Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms
Objective We aimed at identifying acute phase biomarkers in Severe Fever with Thrombocytopenia Syndrome (SFTS), and to establish a model to predict mortality outcomes.Methods A retrospective analysis was conducted on multicenter clinical data. Group-based trajectory modeling (GBTM) was utilized to d...
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Taylor & Francis Group
2025-12-01
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| Series: | Annals of Medicine |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2025.2451184 |
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| author | Jie Zhu Jianmei Zhou Chunhui Tao Guomei Xia Bingyan Liu Xiaowei Zheng Xu Li Zhenhua Zhang |
| author_facet | Jie Zhu Jianmei Zhou Chunhui Tao Guomei Xia Bingyan Liu Xiaowei Zheng Xu Li Zhenhua Zhang |
| author_sort | Jie Zhu |
| collection | DOAJ |
| description | Objective We aimed at identifying acute phase biomarkers in Severe Fever with Thrombocytopenia Syndrome (SFTS), and to establish a model to predict mortality outcomes.Methods A retrospective analysis was conducted on multicenter clinical data. Group-based trajectory modeling (GBTM) was utilized to demonstrate the overall trend of laboratory indicators and their correlation with mortality. Six different machine learning algorithms were employed to develop prognostic models based on the clinical features during the acute phase, which were reduced using Lasso regression.Results Seven indicators (ALT, AST, BUN, LDH, a-HBDH, DD, and PLT) at 7-10 days post-onset and their change slopes were found to be crucial during disease progression. These, along with other clinical features, were reduced to 8 variables using Lasso regression for model construction. The random forest model demonstrated the best performance in both internal validation (AUC: 0.961) and external validation (AUC: 0.948). Decision Curve Analysis indicated a good balance between model benefits and risks.Conclusions a-HBDH and its change slope along with central nervous symptom manifestations within 7-10 days after onset accurately predicted mortality in SFTS. Various algorithms provided a comprehensive overview of disease progression and constructed more stable and efficient models. |
| format | Article |
| id | doaj-art-5d19a877c0594203afad2f23b6a08674 |
| institution | Kabale University |
| issn | 0785-3890 1365-2060 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of Medicine |
| spelling | doaj-art-5d19a877c0594203afad2f23b6a086742025-01-13T12:29:44ZengTaylor & Francis GroupAnnals of Medicine0785-38901365-20602025-12-0157110.1080/07853890.2025.2451184Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithmsJie Zhu0Jianmei Zhou1Chunhui Tao2Guomei Xia3Bingyan Liu4Xiaowei Zheng5Xu Li6Zhenhua Zhang7Institute of Clinical Virology, Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaInstitute of Clinical Virology, Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaInstitute of Clinical Virology, Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaInstitute of Clinical Virology, Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaInstitute of Clinical Virology, Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Infectious Diseases, The First Affiliated Hospital of University of Science and Technology of China, Hefei, ChinaDepartment of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaInstitute of Clinical Virology, Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaObjective We aimed at identifying acute phase biomarkers in Severe Fever with Thrombocytopenia Syndrome (SFTS), and to establish a model to predict mortality outcomes.Methods A retrospective analysis was conducted on multicenter clinical data. Group-based trajectory modeling (GBTM) was utilized to demonstrate the overall trend of laboratory indicators and their correlation with mortality. Six different machine learning algorithms were employed to develop prognostic models based on the clinical features during the acute phase, which were reduced using Lasso regression.Results Seven indicators (ALT, AST, BUN, LDH, a-HBDH, DD, and PLT) at 7-10 days post-onset and their change slopes were found to be crucial during disease progression. These, along with other clinical features, were reduced to 8 variables using Lasso regression for model construction. The random forest model demonstrated the best performance in both internal validation (AUC: 0.961) and external validation (AUC: 0.948). Decision Curve Analysis indicated a good balance between model benefits and risks.Conclusions a-HBDH and its change slope along with central nervous symptom manifestations within 7-10 days after onset accurately predicted mortality in SFTS. Various algorithms provided a comprehensive overview of disease progression and constructed more stable and efficient models.https://www.tandfonline.com/doi/10.1080/07853890.2025.2451184Severe Fever with Thrombocytopenia Syndromecritical phasegroup-based trajectory fittingmachine learning prognostic model |
| spellingShingle | Jie Zhu Jianmei Zhou Chunhui Tao Guomei Xia Bingyan Liu Xiaowei Zheng Xu Li Zhenhua Zhang Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms Annals of Medicine Severe Fever with Thrombocytopenia Syndrome critical phase group-based trajectory fitting machine learning prognostic model |
| title | Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms |
| title_full | Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms |
| title_fullStr | Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms |
| title_full_unstemmed | Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms |
| title_short | Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms |
| title_sort | identification of early prognostic biomarkers in severe fever with thrombocytopenia syndrome using machine learning algorithms |
| topic | Severe Fever with Thrombocytopenia Syndrome critical phase group-based trajectory fitting machine learning prognostic model |
| url | https://www.tandfonline.com/doi/10.1080/07853890.2025.2451184 |
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