ArticleAnnals of medicine2025
Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms.
Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predicting PD-L1 + CD8 - status in non-small cell lung cancer tissue from clinical indicators using machine learning.Chinese medical journal · 2026Article
- Hyper-activated low-density neutrophil-derived NGAL predicts fatal SFTS: a multi-cohort and single-cell study.Infection · 2026Article
- Early mortality risk prediction in severe fever with thrombocytopenia syndrome using an interpretable machine learning model based on routine clinical parameters.Frontiers in public health · 2026Article
- Development of a Lateral Flow Immunoassay Strips for Rapid Antigen Detection of Dabie bandavirus.ACS omega · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aimed at identifying acute phase biomarkers in Severe Fever with Thrombocytopenia Syndrome (SFTS), and to establish a model to predict mortality outcomes.
methodsA 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.
resultsSeven 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.
conclusionsa-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.
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