Predicting mortality risk of severe fever with thrombocytopenia syndrome: A multi-center retrospective cohort study

Xue-Geng Hong; Hong-Han Ge; Ning Cui; Yan-Li Xu; Xin Yang; Jia-Hao Chen; Xiao-Hong Yin; Yi-Mei Yuan; Chao Zhou; Hao Li; Xiao-Ai Zhang; Ming Yue; Ling Lin; Wei Liu

doi:10.1016/j.virs.2025.12.008

December 2025

Citation: Xue-Geng Hong, Hong-Han Ge, Ning Cui, Yan-Li Xu, Xin Yang, Jia-Hao Chen, Xiao-Hong Yin, Yi-Mei Yuan, Chao Zhou, Hao Li, Xiao-Ai Zhang, Ming Yue, Ling Lin, Wei Liu. Predicting mortality risk of severe fever with thrombocytopenia syndrome: A multi-center retrospective cohort study .VIROLOGICA SINICA, 2025, 40(6) : 1011-1020. http://dx.doi.org/10.1016/j.virs.2025.12.008

Predicting mortality risk of severe fever with thrombocytopenia syndrome: A multi-center retrospective cohort study

Xue-Geng Hong ^a,b ,
Hong-Han Ge ^c ,
Ning Cui ^d ,
Yan-Li Xu ^e ,
Xin Yang ^a ,
Jia-Hao Chen ^a ,
Xiao-Hong Yin ^a ,
Yi-Mei Yuan ^d ,
Chao Zhou ^d ,
Hao Li ^a ,
Xiao-Ai Zhang ^a ,
Ming Yue ^{f
,,} ,
Ling Lin ^{e
,,} ,
Wei Liu ^{a,g
,,}

a. State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing 100071, China;
b. Department of Medical Services, The 960th Hospital of the PLA Joint Logistics Support Force, Jinan 250031, China;
c. School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China;
d. The 154th Hospital, China RongTong Medical Healthcare Group Co. Ltd, Xinyang 464000, China;
e. Department of Infectious Diseases, Yantai Qishan Hospital, Yantai 264001, China;
f. Department of Infectious Diseases, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China;
g. School of Public Health, Anhui Medical University, Hefei 230032, China

Corresponding author: Ming Yue, njym08@163.com
Ling Lin, linling4012@163.com
Wei Liu, lwbime@163.com
Received Date: 10 August 2025
Accepted Date: 06 December 2025
Available online: 10 December 2025

Abstract

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne disease with high mortality, and clinical practice lacks dynamic tools to assess its rapidly evolving course. This study aims to develop stage-specific machine learning models to predict mortality risk using longitudinal biomarker data. We conducted a retrospective analysis of 5359 laboratory-confirmed SFTS patients from two hospitals in the highly endemic region in China. Serial measurements of 46 clinical and laboratory variables were integrated into a three-stage prognostic model developed using extreme gradient boosting (XGBoost). Within each clinical stage, key predictors and their relative contribution (RC) of mortality risk were assessed. Model performance was assessed based on discrimination, calibration, and decision curve analysis (DCA) in internal and external test sets. XGBoost models were constructed across 10 temporal phases, later consolidated into three clinically distinct stages via hierarchical clustering: early (≤7 days), intermediate (days 8-9), and late (≥10 days). Key predictors included age (dominant in early phase; RC, 18.44%), lactate dehydrogenase (LDH; RC peaking at 60.10% in late phase), and monocyte percentage (RC range from 5.25% to 16.04%). Pathophysiological shifts across clinical stages were revealed: early viral cytopathy (dominated by age and MONO%), intermediate immunopathology (marked by LDH surge), and late hepatic failure (dominated by LDH, AST, and TBA). The model showed strong discrimination (Area under the receiver operating characteristic curve, AUCs: 0.84-0.98 internal; 0.91-0.98 external), calibration (Brier scores: 0.04-0.11), and clinical utility via DCA. This study introduces a dynamic staging system that leverages predictive models and real-time patient data to monitor mortality risk and personalize SFTS care, which enables timely interventions to reduce deaths.
- Severe fever with thrombocytopenia syndrome (SFTS)
- , Machine learning
- , Clinical stage
- , Fatal outcome
- , Prediction model

Electronic Supplementary Material

10.1016j.virs.2025.12.008-ESM1.docx
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