ArticleEmerging microbes & infections2025
A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study.
Article in Emerging microbes & infections, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A clinical scoring system for the early identification of superinfections in patients with severe fever with thrombocytopenia syndrome: An ambispective multicentre cohort study.PLoS neglected tropical diseases · 2026Article
- tRNA-derived small RNA, tsRNA-5017b, as a novel biomarker for predicting severity in severe fever with thrombocytopenia syndrome.Microbiology spectrum · 2026Article
- Serum sCD25 predicts mortality and reflects IL-2-driven dysregulation of CD4⁺CD25⁺ T cells in severe fever with thrombocytopenia syndrome.Tropical medicine and health · 2026Article
- Research progress on antiviral drugs and vaccines for severe fever with thrombocytopenia syndrome.Frontiers in immunology · 2026Review
- Immune checkpoint TIM-3 defines hyperactivated NK cells and predicts fatal outcome in severe fever with thrombocytopenia syndrome.PLoS neglected tropical diseases · 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
- The Red Cell Distribution Width to Albumin Ratio as a Novel Prognostic Predictor in Patients with Severe Fever with Thrombocytopenia Syndrome: A Multicenter Study in China.Infection and drug resistance · 2026Article
- Neurological manifestations and MMP8 as a prognostic biomarker in severe fever with thrombocytopenia syndrome.PLoS neglected tropical diseases · 2025Article
- Establishment and validation of a nomogram for predicting severe fever with thrombocytopenia syndrome complicated by invasive pulmonary aspergillosis.BMC infectious diseases · 2025Article
- Impact of Epstein-Barr virus infection on clinical outcomes and immune profiles in patients with severe fever with thrombocytopenia syndrome.Frontiers in immunology · 2025Article
- Dabie bandavirus identified from patients with severe fever with thrombocytopenia syndrome in northwestern of Hubei Province, China, 2024.Frontiers in public health · 2025Article
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Authors and funding
25 authors.
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Abstract
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that imposes a considerable medical burden. In this study, we enrolled 1,606 SFTS patients, developed and validated machine learning models for mortality prediction, and ultimately constructed a model consisting of six variables. The prediction model, UNION-SFTS, constructed using the multilayer perceptron (MLP) algorithm, achieved the best performance with an area under the curve (AUC) of 0.917, an accuracy of 0.905, and a precision of 0.795 on the internal validation set. Additionally, the model achieved an AUC of 0.883 on the prospective validation set and AUCs of 1.000, 0.927 and 0.905 on the three external validation sets, respectively. We developed a user-friendly web-based calculator for clinical use, available at http://175.178.66.58/english/. By utilizing the UNION-SFTS model, clinicians can promptly predict and monitor the disease severity and mortality risk of SFTS patients, enabling early intervention in severe cases and ultimately reduces patient mortality.
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