepatitis B Hepatic steatosis Inflammation Machine learning Diagnostic model |
| url | http://www.sciencedirect.com/science/article/pii/S2589537023005965 |
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