Scholarlink

Objective <p>This study develops a predictive model to evaluate mortality risk in severe fever with thrombocytopenia syndrome (SFTS) patients in intensive care units (ICU) to improve the accuracy of prognosis and guide the optimization of treatment strategies.</p> Methods <p>In this study, a retrospective analysis was conducted on severe SFTS patients admitted to the ICU between July 2019 and October 2023. Patients were categorized into survival and mortality groups. Multivariate logistic regression was performed to determine independent risk factors (IRFs) for mortality. In addition, the nomogram model was constructed and its performance was assessed through ROC curves.</p> Results <p>The study comprised 218 severe SFTS patients. The mortality group showed significantly lower Glasgow Coma Scale (GCS) scores, oxygenation indices, and higher levels of several serological markers, log10(virus loads), and lactic acid. Multivariate analysis identified GCS score [odds ratio (OR) = 0.66, <i>P</i> &lt; 0.001], log10(virus loads) [OR = 2.24, <i>P</i> = 0.001], lactic acid [OR = 1.60, <i>P</i> = 0.01], and cystatin C [OR = 1.80, <i>P</i> = 0.049] as IRFs for mortality. A nomogram incorporating these IRFs demonstrated excellent predictive accuracy (AUC = 0.92, 95% CI: 0.88–0.96), with a sensitivity of 76% and a specificity of 91%. This model showed adequate fit and good clinical applicability.</p> Conclusion <p>The nomogram model, based on GCS score, log10(virus loads), lactic acid, and cystatin C, offers clinical utility in predicting 28-day mortality for severe SFTS patients, though further validation is needed.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Intensive care unit-based mortality risk model construction for severe fever with thrombocytopenia syndrome patients: a retrospective study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月1日

查看全文
  • Puhui Liu,
  • Fangyuan Liu,
  • Chunhui Wang,
  • Aimin Mu,
  • Chuanzhen Niu,
  • Shihong Zhu,
  • Ji Wang

摘要

Objective

This study develops a predictive model to evaluate mortality risk in severe fever with thrombocytopenia syndrome (SFTS) patients in intensive care units (ICU) to improve the accuracy of prognosis and guide the optimization of treatment strategies.

Methods

In this study, a retrospective analysis was conducted on severe SFTS patients admitted to the ICU between July 2019 and October 2023. Patients were categorized into survival and mortality groups. Multivariate logistic regression was performed to determine independent risk factors (IRFs) for mortality. In addition, the nomogram model was constructed and its performance was assessed through ROC curves.

Results

The study comprised 218 severe SFTS patients. The mortality group showed significantly lower Glasgow Coma Scale (GCS) scores, oxygenation indices, and higher levels of several serological markers, log10(virus loads), and lactic acid. Multivariate analysis identified GCS score [odds ratio (OR) = 0.66, P < 0.001], log10(virus loads) [OR = 2.24, P = 0.001], lactic acid [OR = 1.60, P = 0.01], and cystatin C [OR = 1.80, P = 0.049] as IRFs for mortality. A nomogram incorporating these IRFs demonstrated excellent predictive accuracy (AUC = 0.92, 95% CI: 0.88–0.96), with a sensitivity of 76% and a specificity of 91%. This model showed adequate fit and good clinical applicability.

Conclusion

The nomogram model, based on GCS score, log10(virus loads), lactic acid, and cystatin C, offers clinical utility in predicting 28-day mortality for severe SFTS patients, though further validation is needed.