ArticleBMC infectious diseases2024
Construction and validation of a dynamic nomogram using Lasso-logistic regression for predicting the severity of severe fever with thrombocytopenia syndrome patients at admission.
Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Genotype-Specific Amino Acid Signatures in the Gn/Gc Glycoprotein of Severe Fever with Thrombocytopenia Syndrome Virus Associated with Neurological Symptoms.Pathogens (Basel, Switzerland) · 2026Article
- Machine learning models for early mortality prediction in severe fever with thrombocytopenia syndrome.iScience · 2026Article
- Observational
- Admission D-Dimer-to-Albumin Ratio as a Predictor of Poor Prognosis in Patients with Severe Fever with Thrombocytopenia Syndrome: A Retrospective Cohort Study.Infection and drug resistance · 2026Article
- A nomogram prediction model for the risk of intra-abdominal infection after endoscopic full-thickness resection of gastric submucosal tumors.Biomedical engineering online · 2025Article
- Blood transcriptomic profiling reveals gene expression alterations in patients with SFTS-associated encephalitis.Microbiology spectrum · 2025Article
- Age-related disparities in clinical characteristics and outcomes of patients with severe fever with thrombocytopenia syndrome.PLoS neglected tropical diseases · 2025Article
- Time-Varying Effects of Glucocorticoid Treatment in Critically III Patients with Severe Fever with Thrombocytopenia Syndrome: An Inverse Probability of Treatment Weighting Analysis.Journal of inflammation research · 2025Article
- Pathogen profile of co-infections and mortality determinants among patients with severe fever with thrombocytopenia syndrome.Frontiers in cellular and infection microbiology · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundSevere fever with thrombocytopenia syndrome (SFTS) is a highly fatal infectious disease caused by the SFTS virus (SFTSV), posing a significant public health threat. This study aimed to construct a dynamic model for the early identification of SFTS patients at high risk of disease progression.
methodsAll eligible patients enrolled between April 2014 and July 2023 were divided into training and validation sets. Thirty-four clinical variables in the training set underwent analysis using least absolute shrinkage and selection operator (LASSO) logistic regression. Selected variables were then input into the multivariate logistic regression model to construct a dynamic nomogram. The model's performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) in both training and validation sets. Kaplan-Meier survival analysis was utilized to evaluate prognostic performance.
results299 SFTS patients entered the final investigation, with 208 patients in the training set and 90 patients in the validation set. LASSO and the multivariate logistic regression identified six significant prediction factors: age (OR, 1.060; 95% CI, 1.017-1.109; P = 0.007), CREA (OR, 1.017; 95% CI, 1.003-1.031; P = 0.019), PT (OR, 1.765; 95% CI, 1.175-2.752; P = 0.008), D-dimer (OR, 1.039; 95% CI, 1.005-1.078; P = 0.032), nervous system symptoms (OR, 8.244; 95% CI, 3.035-26.858; P < 0.001) and hemorrhage symptoms (OR, 3.414; 95% CI, 1.096-10.974; P = 0.035). The AUC-ROC, C-index, calibration plots, and DCA demonstrated the robust performance of the nomogram in predicting severity at admission, and Kaplan-Meier survival analysis indicated its utility in predicting 28-day mortality among SFTS patients. The dynamic nomogram is accessible at https://sfts.shinyapps.io/SFTS_severity_nomogram/ .
conclusionThis study provided a practical and readily applicable tool for the early identification of high-risk SFTS patients, enabling the timely initiation of intensified treatments and protocol adjustments to mitigate disease progression.
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