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Background <p>Severe fever with thrombocytopenia syndrome (SFTS) is a life-threatening infectious disease with high mortality. Although sarcopenia and myosteatosis are associated with adverse outcomes in multiple acute illnesses, their prognostic impact on SFTS remains unknown. This study aimed to evaluate the association between body composition parameters and 28-day mortality in SFTS patients, and to identify independent prognostic factors.</p> Methods <p>In this retrospective observational single-center study, 234 SFTS patients with complete clinical data and available computed tomography (CT) images were enrolled. Body composition indices, including skeletal muscle index (SMI), subcutaneous fat index (SFI), visceral fat index (VFI), and intermuscular fat index (IMFI), were measured at the 12th thoracic (T12) and 3rd lumbar (L3) vertebral levels using a validated artificial intelligence-driven CT segmentation model. Univariable logistic regression and least absolute shrinkage and selection operator (LASSO) regression were applied to screen candidate predictors of 28-day mortality, and multivariable logistic regression was used to identify independent risk factors and construct a prognostic model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Kaplan–Meier curves with the log-rank test were used to compare survival across different muscle phenotypes.</p> Results <p>The overall 28-day mortality rate was 13.7% (32/234). T12-IMFI was identified as an independent risk factor for 28-day mortality (adjusted odds ratio [aOR] = 1.30, 95% confidence interval [CI] 1.04–1.63, <i>P</i> = 0.021). A prediction model comprising age, log₁₀-transformed viral load, procalcitonin (PCT), and T12-IMFI was constructed, which exhibited satisfactory discrimination performance with an area under the ROC curve (AUC) of 0.877 (95% CI 0.805–0.950) and good calibration (Hosmer-Lemeshow <i>P</i> = 0.278). DCA demonstrated that the model provided superior net benefit across the clinically relevant threshold range of 0.05 to 0.68. The addition of T12-IMFI significantly improved individual risk stratification compared with the clinical-only model (net reclassification improvement [NRI] = 0.470, 95% CI 0.078– 0.862, <i>P</i> = 0.019). Patients with myosteatosis alone showed the highest 28-day mortality rate (26.1%), and significant differences in survival were observed among the four muscle phenotypes (log-rank <i>P</i> = 0.012).</p> Conclusions <p>CT-defined myosteatosis, rather than sarcopenia, is independently associated with 28-day mortality in SFTS patients. T12-IMFI is a valuable prognostic biomarker, and the model incorporating T12-IMFI provides a practical tool for early risk stratification in SFTS patients at admission.</p> Clinical trial number <p>Not applicable.</p>

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The effect of artificial intelligence-driven, computed tomography-based assessment of body composition parameters on 28-day mortality of patients with severe fever with thrombocytopenia syndrome: a retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月23日

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  • Fan Yang,
  • Shiyu Zhang,
  • Shuting Liu,
  • Jianfeng Zhang,
  • Jinrong Yang,
  • Dilihumaer Zhayier,
  • Ruihan Gao,
  • Fan Pu,
  • Minfeng Xu,
  • Bin Zhu,
  • Xin Zheng,
  • Baoju Wang

摘要

Background

Severe fever with thrombocytopenia syndrome (SFTS) is a life-threatening infectious disease with high mortality. Although sarcopenia and myosteatosis are associated with adverse outcomes in multiple acute illnesses, their prognostic impact on SFTS remains unknown. This study aimed to evaluate the association between body composition parameters and 28-day mortality in SFTS patients, and to identify independent prognostic factors.

Methods

In this retrospective observational single-center study, 234 SFTS patients with complete clinical data and available computed tomography (CT) images were enrolled. Body composition indices, including skeletal muscle index (SMI), subcutaneous fat index (SFI), visceral fat index (VFI), and intermuscular fat index (IMFI), were measured at the 12th thoracic (T12) and 3rd lumbar (L3) vertebral levels using a validated artificial intelligence-driven CT segmentation model. Univariable logistic regression and least absolute shrinkage and selection operator (LASSO) regression were applied to screen candidate predictors of 28-day mortality, and multivariable logistic regression was used to identify independent risk factors and construct a prognostic model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Kaplan–Meier curves with the log-rank test were used to compare survival across different muscle phenotypes.

Results

The overall 28-day mortality rate was 13.7% (32/234). T12-IMFI was identified as an independent risk factor for 28-day mortality (adjusted odds ratio [aOR] = 1.30, 95% confidence interval [CI] 1.04–1.63, P = 0.021). A prediction model comprising age, log₁₀-transformed viral load, procalcitonin (PCT), and T12-IMFI was constructed, which exhibited satisfactory discrimination performance with an area under the ROC curve (AUC) of 0.877 (95% CI 0.805–0.950) and good calibration (Hosmer-Lemeshow P = 0.278). DCA demonstrated that the model provided superior net benefit across the clinically relevant threshold range of 0.05 to 0.68. The addition of T12-IMFI significantly improved individual risk stratification compared with the clinical-only model (net reclassification improvement [NRI] = 0.470, 95% CI 0.078– 0.862, P = 0.019). Patients with myosteatosis alone showed the highest 28-day mortality rate (26.1%), and significant differences in survival were observed among the four muscle phenotypes (log-rank P = 0.012).

Conclusions

CT-defined myosteatosis, rather than sarcopenia, is independently associated with 28-day mortality in SFTS patients. T12-IMFI is a valuable prognostic biomarker, and the model incorporating T12-IMFI provides a practical tool for early risk stratification in SFTS patients at admission.

Clinical trial number

Not applicable.