ArticleScientific reports2026
Risk factor analysis and construction of risk nomogram for severe fever with thrombocytopenia syndrome based on clinical classification.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Severe Fever with Thrombocytopenia Syndrome (SFTS) is an acute infectious disease caused by the novel bunyavirus with high mortality rates. Current lack of systematic clinical classification standards and effective risk prediction tools necessitates early identification of severe patients to improve prognosis. We retrospectively analyzed clinical data from 437 SFTS patients admitted to The First Affiliated Hospital of USTC and Hefei Second People's Hospital from January 2022 to December 2024. Patients were classified into mild (177 cases, 40.5%), severe (202 cases, 46.2%), and critical (58 cases, 13.3%) categories based on disease severity. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for disease progression (severe+critical vs. mild). A risk prediction nomogram was constructed using R software with internal validation. Multivariate analysis revealed seven independent risk factors for disease progression: IL-6 > 50 pg/mL (OR = 7.82, 95%CI: 4.23-14.47, P < 0.001), altered consciousness (OR = 6.85, 95%CI: 3.94-11.91, P < 0.001), hemorrhagic manifestations (OR = 4.21, 95%CI: 1.85-9.58, P < 0.001), high-sensitivity CRP > 10 mg/L (OR = 3.45, 95%CI: 2.18-5.47, P < 0.001), pulmonary infection (OR = 2.94, 95%CI: 2.01-4.30, P < 0.001), AST > 200 U/L (OR = 2.13, 95%CI: 1.47-3.08, P < 0.001), and age ≥ 65 years (OR = 1.68, 95%CI: 1.15-2.46, P = 0.007). The nomogram model demonstrated good discriminative ability with a C-index of 0.806 (95%CI: 0.804-0.878) and ROC-AUC of 0.806. Calibration curves showed high concordance between predicted and actual probabilities (Hosmer-Lemeshow test P = 0.512), with sensitivity of 78.5% and specificity of 81.9%. This nomogram prediction model incorporating seven independent risk factors can effectively assist clinical identification of high-risk SFTS patients and has significant clinical utility for early intervention and personalized treatment strategies.
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