Integration of SFTSV Viral Load, Age, and Double‐Negative B‐Cells as Prognostic Biomarkers for Severe Fever With Thrombocytopenia Syndrome Outcomes

Background Severe fever with thrombocytopenia syndrome (SFTS) is a tick‐borne viral illness with high mortality, yet early risk stratification remains challenging. Recent evidence suggests that B‐cell dysregulation contributes to disease severity. Methods A cohort of 168 patients with confirmed SFTS was retrospectively analyzed. Flow cytometry was used to quantify B‐cell subsets in peripheral blood at admission. Laboratory markers, viral load, and B‐cell phenotypes were evaluated for prognostic relevance using univariate and multivariate Cox regression analyses. Receiver operating characteristic (ROC) curves and nomogram models were employed to assess predictive value. Results Deceased patients exhibited significantly higher viral loads, elevated proinflammatory cytokines (interleukin‐6 (IL‐6), IL‐10, and tumor necrosis factor‐alpha (TNF‐α)), and markers of multiorgan dysfunction. Immunophenotyping revealed a reduction in naïve B‐cells (IgD + CD27 − ), alongside expansion of double negative B‐cells (DNBs) (IgD − CD27 − ) in fatal cases. Furthermore, viral load was positively correlated with inflammatory cytokines and dysfunctional B‐cell subsets, suggesting that impaired humoral immunity contributes to persistent hyperinflammation in severe SFTS. Multivariate Cox regression analysis identified higher viral load (HR = 2.193, p < 0.001), older age (HR = 1.073, p < 0.001), and increased proportion of DNBs (HR = 1.024, p = 0.035) as independent predictors of 28‐day mortality. A combined prognostic model integrating these variables achieved excellent performance (AUC = 0.906, 95% CI: 0.814–0.967), significantly surpassing individual markers and enabling early identification of high‐risk SFTS patients. Conclusion This study demonstrates that integrating B‐cell subset profiling with laboratory indicators significantly improves early prognostic assessment in SFTS. These findings provide insights into immune‐pathological mechanisms and support timely risk stratification and intervention to reduce mortality.