Exploring using HBsAg to predict interferon treatment course to achieve clinical cure in chronic hepatitis B patients: a clinical study
ObjectiveAlthough pegylated interferon α-2b (PEG-IFN α-2b) therapy for chronic hepatitis B has received increasing attention, determining the optimal treatment course remains challenging. This research aimed to develop an efficient model for predicting interferon (IFN) treatment course.MethodsPatien...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1528758/full |
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| author | Fei Yan Fei Tang Jing Chen YiCheng Lin XinYu Chen Qin Du WeiLi Yin Jing Liang Lei Liu Fang Wang BaiGuo Xu Qing Ye HuiLing Xiang |
| author_facet | Fei Yan Fei Tang Jing Chen YiCheng Lin XinYu Chen Qin Du WeiLi Yin Jing Liang Lei Liu Fang Wang BaiGuo Xu Qing Ye HuiLing Xiang |
| author_sort | Fei Yan |
| collection | DOAJ |
| description | ObjectiveAlthough pegylated interferon α-2b (PEG-IFN α-2b) therapy for chronic hepatitis B has received increasing attention, determining the optimal treatment course remains challenging. This research aimed to develop an efficient model for predicting interferon (IFN) treatment course.MethodsPatients with chronic hepatitis B, undergoing PEG-IFN α-2b monotherapy or combined with NAs (Nucleoside Analogs), were recruited from January 2018 to December 2023 at Tianjin Third Central Hospital. All patients achieved hepatitis B surface antigen (HBsAg) clearance post-treatment.ResultThe study enrolled 176 patients with chronic hepatitis B, with the median IFN treatment course of 35.23 ± 25.22 weeks. They were randomly divided into two cohorts in a ratio of 7:3. And there were 123 patients in the training cohort and 53 patients in the validation cohort. Univariable and multivariable analyses demonstrated that baseline HBsAg, 12 weeks HBsAg and the presence of cirrhosis significantly influenced IFN treatment course, and both are risk factors (β=7.27,4.27,10.91; p<0.05). After adjusting for confounding factors, HBsAg remained a significant predictor (β=6.99, 95%CI: 3.59,10.40; p<0.05), which was finally included to establish the model. The actual and predicted values in the validation cohort were highly matched, meanwhile the mean absolute percentage error (MAPE), root mean square error (RMSE) and accuracy (ACC) of the validation cohort were calculated. External validation also suggests that the model can be used as a tool for initial assessment.ConclusionBaseline HBsAg in chronic hepatitis B patients were a risk factor for prolonged IFN treatment course with a positive correlation. Ultimately, a personalized model based on baseline HBsAg levels can be established to roughly estimate the duration of interferon therapy prior to treatment initiation, thereby guiding clinical decision-making. |
| format | Article |
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| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-b64185af98cc47efac7b9a96aa741fb82025-01-10T08:41:52ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.15287581528758Exploring using HBsAg to predict interferon treatment course to achieve clinical cure in chronic hepatitis B patients: a clinical studyFei Yan0Fei Tang1Jing Chen2YiCheng Lin3XinYu Chen4Qin Du5WeiLi Yin6Jing Liang7Lei Liu8Fang Wang9BaiGuo Xu10Qing Ye11HuiLing Xiang12The Third Central Clinical College of Tianjin Medical University, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaThe Third Central Clinical College of Tianjin Medical University, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaThe Third Central Clinical College of Tianjin Medical University, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaThe Third Central Clinical College of Tianjin Medical University, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaNankai University Affiliated Third Center Hospital, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, ChinaObjectiveAlthough pegylated interferon α-2b (PEG-IFN α-2b) therapy for chronic hepatitis B has received increasing attention, determining the optimal treatment course remains challenging. This research aimed to develop an efficient model for predicting interferon (IFN) treatment course.MethodsPatients with chronic hepatitis B, undergoing PEG-IFN α-2b monotherapy or combined with NAs (Nucleoside Analogs), were recruited from January 2018 to December 2023 at Tianjin Third Central Hospital. All patients achieved hepatitis B surface antigen (HBsAg) clearance post-treatment.ResultThe study enrolled 176 patients with chronic hepatitis B, with the median IFN treatment course of 35.23 ± 25.22 weeks. They were randomly divided into two cohorts in a ratio of 7:3. And there were 123 patients in the training cohort and 53 patients in the validation cohort. Univariable and multivariable analyses demonstrated that baseline HBsAg, 12 weeks HBsAg and the presence of cirrhosis significantly influenced IFN treatment course, and both are risk factors (β=7.27,4.27,10.91; p<0.05). After adjusting for confounding factors, HBsAg remained a significant predictor (β=6.99, 95%CI: 3.59,10.40; p<0.05), which was finally included to establish the model. The actual and predicted values in the validation cohort were highly matched, meanwhile the mean absolute percentage error (MAPE), root mean square error (RMSE) and accuracy (ACC) of the validation cohort were calculated. External validation also suggests that the model can be used as a tool for initial assessment.ConclusionBaseline HBsAg in chronic hepatitis B patients were a risk factor for prolonged IFN treatment course with a positive correlation. Ultimately, a personalized model based on baseline HBsAg levels can be established to roughly estimate the duration of interferon therapy prior to treatment initiation, thereby guiding clinical decision-making.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1528758/fullchronic hepatitis B (CHB)clinical curehepatitis b surface antigen (HBsAg)PEG-IFN αmodel |
| spellingShingle | Fei Yan Fei Tang Jing Chen YiCheng Lin XinYu Chen Qin Du WeiLi Yin Jing Liang Lei Liu Fang Wang BaiGuo Xu Qing Ye HuiLing Xiang Exploring using HBsAg to predict interferon treatment course to achieve clinical cure in chronic hepatitis B patients: a clinical study Frontiers in Immunology chronic hepatitis B (CHB) clinical cure hepatitis b surface antigen (HBsAg) PEG-IFN α model |
| title | Exploring using HBsAg to predict interferon treatment course to achieve clinical cure in chronic hepatitis B patients: a clinical study |
| title_full | Exploring using HBsAg to predict interferon treatment course to achieve clinical cure in chronic hepatitis B patients: a clinical study |
| title_fullStr | Exploring using HBsAg to predict interferon treatment course to achieve clinical cure in chronic hepatitis B patients: a clinical study |
| title_full_unstemmed | Exploring using HBsAg to predict interferon treatment course to achieve clinical cure in chronic hepatitis B patients: a clinical study |
| title_short | Exploring using HBsAg to predict interferon treatment course to achieve clinical cure in chronic hepatitis B patients: a clinical study |
| title_sort | exploring using hbsag to predict interferon treatment course to achieve clinical cure in chronic hepatitis b patients a clinical study |
| topic | chronic hepatitis B (CHB) clinical cure hepatitis b surface antigen (HBsAg) PEG-IFN α model |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1528758/full |
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