ArticleScientific reports2024
A nomogram to predict mortality in patients with severe fever with thrombocytopenia syndrome.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical Predictors of Mortality in Severe Fever with Thrombocytopenia Syndrome: An Updated Systematic Review and Meta-Analysis.Pathogens (Basel, Switzerland) · 2026Pooled it
- Pre-existing immunity to heartland virus protects rhesus macaques from challenge with severe fever with thrombocytopenia syndrome virus.Emerging microbes & infections · 2026Article
- Bioinformatics identification of candidate biomarkers associated with T cell proliferation in atherosclerosis via WGCNA and machine learning.Journal of cardiothoracic surgery · 2026Article
- Risk factor analysis and construction of risk nomogram for severe fever with thrombocytopenia syndrome based on clinical classification.Scientific reports · 2026Article
- Leveraging mitochondrial dynamics-related risk signatures to predict the prognosis and tumor microenvironment of lung adenocarcinoma.Discover oncology · 2026Article
- Association of age-stratified cytokine profiles with clinical outcomes in patients with severe fever with thrombocytopenia syndrome.BMC infectious diseases · 2026Article
- Machine learning models for early mortality prediction in severe fever with thrombocytopenia syndrome.iScience · 2026Article
- Comprehensive analysis of key palmitoylation-modifying enzymes in clear cell renal cell carcinoma: implications for prognosis and therapy.Cancer cell international · 2026Article
- Repurposed metformin-apigenin combination therapy for metabolic dysfunction-associated steatohepatitis: from integrative multi-omics discovery toFrontiers in pharmacology · 2026Article
- Development and Validation of a Novel Predictive Model and Simplified Clinical Indicator for Mortality in Severe Fever with Thrombocytopenia Syndrome Patients.Infection and drug resistance · 2026Article
- Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms.Annals of medicine · 2025Article
- Age-related disparities in clinical characteristics and outcomes of patients with severe fever with thrombocytopenia syndrome.PLoS neglected tropical diseases · 2025Article
- Prediction of Mortality by Clinical Laboratory Parameters in Severe Fever with Thrombocytopenia Syndrome: A Meta-Analysis.Tropical medicine and infectious disease · 2025Review
- Integration of Bioinformatics and Machine Learning Strategies Identifies Ferroptosis and Immune Infiltration Signatures in Peri-Implantitis.International journal of molecular sciences · 2025Article
- Multi-biomarker score model for predicting fatal outcomes in severe fever with thrombocytopenia syndrome: a multicenter cohort study.Frontiers in cellular and infection microbiology · 2025Article
- Alarming implications: severe fever with thrombocytopenia syndrome and its biological vectors in the context of climate change.Frontiers in microbiology · 2025Review
- Inhibition of CYP450 family 1 subfamily B member 1 (CYP1B1) expression in macrophage reduces the inflammatory response in type 2 diabetes mellitus combined with tuberculosis.Frontiers in endocrinology · 2025Article
- Malignant epithelial cell marker-driven risk signature enables precise stratification in esophageal cancer.Frontiers in immunology · 2025Article
- Early therapy contributes to the normalization of platelet in patients with severe fever with thrombocytopenia syndrome during the convalescent phase.PLoS neglected tropical diseases · 2025Article
- Development and validation of a clinical and laboratory-based nomogram to predict mortality in patients with severe fever with thrombocytopenia syndrome.BMC infectious diseases · 2024Article
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3 authors.
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Abstract
Severe fever with thrombocytopenia syndrome (SFTS) is an acute infectious disease caused by a novel Bunyavirus infection with low population immunity and high mortality rate. Lacking specific therapies, the treatment measures vary with the severity of the disease, therefore, a case control study involved 394 SFTS patients was taken to determine risk factors for mortality. Comparative clinical data from the first 24 h after admission was collected through the electronic medical record system. Independent risk factors for death of SFTS were identified through univariate and multivariate binary logistic regression analyses. The results of the logistic regression were visualized using a nomogram which was created by downloading RMS package in the R program. In our study, four independent mortality risk factors were identified: advanced age(mean 70.45 ± 7.76 years), MODS, elevated APTT, and D-dimer. The AUC of the nomogram was 0.873 (0.832, 0.915), and the model passes the calibration test namely Unreliability test with P = 0.958, showing that the model's predictive ability is excellent. The nomogram to determine the risk of death in SFTS efficiently provide a basis for clinical decision-making for treatment.
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