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Objective <p>To identify risk factors associated with poor prognosis in patients with severe fever with thrombocytopenia syndrome (SFTS) and develop a prognostic model based on these factors.</p> Methods <p>A retrospective analysis was conducted on 207 patients with SFTS admitted to Tongji Hospital from April 1, 2023, to July 18, 2024. Patients were categorized into survival (<i>n</i> = 133) and death (<i>n</i> = 74) groups based on their prognosis. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of mortality, incorporating demographic characteristics and inflammatory biomarkers measured within 24&#xa0;h of hospital admission. A nomogram model was constructed using R software based on the regression coefficients of the identified predictors. The model’s discriminative ability was evaluated using the receiver operating characteristic (ROC) curve, with the area under the curve (AUC) and concordance index (C-index) calculated. Internal validation was performed using the Bootstrap resampling method (1,000 iterations).Furthermore, an external validation cohort comprising 55 patients with SFTS admitted to our hospital between August 2024 and February 2025 was retrospectively collected to evaluate the model’s generalizability and stability.</p> Results <p>Age, viral load, procalcitonin (PCT), and interleukin-10 (IL-10) were identified as independent risk factors for poor prognosis. A nomogram model incorporating these four factors demonstrated robust predictive performance, yielding an AUC of 0.905 (95% CI, 0.862–0.949; <i>P</i> &lt; 0.001).Internal and external validations confirmed the model’s stability and strong prognostic performance in patients with SFTS. Decision curve analysis (DCA) showed that the nomogram yielded a higher net benefit over a wider threshold probability range than previous models in predicting SFTS mortality.</p> Conclusion <p>This study provides a novel prognostic model for SFTS patients, which may aid in early risk stratification. However, its clinical utility and generalizability need to be further validated in larger cohorts.</p> Clinical trial <p>Not applicable.</p>

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Construction of a novel prognostic model for severe fever with thrombocytopenia syndrome patients based on inflammatory indicators

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Xu Xiang,
  • Song Li,
  • Yueqing Dai

摘要

Objective

To identify risk factors associated with poor prognosis in patients with severe fever with thrombocytopenia syndrome (SFTS) and develop a prognostic model based on these factors.

Methods

A retrospective analysis was conducted on 207 patients with SFTS admitted to Tongji Hospital from April 1, 2023, to July 18, 2024. Patients were categorized into survival (n = 133) and death (n = 74) groups based on their prognosis. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of mortality, incorporating demographic characteristics and inflammatory biomarkers measured within 24 h of hospital admission. A nomogram model was constructed using R software based on the regression coefficients of the identified predictors. The model’s discriminative ability was evaluated using the receiver operating characteristic (ROC) curve, with the area under the curve (AUC) and concordance index (C-index) calculated. Internal validation was performed using the Bootstrap resampling method (1,000 iterations).Furthermore, an external validation cohort comprising 55 patients with SFTS admitted to our hospital between August 2024 and February 2025 was retrospectively collected to evaluate the model’s generalizability and stability.

Results

Age, viral load, procalcitonin (PCT), and interleukin-10 (IL-10) were identified as independent risk factors for poor prognosis. A nomogram model incorporating these four factors demonstrated robust predictive performance, yielding an AUC of 0.905 (95% CI, 0.862–0.949; P < 0.001).Internal and external validations confirmed the model’s stability and strong prognostic performance in patients with SFTS. Decision curve analysis (DCA) showed that the nomogram yielded a higher net benefit over a wider threshold probability range than previous models in predicting SFTS mortality.

Conclusion

This study provides a novel prognostic model for SFTS patients, which may aid in early risk stratification. However, its clinical utility and generalizability need to be further validated in larger cohorts.

Clinical trial

Not applicable.