A Cytokine-Based Prognostic Model for Predicting Mortality in Patients with Severe Fever with Thrombocytopenia Syndrome: A Cohort Study

Objective: This study aims to characterize the cytokine profiles of severe cases of Severe Fever with Thrombocytopenia Syndrome (SFTS) and to establish an effective prognostic model based on these characteristics. By analyzing clinical symptoms, laboratory markers, and cytokine levels, the study explores their relationships with disease severity and clinical outcomes, ultimately providing guidance for early diagnosis and intervention. Methods: This study included SFTS patients admitted to the First Affiliated Hospital of Zhejiang University School of Medicine from January 2014 to November 2024, whose clinical data and outcomes were analyzed. Key prognostic indicators were selected using LASSO regression analysis, combining cytokines (such as IL-6, IL-10, etc.) and clinical laboratory markers (such as platelet count, creatinine, etc.) to construct a prognostic prediction model. Additionally, multivariate logistic regression was performed to further refine the prognostic model. Results: A total of 110 patients were included in the analysis, with 22 in the recovery group and 88 in the death group. A comparison of clinical and laboratory data revealed significant differences in multiple physiological systems, such as platelet count, renal function, liver function, and coagulation status. Cytokines IL-6, IL-8, IL-10, and IFN-α were significantly higher in the death group compared to the recovery group, indicating that these cytokines may be closely associated with the prognosis of SFTS. Furthermore, the prognostic models (Model A and Model B) demonstrated AUC values of 0.8533 and 0.8982, respectively, with Model B showing improved prognostic accuracy. Conclusion: Cytokines, such as IL-6 and IL-10, are closely related to the severity of SFTS and can serve as early warning indicators. By incorporating these biomarkers with clinical symptoms and laboratory markers, the accuracy of early prognosis prediction for severe SFTS can be improved, providing a scientific basis for clinical intervention and ultimately enhancing patient outcomes while reducing social and economic burdens.